MIT6_036_hw08_colab_notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MIT6.036 hw08 colab notebook",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "v-sSs7N4mMiX",
        "Zh4u39OCjLza"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DanyunHe/MIT6.036/blob/master/MIT6_036_hw08_colab_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "_xIaEwCD406A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#MIT 6.036 Spring 2019: Homework 8#\n",
        "\n",
        "This colab notebook provides code and a framework for [homework 8](https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/).  You can work out your solutions here, then submit your results back on the homework page when ready.\n",
        "\n",
        "## <section>**Setup**</section>\n",
        "\n",
        "First, download the code distribution for this homework that contains test cases and helper functions.\n",
        "\n",
        "Run the next code block to download and import the code for this lab.\n"
      ]
    },
    {
      "metadata": {
        "id": "2YM-_zLf9Bp-",
        "colab_type": "code",
        "outputId": "8c80ae12-7b14-46d4-f822-9a5eb9b9a95d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "cell_type": "code",
      "source": [
        "!rm -rf code_for_hw8* __MACOSX data .DS_Store\n",
        "!wget --quiet https://introml.odl.mit.edu/cat-soop/_static/6.036/homework/hw08/code_for_hw8.zip\n",
        "!unzip code_for_hw8.zip\n",
        "!mv code_for_hw8/* .\n",
        "\n",
        "from code_for_hw8_oop import *\n",
        "import numpy as np\n",
        "import math as m"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  code_for_hw8.zip\n",
            "   creating: code_for_hw8/\n",
            "  inflating: code_for_hw8/.DS_Store  \n",
            "   creating: __MACOSX/\n",
            "   creating: __MACOSX/code_for_hw8/\n",
            "  inflating: __MACOSX/code_for_hw8/._.DS_Store  \n",
            "  inflating: code_for_hw8/code_for_hw8_oop.py  \n",
            "  inflating: __MACOSX/code_for_hw8/._code_for_hw8_oop.py  \n",
            "   creating: code_for_hw8/data/\n",
            "  inflating: code_for_hw8/data/data3_train.csv  \n",
            "   creating: __MACOSX/code_for_hw8/data/\n",
            "  inflating: __MACOSX/code_for_hw8/data/._data3_train.csv  \n",
            "  inflating: code_for_hw8/data/data4_train.csv  \n",
            "  inflating: code_for_hw8/data/data4_validate.csv  \n",
            "  inflating: code_for_hw8/data/data3_validate.csv  \n",
            "  inflating: code_for_hw8/data/dataXor_train.csv  \n",
            "  inflating: code_for_hw8/data/data2_train.csv  \n",
            "  inflating: code_for_hw8/data/data2_validate.csv  \n",
            "  inflating: code_for_hw8/data/data3class_train.csv  \n",
            "  inflating: code_for_hw8/data/data1_validate.csv  \n",
            "  inflating: code_for_hw8/data/data1_train.csv  \n",
            "  inflating: code_for_hw8/code_for_hw8_keras.py  \n",
            "  inflating: __MACOSX/code_for_hw8/._code_for_hw8_keras.py  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "q9yVU74AuUU-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 1) Batch Normalization\n",
        "No code involved,  <a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>"
      ]
    },
    {
      "metadata": {
        "id": "v-sSs7N4mMiX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 2) Implementing Mini-batch Gradient Descent and Batch Normalization (OPTIONAL)\n",
        "\n",
        "** Note: You can click the arrow on the left of this text block to collapse/expand this optional section and all its code blocks **\n",
        "\n",
        "Last week we implemented a framework for building neural networks from scratch. We trained our models using *stochastic* gradient descent. In this problem, we explore how we can implement batch normalization as a module `BatchNorm` in our framework. It is the same module which you analyzed in problem 1. "
      ]
    },
    {
      "metadata": {
        "id": "HgxmIfXVmVwd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Key to the concept of batch normalization is the doing gradient descent on batches of data. So we instead of using last week's stochastic gradient descent, we will first implement the *mini-batch* gradient descent method `mini_gd`, which is a hybrid between *stochastic* gradient descent and *batch* gradient descent. The lecture notes on <a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week7/neural_networks_2/1?activate_block_id=block-v1%3AMITx%2B6.036%2B2019_Spring%2Btype%40vertical%2Bblock%40neural_networks_2_optimizing_neural_network_parameters_vert\"> optimizing neural network parameters</a> are helpful for this part.\n",
        "\n",
        "In *mini-batch* gradient descent, for a mini-batch of size $K$, we select $K$ distinct data points uniformly at random from the data set and update the network weights based only on their contributions to the gradient:\n",
        "$$W := W - \\eta\\sum_{i=1}^K \\nabla_W \\mathcal{L}(h(x^{(i)}; W), y^{(i)})\\;\\;.$$\n",
        "\n",
        "Our *mini-batch* method `mini_gd` will be implemented within the `Sequential` python class (see homework 7 problem 2) and will take the following as inputs:\n",
        "\n",
        "* `X`: a standard data array (d by n)\n",
        "* `y`: a standard labels row vector (1 by n)\n",
        "* `iters`: the number of updates to perform on weights $W$\n",
        "* `lrate`: the learning rate used\n",
        "* `K`: the mini-batch size to be used\n",
        "\n",
        "One call of `mini_gd` should call `Sequential.backward` for back-propagation and `Sequential.step` for updating the weights, for a total of `iters` times, using `lrate` as the learning rate. As in our implementation of `sgd` from homework 7, we compute the predicted output for a mini-batch of data with the `Sequential.forward` method. We compute the loss between our predictions and the true labels using the assigned `Sequential.loss` method. (Note that in homework 7, `Sequential.step` was called `Sequential.sgd_step`. While the functionality of the step function is the same, it has been renamed for convenience. The same is true for the `module.step` function of each module we implemented, where applicable.)\n",
        "\n",
        "For picking $K$ unique data points at random from our large data-set for each mini-batch, we will implement the following strategy: we will first shuffle our data points `X` (and associated labels `y`). Then, we get <math>\\frac{n}{k}</math> (rounded down to the nearest integer) different mini-batches by grouping each $K$ consecutive points from this shuffled array. If we end up iterating over all the points but need more mini-batches, we will repeat the shuffling and the batching process."
      ]
    },
    {
      "metadata": {
        "id": "Dr1kWI08mdo4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>2A)</b>You need to fill in the missing code below. We have implemented the shuffling of indices and have provided you with the outer and inner loops."
      ]
    },
    {
      "metadata": {
        "id": "h_lvmO9Z22bH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** This OPTIONAL problem has you extend your homework 7 implementation for building neural networks. **\n",
        "### PLEASE COPY IN YOUR CODE FROM HOMEWORK 7 TO COMPLEMENT THE CLASSES GIVEN HERE\n",
        "\n",
        "Recall that your implementation from homework 7 included the following classes:\n",
        "    \n",
        "  * Module\n",
        "  * Linear \n",
        "  * Tanh \n",
        "  * ReLU \n",
        "  * SoftMax\n",
        "  * NLL\n",
        "  * Sequential"
      ]
    },
    {
      "metadata": {
        "id": "ovtbqrrSmezE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import math as m \n",
        "\n",
        "class Sequential:\n",
        "    def __init__(self, modules, loss):            \n",
        "        self.modules = modules\n",
        "        self.loss = loss\n",
        "\n",
        "    def mini_gd(self, X, Y, iters, lrate, notif_each=None, K=10):\n",
        "        D, N = X.shape\n",
        "\n",
        "        np.random.seed(0)\n",
        "        num_updates = 0\n",
        "        indices = np.arange(N)\n",
        "        while num_updates < iters:\n",
        "\n",
        "            np.random.shuffle(indices)\n",
        "            X = None  # Your code\n",
        "            Y = None  # Your code\n",
        "\n",
        "            for j in range(m.floor(N/K)):\n",
        "                if num_updates >= iters: break\n",
        "\n",
        "                # Implement the main part of mini_gd here\n",
        "                # Your code\n",
        "                \n",
        "                num_updates += 1\n",
        "\n",
        "    def forward(self, Xt):                        \n",
        "        for m in self.modules: Xt = m.forward(Xt)\n",
        "        return Xt\n",
        "\n",
        "    def backward(self, delta):                   \n",
        "        for m in self.modules[::-1]: delta = m.backward(delta)\n",
        "\n",
        "    def step(self, lrate):    \n",
        "        for m in self.modules: m.step(lrate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1JZeeKXkm6YI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>2B)</b> We are now ready to implement batch normalization into our neural network framework! Our module `BatchNorm` will sit between consecutive layers of neurons, such as the $l^{th}$ and $(l+1)^{th}$ layers, acting as a \"corrector\" which allows $W^l$ to change freely, producing outputs $z^l$, but then the module corrects the covariate shift induced in the signals before they reach the $(l+1)^{th}$ layer, converting $z^l$ to $\\widehat{Z}^l$. \n",
        "\n",
        "The following is a summmary what is described in the <a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week7/neural_networks_2/2\">lecture notes</a>, and it should guide your implementation of the module. \n",
        "\n",
        "Any normalization between the $l^{th}$ and $(l+1)^{th}$ layers is done *separately* for each of the $n^l$ input connections leading to the $(l+1)^{th}$ layer. We handle a mini-batch of data of size $K$, and $Z^l$ is $n^l \\times K$, and the output $\\widehat{Z}^l$is of the same shape. \n",
        "\n",
        "We first compute $n^l$ *batchwise* means and\n",
        "standard deviations.  Let $\\mu^l$ be the $n^l \\times 1$ vector (`self.mus`) where\n",
        "$$\\mu^l_i = \\frac{1}{K} \\sum_{j = 1}^K Z^l_{ij}\\;\\;,$$\n",
        "and let $\\sigma^l$ be the $n^l \\times 1$ vector (`self.vars`) where \n",
        "$$\\sigma^l_i = \\sqrt{\\frac{1}{K} \\sum_{j = 1}^K (Z^l_{ij} - \\mu_i)^2}\\;\\;.$$\n",
        "\n",
        "The normalized data `self.norm` is the matrix $\\overline{Z}$, where\n",
        "$$\\overline{Z}^l_{ij} = \\frac{Z^l_{ij} - \\mu^l_i}{\\sigma^l_i + \\epsilon}\\;\\;,$$\n",
        "and where $\\epsilon$ is a very small constant to guard against division by\n",
        "zero. \n",
        "\n",
        "We define weights $G^l$ (`self.G`) and $B^l$ (`self.B`), each being an $n^l \\times 1$ vector, which we use to to shift and scale the outputs:\n",
        "$$\\widehat{Z}^l_{ij} = G^l_i \\overline{Z}^l_{ij} + B^l_i\\;\\;.$$\n",
        "\n",
        "The outputs are finally ready to be passed to the $(l+1)^{th}$ layer.\n",
        "\n",
        "A slight warning (that we will not worry about here) about `BatchNorm` is that during the *test* phase, if the test mini-batch size is too small (imagine we are deploying a neural network that deals with live video frames), then the lack of samples would cause the freshly-calculated $\\mu^l$ and $\\sigma^l$ to be far off from their true values that the module's parameters $G^l$ and $B^l$ were trained to be compatible with. To fix that, people usually compute a running average of $\\mu^l$ and $\\sigma^l$ during training, to be used at test time. We will assume our test mini-batches are large enough.\n",
        "\n",
        "In this problem we only implement the `BatchNorm.forward` and `BatchNorm.step` methods. We provide you with the implementation for `BatchNorm.backward` and the lecture notes contain the details of the derivations. You will need to fill in the missing code below."
      ]
    },
    {
      "metadata": {
        "id": "UlXP26plm8R7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class BatchNorm(Module):    \n",
        "    def __init__(self, m):\n",
        "        np.random.seed(0)\n",
        "        self.eps = 1e-20\n",
        "        self.m = m  # number of input channels\n",
        "        \n",
        "        # Init learned shifts and scaling factors\n",
        "        self.B = np.zeros([self.m, 1])\n",
        "        self.G = np.random.normal(0, 1.0 * self.m ** (-.5), [self.m, 1])\n",
        "        \n",
        "    # Works on m x b matrices of m input channels and b different inputs\n",
        "    def forward(self, A):# A is m x K: m input channels and mini-batch size K\n",
        "        # Store last inputs and K for next backward() call\n",
        "        self.A = A\n",
        "        self.K = A.shape[1]\n",
        "        \n",
        "        self.mus = None  # Your Code\n",
        "        self.vars = None  # Your Code\n",
        "\n",
        "        # Normalize inputs using their mean and standard deviation\n",
        "        self.norm = None  # Your Code\n",
        "            \n",
        "        # Return scaled and shifted versions of self.norm\n",
        "        return None  # Your Code\n",
        "\n",
        "    def backward(self, dLdZ):\n",
        "        # Re-usable constants\n",
        "        std_inv = 1/np.sqrt(self.vars+self.eps)\n",
        "        A_min_mu = self.A-self.mus\n",
        "        \n",
        "        dLdnorm = dLdZ * self.G\n",
        "        dLdVar = np.sum(dLdnorm * A_min_mu * -0.5 * std_inv**3, axis=1, keepdims=True)\n",
        "        dLdMu = np.sum(dLdnorm*(-std_inv), axis=1, keepdims=True) + dLdVar * (-2/self.K) * np.sum(A_min_mu, axis=1, keepdims=True)\n",
        "        dLdX = (dLdnorm * std_inv) + (dLdVar * (2/self.K) * A_min_mu) + (dLdMu/self.K)\n",
        "        \n",
        "        self.dLdB = np.sum(dLdZ, axis=1, keepdims=True)\n",
        "        self.dLdG = np.sum(dLdZ * self.norm, axis=1, keepdims=True)\n",
        "        return dLdX\n",
        "\n",
        "    def step(self, lrate):\n",
        "        self.B = None  # Your Code\n",
        "        self.G = None  # Your Code\n",
        "        return"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "65LKUAHD_77Y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 3) 2D Datasets\n",
        "\n",
        "For the 2D datasets, we have provided the following function:\n",
        "\n",
        "\n",
        "```\n",
        "run_keras_2d(data_name, layers, epochs, split=0.25, display=True, trials=5)\n",
        "```\n",
        "\n",
        "\n",
        "where:\n",
        "\n",
        "data_name is a string, such as '1', '2', etc.\n",
        "layers is a list of Keras layer definitions for a Sequential model, e.g. \n",
        "```\n",
        "[Dense(input_dim=2, units=10, activation='relu'), Dense(units=2, activation='softmax')]\n",
        "```\n",
        "\n",
        "epochs is an integer indicating how many times to go through the data in training\n",
        "split is a fraction of the training data to use for validation if a validation set is not defined\n",
        "display whether to display result plots\n",
        "verbose whether to print loss and accuracy (percent correctly labeled) each epoch\n",
        "trials is an integer indicating how many times to perform the training and testing\n",
        "2D Data\n",
        "The two-class datasets have data_names: '1','2','3','4'. Target accuracies (percent correct) on the validation set are (99%, 90.5%, 96%, 94%).\n",
        "\n",
        "In this problem, try the following 5 architectures, specified by the number of units in the hidden layers:\n",
        "\n",
        "1: (0), 2: (10), 3: (100), 4: (10, 10), 5: (100, 100))\n",
        "You may find the archs function in the code file to be helpful here.\n",
        "Some of these questions ask for the \"simplest\" architecture; the list above is ordered starting with the simplest."
      ]
    },
    {
      "metadata": {
        "id": "Vp-RWPD7FUeo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "506f6c74-510d-4556-a351-b23858e104c0"
      },
      "cell_type": "code",
      "source": [
        "from code_for_hw8_keras import *\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.optimizers import SGD, Adam\n",
        "from keras.layers import Conv1D, Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "from keras.callbacks import Callback\n",
        "from keras.datasets import mnist\n",
        "from keras import backend as K\n",
        "from keras.initializers import VarianceScaling\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "_YWbWX47_9Tr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7531
        },
        "outputId": "44f766a4-ab03-4ca0-b579-1cba5d9305e8"
      },
      "cell_type": "code",
      "source": [
        "#example of run_keras_2d\n",
        "run_keras_2d(\"2\", archs(2)[3], 10, split=0.25, display=False, verbose=False, trials=5)\n",
        "\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 2\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (200, 2) y (200,) classes {0.0, 1.0}\n",
            "\n",
            "Loss on validation set:0.2637147865210329 Accuracy on validation set: 0.895\n",
            "\n",
            "Loss on validation set:0.2847880501925997 Accuracy on validation set: 0.885\n",
            "\n",
            "Loss on validation set:0.26968979200359655 Accuracy on validation set: 0.885\n",
            "\n",
            "Loss on validation set:0.3039260469599271 Accuracy on validation set: 0.885\n",
            "\n",
            "Loss on validation set:0.27983292571080975 Accuracy on validation set: 0.89\n",
            "\n",
            "Avg. validation accuracy:0.8879999999999999\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[-2.01380386e-01,  3.89440035e-02],\n",
              "        [ 1.29983966e+00,  6.79588882e-01],\n",
              "        [ 6.71250029e-01,  1.28652238e+00],\n",
              "        [ 2.10673747e+00, -9.08443385e-01],\n",
              "        [-4.42767576e-01, -7.70831748e-01],\n",
              "        [-6.49323337e-01, -9.85603310e-01],\n",
              "        [-5.05832862e-02,  1.57840839e+00],\n",
              "        [ 5.26712401e-01, -1.23759149e+00],\n",
              "        [ 1.08681935e+00,  9.30857621e-01],\n",
              "        [ 1.56463949e+00, -9.08035870e-01],\n",
              "        [ 3.02060944e+00, -1.20300597e+00],\n",
              "        [ 1.29690447e+00,  6.12584986e-01],\n",
              "        [-2.41708091e+00, -2.15882089e+00],\n",
              "        [ 1.06082281e+00, -1.11885549e+00],\n",
              "        [-1.11318095e+00,  1.27803332e-01],\n",
              "        [ 2.19311623e+00,  1.04578246e-01],\n",
              "        [-9.41722312e-01, -6.40421937e-02],\n",
              "        [-6.48179440e-01,  1.47403690e+00],\n",
              "        [ 1.18346748e+00,  8.40906891e-01],\n",
              "        [ 6.90088182e-01,  3.46665370e+00],\n",
              "        [ 1.02227645e+00, -9.88603122e-01],\n",
              "        [-2.88914171e+00, -2.85959001e+00],\n",
              "        [-1.26191468e+00,  2.51863932e+00],\n",
              "        [-1.59501645e+00,  2.87988451e-01],\n",
              "        [-1.05326962e+00,  1.15185857e+00],\n",
              "        [ 1.13835016e+00, -1.15406322e+00],\n",
              "        [-1.52803357e+00,  1.06207542e-01],\n",
              "        [-5.92740329e-01, -6.62168505e-01],\n",
              "        [ 8.62984094e-01, -9.72644603e-01],\n",
              "        [-9.95548619e-01, -8.46667114e-01],\n",
              "        [ 1.78788530e+00, -8.24769511e-01],\n",
              "        [-6.29472025e-01, -8.38701239e-01],\n",
              "        [-4.10163532e-01,  6.46080198e-01],\n",
              "        [-7.04391246e-01, -6.03308713e-01],\n",
              "        [ 1.00612700e+00, -9.40350985e-01],\n",
              "        [-8.61137678e-01,  7.81567059e-01],\n",
              "        [ 1.85827112e+00,  3.82066484e-01],\n",
              "        [ 2.94528449e-01,  2.27694472e-01],\n",
              "        [ 3.57445020e-01, -1.39391903e+00],\n",
              "        [-3.49421923e+00, -3.46012699e+00],\n",
              "        [-5.49375511e-01,  2.17692846e+00],\n",
              "        [-1.35201342e+00,  5.86232664e-01],\n",
              "        [-1.19181144e+00,  2.59930958e+00],\n",
              "        [ 1.17777437e+00,  1.01847217e+00],\n",
              "        [-1.36901720e+00, -1.46302366e+00],\n",
              "        [ 2.89856474e+00, -4.27914418e-01],\n",
              "        [ 5.23058521e-01,  2.14536426e+00],\n",
              "        [-1.89990186e+00, -1.99978217e+00],\n",
              "        [-3.53837461e-01,  2.12256043e+00],\n",
              "        [-9.00968364e-01,  9.42808569e-01],\n",
              "        [ 1.09082642e+00, -1.34696817e+00],\n",
              "        [-2.57583372e+00, -7.90985772e-01],\n",
              "        [ 1.17890861e+00, -1.20233299e+00],\n",
              "        [-1.12861044e+00,  7.56588440e-01],\n",
              "        [ 1.98640750e+00, -1.01913834e+00],\n",
              "        [-1.10703946e+00, -1.06669431e+00],\n",
              "        [ 2.32126285e-01,  3.42873063e+00],\n",
              "        [ 1.20175672e+00, -9.30735169e-01],\n",
              "        [-2.60168743e+00, -2.56066661e+00],\n",
              "        [ 2.93319964e-01,  1.62823666e+00],\n",
              "        [-1.18512673e+00,  7.00500961e-01],\n",
              "        [-2.35833682e+00, -3.01459268e-01],\n",
              "        [-7.29678795e-01,  1.23888643e+00],\n",
              "        [-4.93366855e-01,  1.34172764e+00],\n",
              "        [-1.46460089e+00, -1.50083364e+00],\n",
              "        [-4.06029615e-01, -5.24708973e-01],\n",
              "        [-5.17793384e-01, -5.98408386e-01],\n",
              "        [-6.36141556e-01,  1.78870373e-01],\n",
              "        [-2.38213776e+00, -4.27446912e-01],\n",
              "        [ 1.28383683e+00,  9.42786437e-01],\n",
              "        [ 4.31793087e-01, -1.18985592e+00],\n",
              "        [ 8.19290972e-01,  1.15573589e+00],\n",
              "        [ 3.89868104e-02,  1.74244888e+00],\n",
              "        [-5.72601871e-01,  2.11756498e+00],\n",
              "        [ 6.84756854e-01,  1.21344045e+00],\n",
              "        [-7.94594405e-01,  1.86386232e+00],\n",
              "        [ 5.18914019e-01,  1.24502639e+00],\n",
              "        [-1.65677387e+00,  2.93458563e+00],\n",
              "        [-2.13973685e-01, -1.20527645e-01],\n",
              "        [ 3.79872711e-01,  5.58439066e-01],\n",
              "        [ 1.03370608e+00, -1.17180061e+00],\n",
              "        [ 1.18625907e+00, -9.52392458e-01],\n",
              "        [-1.32852528e+00,  8.72765446e-01],\n",
              "        [-4.82403452e-02, -1.36956309e+00],\n",
              "        [ 6.19896012e-01,  1.35393107e+00],\n",
              "        [-1.26950299e+00, -8.17906295e-01],\n",
              "        [-1.46867783e+00,  2.11090221e-01],\n",
              "        [ 1.01633165e+00,  1.37710735e+00],\n",
              "        [-7.39356638e-01,  1.44056221e+00],\n",
              "        [-2.58470717e-01, -1.10330149e+00],\n",
              "        [-7.28370853e-01, -1.18234340e+00],\n",
              "        [-1.18198912e+00, -1.20678058e+00],\n",
              "        [-8.78887779e-01,  2.32969272e-01],\n",
              "        [ 2.79719766e-01,  1.34451459e+00],\n",
              "        [-1.37762951e-01,  2.09710759e+00],\n",
              "        [ 3.19120517e+00, -4.97730362e-01],\n",
              "        [-3.05976739e+00, -2.77813530e+00],\n",
              "        [ 1.98855244e+00,  5.71003530e-01],\n",
              "        [ 2.76443697e+00, -3.35805408e-01],\n",
              "        [-8.24096888e-02,  1.83851715e+00],\n",
              "        [ 1.94384782e-01, -9.86623532e-01],\n",
              "        [ 2.89792941e+00, -1.35873607e+00],\n",
              "        [-7.00003279e-01,  1.34423323e+00],\n",
              "        [ 5.89576749e-01,  1.23463526e+00],\n",
              "        [-6.02091141e-01, -6.87159722e-01],\n",
              "        [-1.15121409e+00, -3.41179981e-02],\n",
              "        [-3.15691448e-01,  1.90027984e+00],\n",
              "        [-6.71602824e-01, -7.68705151e-01],\n",
              "        [ 1.96807656e+00, -9.11262146e-01],\n",
              "        [ 1.35476195e-01,  1.64980982e+00],\n",
              "        [ 3.08360544e-01, -1.16684146e+00],\n",
              "        [-6.65578011e-01, -1.03588191e+00],\n",
              "        [ 3.34484900e+00, -1.33652016e+00],\n",
              "        [ 8.23612562e-02,  2.41533234e-01],\n",
              "        [-6.46735016e-01,  6.06647047e-01],\n",
              "        [ 6.42435955e-01,  5.52341175e-01],\n",
              "        [ 1.18151883e+00, -1.10309630e+00],\n",
              "        [-8.21742059e-01,  1.22294898e+00],\n",
              "        [ 2.97355021e+00, -5.76570301e-01],\n",
              "        [-1.46654240e+00, -9.51878526e-01],\n",
              "        [-9.82064734e-01, -2.67181538e-02],\n",
              "        [-1.41195475e+00,  1.28715574e+00],\n",
              "        [-9.47126381e-01,  1.85008281e+00],\n",
              "        [-1.90665505e+00, -1.15958454e+00],\n",
              "        [-1.60516718e+00, -1.30209115e-01],\n",
              "        [-1.67749204e+00,  2.77485781e-02],\n",
              "        [-9.37735593e-01, -8.55627235e-01],\n",
              "        [-9.36416355e-02,  1.99327266e+00],\n",
              "        [ 3.05865114e+00, -8.82678285e-01],\n",
              "        [ 1.81755983e+00,  4.29775512e-01],\n",
              "        [ 1.27621763e+00, -1.04805327e+00],\n",
              "        [-3.75347204e-01,  2.02717929e+00],\n",
              "        [ 4.19436090e-01,  2.73193792e-01],\n",
              "        [-5.89423801e-01,  2.26165633e+00],\n",
              "        [-9.18193374e-01,  4.93238213e-01],\n",
              "        [ 2.13573174e+00, -1.17763617e+00],\n",
              "        [ 1.94482126e+00, -1.09518442e+00],\n",
              "        [-2.43317123e+00, -2.51628576e+00],\n",
              "        [ 1.57697244e+00,  5.74478871e-01],\n",
              "        [ 2.43020283e+00, -8.97288931e-01],\n",
              "        [-1.35954469e+00,  4.12253503e-01],\n",
              "        [-4.25911024e-01, -5.83676425e-01],\n",
              "        [ 1.27035750e+00, -7.89908472e-01],\n",
              "        [ 3.21315400e-02, -6.04895313e-01],\n",
              "        [ 1.03561105e+00,  1.05972780e+00],\n",
              "        [-1.95841437e-01, -2.67822051e-01],\n",
              "        [ 2.69344042e+00, -2.50460842e-01],\n",
              "        [-1.56802547e+00,  5.53409373e-01],\n",
              "        [ 2.22929994e+00,  6.92925451e-03],\n",
              "        [-1.87850595e+00,  5.31465958e-01],\n",
              "        [-3.45539171e-01,  2.83099351e-01],\n",
              "        [ 3.18447848e+00, -1.37575144e+00],\n",
              "        [ 1.57779603e+00,  5.93931515e-01],\n",
              "        [ 7.43743410e-01, -1.11485766e+00],\n",
              "        [-1.18649873e+00, -1.18973055e+00],\n",
              "        [-1.00426430e+00, -8.12181745e-01],\n",
              "        [ 1.07810322e+00, -7.35877513e-01],\n",
              "        [ 3.23567685e+00, -6.76676337e-01],\n",
              "        [-9.21277900e-01,  2.30607084e+00],\n",
              "        [-2.26550369e+00, -1.41720255e+00],\n",
              "        [-6.51919047e-01, -7.54819636e-01],\n",
              "        [-2.52164834e+00, -2.47998040e+00],\n",
              "        [-5.03929692e-01,  2.19314649e+00],\n",
              "        [-2.24065463e-01,  2.17059256e+00],\n",
              "        [ 2.61965174e-01,  1.64526150e+00],\n",
              "        [-6.09349593e-01,  2.77101678e+00],\n",
              "        [-2.60372971e+00, -1.33239916e+00],\n",
              "        [-3.07197403e+00, -3.09217414e+00],\n",
              "        [-5.80205564e-01,  1.90971032e+00],\n",
              "        [-7.07188277e-01,  1.90105335e+00],\n",
              "        [-4.19772033e-01, -1.61455877e-01],\n",
              "        [ 2.07866520e-01, -2.75360165e-01],\n",
              "        [-1.65987525e+00, -1.73574922e+00],\n",
              "        [-1.81229220e+00, -4.08248228e-01],\n",
              "        [-2.33903406e-01, -9.22549325e-01],\n",
              "        [-2.50101622e+00, -2.37689187e+00],\n",
              "        [-5.14962371e-01, -7.77415165e-01],\n",
              "        [ 8.74059390e-01, -1.19176435e+00],\n",
              "        [-1.00429615e+00,  9.53989013e-01],\n",
              "        [-1.03428819e+00,  4.34307627e-01],\n",
              "        [ 1.42432842e+00,  6.12940999e-01],\n",
              "        [ 1.07430328e+00, -1.10622246e+00],\n",
              "        [ 1.76262011e+00,  4.87321452e-01],\n",
              "        [ 2.30127946e+00, -1.10082751e+00],\n",
              "        [ 3.10719492e-01,  1.30610237e+00],\n",
              "        [-9.01038899e-01, -1.58127225e-01],\n",
              "        [ 1.28582961e+00, -1.09647456e+00],\n",
              "        [ 8.19755298e-01,  4.60879228e-01],\n",
              "        [-1.48968586e+00, -1.34403643e+00],\n",
              "        [-3.22573916e-03, -1.20271331e+00],\n",
              "        [-1.46853464e+00,  1.57104828e+00],\n",
              "        [ 7.24359432e-01, -1.21214224e+00],\n",
              "        [-1.28607425e+00, -1.35485789e+00],\n",
              "        [-1.87761734e+00, -4.85096491e-02],\n",
              "        [-6.90958835e-01,  2.38342967e+00],\n",
              "        [ 2.93140733e-01,  2.09981566e-01],\n",
              "        [-1.48103028e+00, -1.55116116e+00],\n",
              "        [ 2.39710997e+00,  8.71732474e-02],\n",
              "        [-3.88712941e-01,  1.77506085e+00],\n",
              "        [ 3.98998700e-01, -8.24673303e-01],\n",
              "        [-2.75614208e-01,  2.57765334e+00],\n",
              "        [ 3.73268868e-01, -9.99680103e-01],\n",
              "        [ 8.76541883e-01,  8.48365509e-01],\n",
              "        [-5.05427102e-01,  2.25922912e+00],\n",
              "        [-2.46493986e+00, -2.41956036e+00],\n",
              "        [-7.93556771e-01, -7.88516236e-01],\n",
              "        [-1.13580376e+00, -1.20385579e+00],\n",
              "        [ 6.17684409e-01,  1.29507295e+00],\n",
              "        [ 8.47792201e-01,  9.77964687e-01],\n",
              "        [-1.44029872e+00,  8.04109469e-01],\n",
              "        [-1.69789718e+00,  6.41344934e-01],\n",
              "        [ 2.18903178e+00, -1.08510531e+00],\n",
              "        [-1.45325783e+00, -1.97605623e+00],\n",
              "        [-5.97746959e-01, -7.40813089e-01],\n",
              "        [ 9.24357799e-01,  8.82615400e-01],\n",
              "        [-1.23195864e-01,  1.75266999e+00],\n",
              "        [-2.09917294e+00,  1.84756054e-01],\n",
              "        [-1.90008080e+00, -1.80945582e+00],\n",
              "        [-1.08957216e-01,  2.42615396e-03],\n",
              "        [-1.35537659e+00, -6.66133824e-02],\n",
              "        [-3.38675241e-01,  2.18599111e+00],\n",
              "        [ 2.68942514e+00, -7.18395270e-01],\n",
              "        [-2.35479504e-02,  1.69099223e+00],\n",
              "        [ 1.51241210e-01, -8.95427218e-01],\n",
              "        [ 3.76113477e+00, -1.10954126e+00],\n",
              "        [-6.56994421e-01,  9.79162187e-01],\n",
              "        [-7.95287193e-01, -9.36767686e-01],\n",
              "        [ 8.86340826e-01, -8.20409764e-01],\n",
              "        [ 2.13283112e+00,  1.57732915e-01],\n",
              "        [ 8.90807683e-01, -1.10688054e+00],\n",
              "        [-8.41387781e-01, -6.55598384e-01],\n",
              "        [ 1.42643563e+00, -8.48521458e-01],\n",
              "        [-1.10228747e+00, -1.09571841e+00],\n",
              "        [-1.31367184e+00,  1.87047926e+00],\n",
              "        [-2.76591496e+00, -2.90422443e+00],\n",
              "        [-1.56702723e+00, -2.57259783e-01],\n",
              "        [ 2.70059942e-01, -1.21916712e+00],\n",
              "        [ 4.53498957e-01, -9.35699796e-01],\n",
              "        [ 1.02025116e+00,  9.69768986e-01],\n",
              "        [ 1.68331248e+00, -1.08368613e+00],\n",
              "        [-1.77585096e+00, -3.67076701e-01],\n",
              "        [-1.44334058e-01,  9.41108388e-01],\n",
              "        [ 8.19718455e-01, -8.71823660e-01],\n",
              "        [ 8.68697101e-01,  9.47653547e-01],\n",
              "        [-1.09381583e+00,  2.53389307e+00],\n",
              "        [-3.86104675e-01,  1.26658926e+00],\n",
              "        [ 1.41173550e+00, -8.62062765e-01],\n",
              "        [ 2.36331478e-01,  1.69805471e+00],\n",
              "        [ 5.50561778e-01,  2.26509563e+00],\n",
              "        [ 2.59916910e+00,  2.33886611e+00],\n",
              "        [-1.71643943e+00,  5.75962337e-01],\n",
              "        [ 1.55221528e+00, -1.01146236e+00],\n",
              "        [-8.19505075e-01,  3.12796208e+00],\n",
              "        [-1.92869785e+00, -1.78083364e+00],\n",
              "        [-7.18229216e-01, -7.69472198e-01],\n",
              "        [-1.34403040e+00,  8.18472503e-01],\n",
              "        [ 2.68940073e-01,  2.54195359e+00],\n",
              "        [ 2.10579726e+00, -5.83057511e-01],\n",
              "        [-1.81904649e-01, -7.72614030e-01],\n",
              "        [-1.85258485e+00,  3.09931699e+00],\n",
              "        [-4.22934818e-01,  1.95592950e+00],\n",
              "        [-1.39075533e+00, -3.64814143e-01],\n",
              "        [ 3.36116762e-01,  2.13401670e-01],\n",
              "        [ 1.64799855e-01,  1.43407080e+00],\n",
              "        [-1.74081340e+00,  1.60936263e-01],\n",
              "        [ 1.38143360e+00, -8.37434812e-01],\n",
              "        [-1.42668198e+00,  5.16627514e-01],\n",
              "        [ 1.66486416e+00, -1.14545031e+00],\n",
              "        [-1.97942274e+00, -1.87038098e+00],\n",
              "        [ 2.53917995e+00, -1.23445992e-01],\n",
              "        [-1.30422514e+00, -1.73323671e-01],\n",
              "        [ 1.66147159e+00,  5.40435722e-01],\n",
              "        [-1.31652535e+00,  1.13014664e+00],\n",
              "        [-7.33049608e-01,  2.38239045e+00],\n",
              "        [-2.85826391e-01,  1.83816154e+00],\n",
              "        [ 2.46117425e-01,  1.60895523e-01],\n",
              "        [-3.76479700e-01,  1.45439639e+00],\n",
              "        [ 5.35555417e-01, -1.26659885e+00],\n",
              "        [-1.21230484e+00, -1.00046553e+00],\n",
              "        [-8.86208956e-02, -1.11372460e+00],\n",
              "        [-1.73439407e+00,  5.68351852e-01],\n",
              "        [-1.37537243e+00, -1.16004881e-01],\n",
              "        [ 2.68221896e+00, -6.58780220e-01],\n",
              "        [-1.24686631e+00, -8.37097756e-01],\n",
              "        [ 1.55322202e+00, -1.15509002e+00],\n",
              "        [-6.29939337e-01, -6.97129224e-01],\n",
              "        [-7.20624165e-01,  1.87336314e+00],\n",
              "        [-1.08028402e+00,  1.04648140e+00],\n",
              "        [ 4.68547777e-01,  3.25495022e-01],\n",
              "        [-1.07468028e+00,  1.29721447e+00],\n",
              "        [-1.36880785e+00, -1.49879685e+00],\n",
              "        [-9.87987581e-01, -6.47696279e-01],\n",
              "        [ 3.27843287e+00, -1.17543098e+00],\n",
              "        [ 1.75026056e+00,  4.92125173e-01],\n",
              "        [-1.17963559e+00, -1.15355099e+00],\n",
              "        [-4.69034360e-01, -7.51710454e-01],\n",
              "        [-2.06570460e+00, -1.44385339e+00],\n",
              "        [ 1.45166294e+00, -9.35462323e-01],\n",
              "        [ 5.97812083e-01, -1.15361297e+00],\n",
              "        [-5.10640051e-01, -1.07910459e+00],\n",
              "        [ 1.04884381e+00,  8.74936808e-01],\n",
              "        [-8.37372488e-01,  2.43210108e+00],\n",
              "        [ 2.58207855e+00, -2.98252361e-01],\n",
              "        [ 3.50949222e-01,  1.49877022e+00],\n",
              "        [-2.22585412e+00, -4.07447840e-01],\n",
              "        [ 3.39392196e+00, -6.68161919e-01],\n",
              "        [-2.16376388e+00, -2.34818460e+00],\n",
              "        [ 2.27983193e-01, -7.77485339e-01],\n",
              "        [ 6.63736839e-01, -6.85814959e-01],\n",
              "        [ 8.70850526e-01, -1.15025051e+00],\n",
              "        [-7.58281099e-01, -6.23951011e-01],\n",
              "        [ 2.65477985e-01, -8.30744385e-01],\n",
              "        [-4.98829964e-01, -3.93809547e-01],\n",
              "        [ 6.26902264e-01,  5.37022914e-01],\n",
              "        [-1.62337680e+00, -5.24442976e-01],\n",
              "        [-1.16939583e-01, -1.00651720e+00],\n",
              "        [-6.16310244e-01,  2.61525230e+00],\n",
              "        [-1.23137241e+00,  1.36368207e+00],\n",
              "        [ 1.66798937e+00, -9.63566174e-01],\n",
              "        [-7.51873287e-01, -9.06438672e-01],\n",
              "        [ 6.45990947e-01,  4.89172146e-01],\n",
              "        [-1.24883647e-01, -1.41134448e-01],\n",
              "        [ 6.38815181e-01, -1.07125863e+00],\n",
              "        [-1.66640200e+00,  9.67777299e-01],\n",
              "        [-1.28442381e+00,  4.07752035e-01],\n",
              "        [-6.03551194e-01,  1.82856381e+00],\n",
              "        [ 1.05994811e+00,  1.03747218e+00],\n",
              "        [ 1.30703525e+00, -1.16731354e+00],\n",
              "        [ 2.53951333e+00, -7.29674348e-02],\n",
              "        [-9.76365821e-02, -1.94969085e-01],\n",
              "        [ 1.60663203e+00, -1.13067848e+00],\n",
              "        [-1.93709034e-01,  1.85199160e+00],\n",
              "        [ 2.23898560e-01, -4.00037449e-02],\n",
              "        [ 1.02735575e+00,  9.99444459e-01],\n",
              "        [ 1.66867972e-01,  1.37301272e+00],\n",
              "        [ 7.25919966e-01, -8.40558190e-01],\n",
              "        [-4.14619812e-01, -5.21097750e-01],\n",
              "        [ 2.34431322e-01, -1.11400704e+00],\n",
              "        [-1.02669141e-01, -2.45622654e-01],\n",
              "        [-1.14560378e+00, -1.12607280e-01],\n",
              "        [ 2.69131210e+00, -2.53916318e-01],\n",
              "        [-9.53272242e-01,  1.86033080e+00],\n",
              "        [ 2.19767513e+00,  1.17447527e-01],\n",
              "        [-3.41488482e-01,  9.33536393e-01],\n",
              "        [ 3.08944716e-01,  2.71764082e-01],\n",
              "        [-4.65271850e-01,  1.64842999e+00],\n",
              "        [ 2.98135036e-01,  4.43167581e-01],\n",
              "        [-9.33115123e-01, -6.72105750e-01],\n",
              "        [ 2.28863998e+00,  2.03108305e-01],\n",
              "        [-6.89366568e-01,  7.23940587e-01],\n",
              "        [ 5.81614904e-01,  1.41239638e+00],\n",
              "        [-2.46124762e-01,  4.70171079e-01],\n",
              "        [-1.10729367e+00,  1.10084865e+00],\n",
              "        [-1.08109509e+00, -9.55261491e-01],\n",
              "        [ 1.30086718e+00,  7.90331778e-01],\n",
              "        [ 9.49140752e-01,  1.13378684e+00],\n",
              "        [ 4.03343512e-01, -1.18144701e+00],\n",
              "        [ 1.25838984e+00,  8.14428936e-01],\n",
              "        [ 2.10553251e+00,  2.04110566e-01],\n",
              "        [-4.67095185e-01,  1.73027049e+00],\n",
              "        [ 1.71591188e+00,  2.98827140e-01],\n",
              "        [ 2.43823001e-02, -1.74471417e-01],\n",
              "        [-2.36621573e+00,  1.11700610e-01],\n",
              "        [ 1.62640888e+00,  7.47502437e-01],\n",
              "        [ 8.47299632e-01,  1.05875704e+00],\n",
              "        [ 1.43858200e+00, -7.69812784e-01],\n",
              "        [-1.36693019e+00,  1.44362902e+00],\n",
              "        [-4.81598319e-01, -2.64799686e-01],\n",
              "        [-1.57972869e+00, -9.26244499e-02],\n",
              "        [ 9.11255946e-01, -9.59918736e-01],\n",
              "        [-4.31171924e-01,  2.14837671e+00],\n",
              "        [-3.84516669e+00, -3.53189772e+00],\n",
              "        [-5.88251855e-01,  2.39633329e+00],\n",
              "        [ 2.92020832e-01,  1.43316007e+00],\n",
              "        [-1.36750918e+00,  1.15409568e+00],\n",
              "        [ 8.73106827e-01,  1.04839513e+00],\n",
              "        [ 2.60675347e+00, -1.07374689e-01],\n",
              "        [ 1.02953465e+00,  9.19518371e-01],\n",
              "        [-7.63016366e-01, -7.47331914e-01],\n",
              "        [-2.19077898e-01,  2.00850459e+00],\n",
              "        [-1.03110059e+00,  1.49793799e+00],\n",
              "        [-1.08569185e+00,  8.57924729e-01],\n",
              "        [-1.14554246e-01,  1.70097336e+00],\n",
              "        [-3.03817807e-01,  2.06131963e+00],\n",
              "        [ 1.37457316e+00, -9.84687572e-01],\n",
              "        [-1.28757729e+00, -2.48420959e-01],\n",
              "        [-1.58699818e+00, -1.68500906e+00],\n",
              "        [-2.57254344e+00, -5.67245715e-01],\n",
              "        [-3.49426737e-01,  1.53661751e+00],\n",
              "        [-1.53493211e+00, -1.35908778e+00],\n",
              "        [-2.00387447e+00, -7.74858858e-01],\n",
              "        [-3.07858733e-01, -5.30408365e-02],\n",
              "        [ 1.59368307e-01, -7.22677608e-01],\n",
              "        [-4.15560379e-01, -6.37232073e-01],\n",
              "        [ 2.61954387e+00, -1.05985923e+00],\n",
              "        [-1.46090561e+00, -1.18148301e+00],\n",
              "        [-1.20908788e+00,  7.53534829e-01],\n",
              "        [-1.91673335e-01,  1.95463268e+00],\n",
              "        [-7.14648423e-01, -7.21520901e-01],\n",
              "        [-7.50431493e-01, -7.21794955e-01]]),\n",
              " array([0., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 0.,\n",
              "        0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0.,\n",
              "        1., 0., 1., 0., 1., 0., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1.,\n",
              "        0., 1., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 0., 1., 1.,\n",
              "        0., 0., 0., 0., 1., 1., 0., 0., 1., 0., 1., 0., 1., 1., 1., 1., 1.,\n",
              "        0., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 0., 1.,\n",
              "        1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1.,\n",
              "        1., 0., 1., 1., 0., 0., 1., 1., 1., 0., 1., 0., 1., 0., 0., 1., 1.,\n",
              "        1., 0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 1., 0., 1.,\n",
              "        0., 0., 1., 1., 1., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
              "        0., 0., 0., 1., 1., 0., 0., 1., 0., 1., 1., 1., 0., 0., 0., 0., 1.,\n",
              "        1., 1., 1., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0., 0., 0., 1., 1.,\n",
              "        1., 1., 0., 0., 1., 1., 1., 0., 1., 1., 0., 0., 0., 1., 0., 0., 1.,\n",
              "        0., 0., 1., 1., 1., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
              "        0., 0., 1., 0., 0., 1., 1., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\n",
              "        0., 1., 1., 1., 0., 1., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1.,\n",
              "        0., 0., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 0., 0.,\n",
              "        1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 1., 1., 1.,\n",
              "        1., 1., 0., 1., 0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 0., 0., 1.,\n",
              "        0., 1., 1., 1., 0., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 1., 0., 1., 0., 0., 1., 0., 0.]),\n",
              " <keras.engine.sequential.Sequential at 0x7f595e867cf8>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "deNx0joDeCHM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7531
        },
        "outputId": "9429adb1-8a92-4241-9f4a-4f82749f5346"
      },
      "cell_type": "code",
      "source": [
        "run_keras_2d(\"2\", archs(2)[4], 10, split=0.25, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 2\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (200, 2) y (200,) classes {0.0, 1.0}\n",
            "\n",
            "Loss on validation set:0.21735329518439472 Accuracy on validation set: 0.915\n",
            "\n",
            "Loss on validation set:0.20132579104017714 Accuracy on validation set: 0.915\n",
            "\n",
            "Loss on validation set:0.19684233161191883 Accuracy on validation set: 0.925\n",
            "\n",
            "Loss on validation set:0.2054085673844319 Accuracy on validation set: 0.92\n",
            "\n",
            "Loss on validation set:0.22211212343669778 Accuracy on validation set: 0.905\n",
            "\n",
            "Avg. validation accuracy:0.916\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[-3.07197403e+00, -3.09217414e+00],\n",
              "        [-2.26550369e+00, -1.41720255e+00],\n",
              "        [-9.76365821e-02, -1.94969085e-01],\n",
              "        [ 1.64799855e-01,  1.43407080e+00],\n",
              "        [-1.91673335e-01,  1.95463268e+00],\n",
              "        [ 1.60663203e+00, -1.13067848e+00],\n",
              "        [ 3.98998700e-01, -8.24673303e-01],\n",
              "        [ 2.68942514e+00, -7.18395270e-01],\n",
              "        [-1.30422514e+00, -1.73323671e-01],\n",
              "        [-4.14619812e-01, -5.21097750e-01],\n",
              "        [-1.35201342e+00,  5.86232664e-01],\n",
              "        [ 1.94482126e+00, -1.09518442e+00],\n",
              "        [ 1.18151883e+00, -1.10309630e+00],\n",
              "        [ 5.26712401e-01, -1.23759149e+00],\n",
              "        [-3.07858733e-01, -5.30408365e-02],\n",
              "        [-1.32852528e+00,  8.72765446e-01],\n",
              "        [ 1.42643563e+00, -8.48521458e-01],\n",
              "        [ 1.07810322e+00, -7.35877513e-01],\n",
              "        [ 8.19718455e-01, -8.71823660e-01],\n",
              "        [-8.41387781e-01, -6.55598384e-01],\n",
              "        [ 8.47792201e-01,  9.77964687e-01],\n",
              "        [ 1.29983966e+00,  6.79588882e-01],\n",
              "        [-1.28757729e+00, -2.48420959e-01],\n",
              "        [-1.35537659e+00, -6.66133824e-02],\n",
              "        [ 2.53951333e+00, -7.29674348e-02],\n",
              "        [ 1.08681935e+00,  9.30857621e-01],\n",
              "        [ 1.37457316e+00, -9.84687572e-01],\n",
              "        [-3.45539171e-01,  2.83099351e-01],\n",
              "        [ 3.08360544e-01, -1.16684146e+00],\n",
              "        [ 3.39392196e+00, -6.68161919e-01],\n",
              "        [-1.90008080e+00, -1.80945582e+00],\n",
              "        [-1.74081340e+00,  1.60936263e-01],\n",
              "        [-9.21277900e-01,  2.30607084e+00],\n",
              "        [ 3.08944716e-01,  2.71764082e-01],\n",
              "        [ 1.02025116e+00,  9.69768986e-01],\n",
              "        [-7.04391246e-01, -6.03308713e-01],\n",
              "        [-1.44334058e-01,  9.41108388e-01],\n",
              "        [ 7.24359432e-01, -1.21214224e+00],\n",
              "        [-8.21742059e-01,  1.22294898e+00],\n",
              "        [ 1.57779603e+00,  5.93931515e-01],\n",
              "        [ 4.19436090e-01,  2.73193792e-01],\n",
              "        [ 2.07866520e-01, -2.75360165e-01],\n",
              "        [ 1.66798937e+00, -9.63566174e-01],\n",
              "        [-3.03817807e-01,  2.06131963e+00],\n",
              "        [-5.72601871e-01,  2.11756498e+00],\n",
              "        [-1.08569185e+00,  8.57924729e-01],\n",
              "        [-1.16939583e-01, -1.00651720e+00],\n",
              "        [ 1.57697244e+00,  5.74478871e-01],\n",
              "        [ 6.90088182e-01,  3.46665370e+00],\n",
              "        [-1.36693019e+00,  1.44362902e+00],\n",
              "        [-3.15691448e-01,  1.90027984e+00],\n",
              "        [-1.21230484e+00, -1.00046553e+00],\n",
              "        [-1.37537243e+00, -1.16004881e-01],\n",
              "        [-6.29939337e-01, -6.97129224e-01],\n",
              "        [ 2.32126285e-01,  3.42873063e+00],\n",
              "        [ 2.28863998e+00,  2.03108305e-01],\n",
              "        [-6.16310244e-01,  2.61525230e+00],\n",
              "        [-7.58281099e-01, -6.23951011e-01],\n",
              "        [-1.92869785e+00, -1.78083364e+00],\n",
              "        [-7.07188277e-01,  1.90105335e+00],\n",
              "        [-9.01038899e-01, -1.58127225e-01],\n",
              "        [-2.60372971e+00, -1.33239916e+00],\n",
              "        [-1.26950299e+00, -8.17906295e-01],\n",
              "        [-3.88712941e-01,  1.77506085e+00],\n",
              "        [ 5.18914019e-01,  1.24502639e+00],\n",
              "        [ 3.89868104e-02,  1.74244888e+00],\n",
              "        [-1.23137241e+00,  1.36368207e+00],\n",
              "        [-7.63016366e-01, -7.47331914e-01],\n",
              "        [ 1.20175672e+00, -9.30735169e-01],\n",
              "        [ 8.76541883e-01,  8.48365509e-01],\n",
              "        [ 1.05994811e+00,  1.03747218e+00],\n",
              "        [-1.46867783e+00,  2.11090221e-01],\n",
              "        [-9.18193374e-01,  4.93238213e-01],\n",
              "        [-2.35833682e+00, -3.01459268e-01],\n",
              "        [ 1.07430328e+00, -1.10622246e+00],\n",
              "        [-7.51873287e-01, -9.06438672e-01],\n",
              "        [ 1.43858200e+00, -7.69812784e-01],\n",
              "        [ 2.93140733e-01,  2.09981566e-01],\n",
              "        [-1.48968586e+00, -1.34403643e+00],\n",
              "        [ 3.73268868e-01, -9.99680103e-01],\n",
              "        [-9.37735593e-01, -8.55627235e-01],\n",
              "        [-4.42767576e-01, -7.70831748e-01],\n",
              "        [ 3.50949222e-01,  1.49877022e+00],\n",
              "        [-2.36621573e+00,  1.11700610e-01],\n",
              "        [ 3.10719492e-01,  1.30610237e+00],\n",
              "        [-1.37762951e-01,  2.09710759e+00],\n",
              "        [ 2.79719766e-01,  1.34451459e+00],\n",
              "        [-6.29472025e-01, -8.38701239e-01],\n",
              "        [-1.08957216e-01,  2.42615396e-03],\n",
              "        [ 3.76113477e+00, -1.10954126e+00],\n",
              "        [-2.57254344e+00, -5.67245715e-01],\n",
              "        [-1.18512673e+00,  7.00500961e-01],\n",
              "        [-3.05976739e+00, -2.77813530e+00],\n",
              "        [-1.24883647e-01, -1.41134448e-01],\n",
              "        [ 6.38815181e-01, -1.07125863e+00],\n",
              "        [ 1.55322202e+00, -1.15509002e+00],\n",
              "        [ 1.27621763e+00, -1.04805327e+00],\n",
              "        [-1.48103028e+00, -1.55116116e+00],\n",
              "        [ 2.10553251e+00,  2.04110566e-01],\n",
              "        [-1.36901720e+00, -1.46302366e+00],\n",
              "        [-1.89990186e+00, -1.99978217e+00],\n",
              "        [-1.10729367e+00,  1.10084865e+00],\n",
              "        [ 1.45166294e+00, -9.35462323e-01],\n",
              "        [-6.36141556e-01,  1.78870373e-01],\n",
              "        [-2.41708091e+00, -2.15882089e+00],\n",
              "        [-4.06029615e-01, -5.24708973e-01],\n",
              "        [-1.81229220e+00, -4.08248228e-01],\n",
              "        [-5.97746959e-01, -7.40813089e-01],\n",
              "        [ 1.04884381e+00,  8.74936808e-01],\n",
              "        [-4.81598319e-01, -2.64799686e-01],\n",
              "        [ 1.75026056e+00,  4.92125173e-01],\n",
              "        [-6.46735016e-01,  6.06647047e-01],\n",
              "        [ 8.68697101e-01,  9.47653547e-01],\n",
              "        [-4.98829964e-01, -3.93809547e-01],\n",
              "        [ 1.71591188e+00,  2.98827140e-01],\n",
              "        [-1.12861044e+00,  7.56588440e-01],\n",
              "        [-2.85826391e-01,  1.83816154e+00],\n",
              "        [ 2.65477985e-01, -8.30744385e-01],\n",
              "        [ 2.36331478e-01,  1.69805471e+00],\n",
              "        [ 5.97812083e-01, -1.15361297e+00],\n",
              "        [-6.89366568e-01,  7.23940587e-01],\n",
              "        [ 3.21315400e-02, -6.04895313e-01],\n",
              "        [ 1.41173550e+00, -8.62062765e-01],\n",
              "        [-1.31652535e+00,  1.13014664e+00],\n",
              "        [-1.66640200e+00,  9.67777299e-01],\n",
              "        [ 4.68547777e-01,  3.25495022e-01],\n",
              "        [-1.67749204e+00,  2.77485781e-02],\n",
              "        [ 1.66486416e+00, -1.14545031e+00],\n",
              "        [-1.56702723e+00, -2.57259783e-01],\n",
              "        [-4.15560379e-01, -6.37232073e-01],\n",
              "        [ 9.49140752e-01,  1.13378684e+00],\n",
              "        [-4.22934818e-01,  1.95592950e+00],\n",
              "        [-1.90665505e+00, -1.15958454e+00],\n",
              "        [ 1.28582961e+00, -1.09647456e+00],\n",
              "        [ 2.43823001e-02, -1.74471417e-01],\n",
              "        [-7.29678795e-01,  1.23888643e+00],\n",
              "        [-2.75614208e-01,  2.57765334e+00],\n",
              "        [-1.24686631e+00, -8.37097756e-01],\n",
              "        [-5.80205564e-01,  1.90971032e+00],\n",
              "        [-6.03551194e-01,  1.82856381e+00],\n",
              "        [-1.26191468e+00,  2.51863932e+00],\n",
              "        [-2.24065463e-01,  2.17059256e+00],\n",
              "        [-1.46460089e+00, -1.50083364e+00],\n",
              "        [-1.08028402e+00,  1.04648140e+00],\n",
              "        [-9.47126381e-01,  1.85008281e+00],\n",
              "        [ 1.09082642e+00, -1.34696817e+00],\n",
              "        [-2.58470717e-01, -1.10330149e+00],\n",
              "        [ 7.25919966e-01, -8.40558190e-01],\n",
              "        [ 1.59368307e-01, -7.22677608e-01],\n",
              "        [-8.24096888e-02,  1.83851715e+00],\n",
              "        [-9.87987581e-01, -6.47696279e-01],\n",
              "        [-2.46124762e-01,  4.70171079e-01],\n",
              "        [-5.92740329e-01, -6.62168505e-01],\n",
              "        [-2.00387447e+00, -7.74858858e-01],\n",
              "        [-6.71602824e-01, -7.68705151e-01],\n",
              "        [-1.57972869e+00, -9.26244499e-02],\n",
              "        [ 6.63736839e-01, -6.85814959e-01],\n",
              "        [ 1.38143360e+00, -8.37434812e-01],\n",
              "        [-2.46493986e+00, -2.41956036e+00],\n",
              "        [ 9.11255946e-01, -9.59918736e-01],\n",
              "        [ 2.58207855e+00, -2.98252361e-01],\n",
              "        [-2.35479504e-02,  1.69099223e+00],\n",
              "        [ 1.96807656e+00, -9.11262146e-01],\n",
              "        [-2.52164834e+00, -2.47998040e+00],\n",
              "        [ 1.66867972e-01,  1.37301272e+00],\n",
              "        [-1.45325783e+00, -1.97605623e+00],\n",
              "        [-1.02669141e-01, -2.45622654e-01],\n",
              "        [-1.23195864e-01,  1.75266999e+00],\n",
              "        [ 2.70059942e-01, -1.21916712e+00],\n",
              "        [-7.95287193e-01, -9.36767686e-01],\n",
              "        [-9.95548619e-01, -8.46667114e-01],\n",
              "        [-1.03110059e+00,  1.49793799e+00],\n",
              "        [ 3.36116762e-01,  2.13401670e-01],\n",
              "        [-8.86208956e-02, -1.11372460e+00],\n",
              "        [-1.17963559e+00, -1.15355099e+00],\n",
              "        [ 3.79872711e-01,  5.58439066e-01],\n",
              "        [-2.19077898e-01,  2.00850459e+00],\n",
              "        [ 2.22929994e+00,  6.92925451e-03],\n",
              "        [ 8.86340826e-01, -8.20409764e-01],\n",
              "        [ 8.19290972e-01,  1.15573589e+00],\n",
              "        [-4.69034360e-01, -7.51710454e-01],\n",
              "        [-1.62337680e+00, -5.24442976e-01],\n",
              "        [ 1.18346748e+00,  8.40906891e-01],\n",
              "        [ 2.23898560e-01, -4.00037449e-02],\n",
              "        [ 2.93319964e-01,  1.62823666e+00],\n",
              "        [ 2.61954387e+00, -1.05985923e+00],\n",
              "        [-2.57583372e+00, -7.90985772e-01],\n",
              "        [ 1.02953465e+00,  9.19518371e-01],\n",
              "        [-2.33903406e-01, -9.22549325e-01],\n",
              "        [-1.14560378e+00, -1.12607280e-01],\n",
              "        [ 8.47299632e-01,  1.05875704e+00],\n",
              "        [-7.94594405e-01,  1.86386232e+00],\n",
              "        [ 1.66147159e+00,  5.40435722e-01],\n",
              "        [-1.36880785e+00, -1.49879685e+00],\n",
              "        [ 1.17890861e+00, -1.20233299e+00],\n",
              "        [ 5.23058521e-01,  2.14536426e+00],\n",
              "        [-5.14962371e-01, -7.77415165e-01],\n",
              "        [-1.85258485e+00,  3.09931699e+00],\n",
              "        [ 2.53917995e+00, -1.23445992e-01],\n",
              "        [ 1.13835016e+00, -1.15406322e+00],\n",
              "        [-1.00429615e+00,  9.53989013e-01],\n",
              "        [-1.39075533e+00, -3.64814143e-01],\n",
              "        [ 1.18625907e+00, -9.52392458e-01],\n",
              "        [ 3.57445020e-01, -1.39391903e+00],\n",
              "        [ 1.94384782e-01, -9.86623532e-01],\n",
              "        [ 8.23612562e-02,  2.41533234e-01],\n",
              "        [ 1.25838984e+00,  8.14428936e-01],\n",
              "        [-1.60516718e+00, -1.30209115e-01],\n",
              "        [ 1.01633165e+00,  1.37710735e+00],\n",
              "        [ 3.02060944e+00, -1.20300597e+00],\n",
              "        [-1.08109509e+00, -9.55261491e-01],\n",
              "        [-7.50431493e-01, -7.21794955e-01],\n",
              "        [-1.53493211e+00, -1.35908778e+00],\n",
              "        [-1.19181144e+00,  2.59930958e+00],\n",
              "        [ 2.19767513e+00,  1.17447527e-01],\n",
              "        [-3.84516669e+00, -3.53189772e+00],\n",
              "        [ 2.10673747e+00, -9.08443385e-01],\n",
              "        [-3.22573916e-03, -1.20271331e+00],\n",
              "        [ 1.17777437e+00,  1.01847217e+00],\n",
              "        [-7.28370853e-01, -1.18234340e+00],\n",
              "        [-1.97942274e+00, -1.87038098e+00],\n",
              "        [ 6.45990947e-01,  4.89172146e-01],\n",
              "        [-1.13580376e+00, -1.20385579e+00],\n",
              "        [-1.18198912e+00, -1.20678058e+00],\n",
              "        [ 2.92020832e-01,  1.43316007e+00],\n",
              "        [ 1.68331248e+00, -1.08368613e+00],\n",
              "        [ 2.39710997e+00,  8.71732474e-02],\n",
              "        [ 2.76443697e+00, -3.35805408e-01],\n",
              "        [-1.18649873e+00, -1.18973055e+00],\n",
              "        [-7.18229216e-01, -7.69472198e-01],\n",
              "        [ 8.73106827e-01,  1.04839513e+00],\n",
              "        [-5.05427102e-01,  2.25922912e+00],\n",
              "        [-1.46654240e+00, -9.51878526e-01],\n",
              "        [ 1.42432842e+00,  6.12940999e-01],\n",
              "        [ 1.00612700e+00, -9.40350985e-01],\n",
              "        [-7.00003279e-01,  1.34423323e+00],\n",
              "        [-4.65271850e-01,  1.64842999e+00],\n",
              "        [-4.67095185e-01,  1.73027049e+00],\n",
              "        [-7.20624165e-01,  1.87336314e+00],\n",
              "        [ 6.42435955e-01,  5.52341175e-01],\n",
              "        [ 1.51241210e-01, -8.95427218e-01],\n",
              "        [ 3.34484900e+00, -1.33652016e+00],\n",
              "        [ 2.89792941e+00, -1.35873607e+00],\n",
              "        [-1.28607425e+00, -1.35485789e+00],\n",
              "        [ 5.50561778e-01,  2.26509563e+00],\n",
              "        [-2.22585412e+00, -4.07447840e-01],\n",
              "        [ 2.68221896e+00, -6.58780220e-01],\n",
              "        [-1.41195475e+00,  1.28715574e+00],\n",
              "        [ 2.89856474e+00, -4.27914418e-01],\n",
              "        [-1.69789718e+00,  6.41344934e-01],\n",
              "        [-6.49323337e-01, -9.85603310e-01],\n",
              "        [ 2.69344042e+00, -2.50460842e-01],\n",
              "        [ 2.69131210e+00, -2.53916318e-01],\n",
              "        [-1.03428819e+00,  4.34307627e-01],\n",
              "        [ 1.03370608e+00, -1.17180061e+00],\n",
              "        [ 2.97355021e+00, -5.76570301e-01],\n",
              "        [-2.43317123e+00, -2.51628576e+00],\n",
              "        [ 2.94528449e-01,  2.27694472e-01],\n",
              "        [-4.19772033e-01, -1.61455877e-01],\n",
              "        [-8.19505075e-01,  3.12796208e+00],\n",
              "        [ 2.61965174e-01,  1.64526150e+00],\n",
              "        [-8.61137678e-01,  7.81567059e-01],\n",
              "        [-2.76591496e+00, -2.90422443e+00],\n",
              "        [ 1.78788530e+00, -8.24769511e-01],\n",
              "        [-2.50101622e+00, -2.37689187e+00],\n",
              "        [ 1.76262011e+00,  4.87321452e-01],\n",
              "        [-9.82064734e-01, -2.67181538e-02],\n",
              "        [-2.06570460e+00, -1.44385339e+00],\n",
              "        [-1.87761734e+00, -4.85096491e-02],\n",
              "        [-7.14648423e-01, -7.21520901e-01],\n",
              "        [-1.58699818e+00, -1.68500906e+00],\n",
              "        [-1.73439407e+00,  5.68351852e-01],\n",
              "        [ 1.03561105e+00,  1.05972780e+00],\n",
              "        [ 3.05865114e+00, -8.82678285e-01],\n",
              "        [ 1.56463949e+00, -9.08035870e-01],\n",
              "        [-3.86104675e-01,  1.26658926e+00],\n",
              "        [-5.88251855e-01,  2.39633329e+00],\n",
              "        [-1.20908788e+00,  7.53534829e-01],\n",
              "        [-1.10228747e+00, -1.09571841e+00],\n",
              "        [-4.25911024e-01, -5.83676425e-01],\n",
              "        [-1.65677387e+00,  2.93458563e+00],\n",
              "        [ 1.27035750e+00, -7.89908472e-01],\n",
              "        [ 2.19311623e+00,  1.04578246e-01],\n",
              "        [-5.05832862e-02,  1.57840839e+00],\n",
              "        [ 4.31793087e-01, -1.18985592e+00],\n",
              "        [ 7.43743410e-01, -1.11485766e+00],\n",
              "        [ 1.30086718e+00,  7.90331778e-01],\n",
              "        [ 2.10579726e+00, -5.83057511e-01],\n",
              "        [ 8.19755298e-01,  4.60879228e-01],\n",
              "        [-5.17793384e-01, -5.98408386e-01],\n",
              "        [-5.49375511e-01,  2.17692846e+00],\n",
              "        [-3.49421923e+00, -3.46012699e+00],\n",
              "        [ 3.23567685e+00, -6.76676337e-01],\n",
              "        [-6.65578011e-01, -1.03588191e+00],\n",
              "        [ 4.53498957e-01, -9.35699796e-01],\n",
              "        [-8.37372488e-01,  2.43210108e+00],\n",
              "        [-1.44029872e+00,  8.04109469e-01],\n",
              "        [-1.42668198e+00,  5.16627514e-01],\n",
              "        [ 1.06082281e+00, -1.11885549e+00],\n",
              "        [ 2.13573174e+00, -1.17763617e+00],\n",
              "        [-4.10163532e-01,  6.46080198e-01],\n",
              "        [ 8.74059390e-01, -1.19176435e+00],\n",
              "        [ 1.30703525e+00, -1.16731354e+00],\n",
              "        [ 3.19120517e+00, -4.97730362e-01],\n",
              "        [-2.16376388e+00, -2.34818460e+00],\n",
              "        [ 8.62984094e-01, -9.72644603e-01],\n",
              "        [-3.49426737e-01,  1.53661751e+00],\n",
              "        [ 4.03343512e-01, -1.18144701e+00],\n",
              "        [ 6.84756854e-01,  1.21344045e+00],\n",
              "        [-3.53837461e-01,  2.12256043e+00],\n",
              "        [-2.09917294e+00,  1.84756054e-01],\n",
              "        [-1.15121409e+00, -3.41179981e-02],\n",
              "        [ 2.98135036e-01,  4.43167581e-01],\n",
              "        [ 5.81614904e-01,  1.41239638e+00],\n",
              "        [-1.36750918e+00,  1.15409568e+00],\n",
              "        [ 2.34431322e-01, -1.11400704e+00],\n",
              "        [ 2.68940073e-01,  2.54195359e+00],\n",
              "        [-1.59501645e+00,  2.87988451e-01],\n",
              "        [-6.48179440e-01,  1.47403690e+00],\n",
              "        [-1.46853464e+00,  1.57104828e+00],\n",
              "        [-1.11318095e+00,  1.27803332e-01],\n",
              "        [ 3.18447848e+00, -1.37575144e+00],\n",
              "        [-1.65987525e+00, -1.73574922e+00],\n",
              "        [-9.36416355e-02,  1.99327266e+00],\n",
              "        [ 2.13283112e+00,  1.57732915e-01],\n",
              "        [-1.77585096e+00, -3.67076701e-01],\n",
              "        [ 9.24357799e-01,  8.82615400e-01],\n",
              "        [ 6.19896012e-01,  1.35393107e+00],\n",
              "        [-6.02091141e-01, -6.87159722e-01],\n",
              "        [-2.01380386e-01,  3.89440035e-02],\n",
              "        [-5.03929692e-01,  2.19314649e+00],\n",
              "        [-4.82403452e-02, -1.36956309e+00],\n",
              "        [ 1.98855244e+00,  5.71003530e-01],\n",
              "        [-1.10703946e+00, -1.06669431e+00],\n",
              "        [-1.14554246e-01,  1.70097336e+00],\n",
              "        [ 1.29690447e+00,  6.12584986e-01],\n",
              "        [-3.38675241e-01,  2.18599111e+00],\n",
              "        [ 1.55221528e+00, -1.01146236e+00],\n",
              "        [-1.81904649e-01, -7.72614030e-01],\n",
              "        [-1.46090561e+00, -1.18148301e+00],\n",
              "        [-1.28442381e+00,  4.07752035e-01],\n",
              "        [ 6.71250029e-01,  1.28652238e+00],\n",
              "        [ 8.70850526e-01, -1.15025051e+00],\n",
              "        [-6.90958835e-01,  2.38342967e+00],\n",
              "        [-2.60168743e+00, -2.56066661e+00],\n",
              "        [-2.88914171e+00, -2.85959001e+00],\n",
              "        [ 6.17684409e-01,  1.29507295e+00],\n",
              "        [-9.00968364e-01,  9.42808569e-01],\n",
              "        [-1.05326962e+00,  1.15185857e+00],\n",
              "        [-3.75347204e-01,  2.02717929e+00],\n",
              "        [-1.35954469e+00,  4.12253503e-01],\n",
              "        [ 6.26902264e-01,  5.37022914e-01],\n",
              "        [-1.09381583e+00,  2.53389307e+00],\n",
              "        [-1.93709034e-01,  1.85199160e+00],\n",
              "        [-9.33115123e-01, -6.72105750e-01],\n",
              "        [-7.33049608e-01,  2.38239045e+00],\n",
              "        [ 2.18903178e+00, -1.08510531e+00],\n",
              "        [ 1.98640750e+00, -1.01913834e+00],\n",
              "        [-2.38213776e+00, -4.27446912e-01],\n",
              "        [ 1.81755983e+00,  4.29775512e-01],\n",
              "        [-1.00426430e+00, -8.12181745e-01],\n",
              "        [-7.93556771e-01, -7.88516236e-01],\n",
              "        [ 3.27843287e+00, -1.17543098e+00],\n",
              "        [-9.41722312e-01, -6.40421937e-02],\n",
              "        [-1.34403040e+00,  8.18472503e-01],\n",
              "        [-6.51919047e-01, -7.54819636e-01],\n",
              "        [ 5.89576749e-01,  1.23463526e+00],\n",
              "        [-4.31171924e-01,  2.14837671e+00],\n",
              "        [ 2.59916910e+00,  2.33886611e+00],\n",
              "        [ 2.30127946e+00, -1.10082751e+00],\n",
              "        [-5.10640051e-01, -1.07910459e+00],\n",
              "        [ 2.60675347e+00, -1.07374689e-01],\n",
              "        [ 1.35476195e-01,  1.64980982e+00],\n",
              "        [-1.52803357e+00,  1.06207542e-01],\n",
              "        [ 1.02227645e+00, -9.88603122e-01],\n",
              "        [-7.39356638e-01,  1.44056221e+00],\n",
              "        [ 2.27983193e-01, -7.77485339e-01],\n",
              "        [-3.76479700e-01,  1.45439639e+00],\n",
              "        [-6.09349593e-01,  2.77101678e+00],\n",
              "        [ 1.85827112e+00,  3.82066484e-01],\n",
              "        [-1.07468028e+00,  1.29721447e+00],\n",
              "        [-3.41488482e-01,  9.33536393e-01],\n",
              "        [-6.56994421e-01,  9.79162187e-01],\n",
              "        [ 2.43020283e+00, -8.97288931e-01],\n",
              "        [-1.71643943e+00,  5.75962337e-01],\n",
              "        [ 2.46117425e-01,  1.60895523e-01],\n",
              "        [ 1.62640888e+00,  7.47502437e-01],\n",
              "        [-8.78887779e-01,  2.32969272e-01],\n",
              "        [ 8.90807683e-01, -1.10688054e+00],\n",
              "        [ 1.28383683e+00,  9.42786437e-01],\n",
              "        [-2.13973685e-01, -1.20527645e-01],\n",
              "        [-9.53272242e-01,  1.86033080e+00],\n",
              "        [-1.56802547e+00,  5.53409373e-01],\n",
              "        [-4.93366855e-01,  1.34172764e+00],\n",
              "        [ 5.35555417e-01, -1.26659885e+00],\n",
              "        [-1.95841437e-01, -2.67822051e-01],\n",
              "        [-1.87850595e+00,  5.31465958e-01],\n",
              "        [-1.31367184e+00,  1.87047926e+00],\n",
              "        [ 1.02735575e+00,  9.99444459e-01],\n",
              "        [-5.89423801e-01,  2.26165633e+00]]),\n",
              " array([0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 0., 0., 1.,\n",
              "        1., 1., 0., 1., 1., 0., 0., 1., 1., 1., 0., 1., 1., 0., 0., 1., 0.,\n",
              "        1., 0., 0., 1., 0., 1., 0., 0., 1., 1., 1., 0., 1., 1., 0., 0., 1.,\n",
              "        1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0.,\n",
              "        1., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 1., 0., 0., 1., 0., 1.,\n",
              "        0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 0., 1., 0., 0., 0.,\n",
              "        1., 0., 0., 0., 0., 1., 1., 0., 1., 0., 1., 0., 1., 0., 1., 1., 1.,\n",
              "        1., 0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0.,\n",
              "        0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0.,\n",
              "        0., 1., 0., 1., 1., 0., 1., 1., 1., 1., 0., 1., 0., 0., 1., 1., 0.,\n",
              "        0., 0., 0., 1., 1., 0., 1., 1., 1., 1., 0., 0., 1., 0., 1., 1., 0.,\n",
              "        1., 1., 0., 1., 0., 1., 0., 1., 0., 0., 1., 1., 1., 0., 0., 1., 1.,\n",
              "        1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 0., 1., 1., 1., 1., 0.,\n",
              "        0., 0., 0., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.,\n",
              "        0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 1., 0., 0., 1., 1., 0., 1.,\n",
              "        1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
              "        1., 1., 1., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
              "        0., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 0., 1., 1., 1., 0., 1.,\n",
              "        0., 1., 1., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 1., 0.,\n",
              "        0., 1., 0., 1., 1., 0., 0., 1., 1., 1., 0., 1., 1., 1., 1., 1., 0.,\n",
              "        0., 1., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1.,\n",
              "        1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0.,\n",
              "        1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1., 0.,\n",
              "        0., 0., 0., 1., 0., 0., 0., 1., 1.]),\n",
              " <keras.engine.sequential.Sequential at 0x7f595d5bf208>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "uBhaIxgTbR4M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7531
        },
        "outputId": "53889afb-f694-4717-a519-1e613ba08a54"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3\", archs(2)[3], 10, split=0.25, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (200, 2) y (200,) classes {0.0, 1.0}\n",
            "\n",
            "Loss on validation set:0.11737349464441955 Accuracy on validation set: 0.965\n",
            "\n",
            "Loss on validation set:0.11629107871028928 Accuracy on validation set: 0.965\n",
            "\n",
            "Loss on validation set:0.11663985382366093 Accuracy on validation set: 0.96\n",
            "\n",
            "Loss on validation set:0.10883719142266728 Accuracy on validation set: 0.97\n",
            "\n",
            "Loss on validation set:0.12772527949093587 Accuracy on validation set: 0.955\n",
            "\n",
            "Avg. validation accuracy:0.9629999999999999\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 9.64624265e-01, -1.72598657e+00],\n",
              "        [ 3.11442245e-01,  1.10354795e+00],\n",
              "        [ 9.33095520e-02, -1.34614077e+00],\n",
              "        [-4.98048222e-01, -2.05324570e+00],\n",
              "        [-2.87826838e-01,  8.75110161e-01],\n",
              "        [-6.07685719e-01, -9.00295674e-01],\n",
              "        [ 1.53031689e+00, -1.42527722e+00],\n",
              "        [ 2.88990304e-01, -3.04575699e-01],\n",
              "        [ 2.84183705e-01,  9.97209950e-01],\n",
              "        [-8.66231054e-01,  1.14435380e+00],\n",
              "        [-6.11864335e-01, -3.04394204e-01],\n",
              "        [-1.02646091e+00,  7.30186986e-01],\n",
              "        [ 2.15120831e-01, -1.07328215e+00],\n",
              "        [ 3.07141783e-01, -5.24623805e-01],\n",
              "        [ 1.62752185e-01,  9.21716731e-01],\n",
              "        [ 4.28358501e-01, -1.10757364e+00],\n",
              "        [-3.13859609e-01,  1.08169038e+00],\n",
              "        [-9.78487004e-01,  1.33753667e+00],\n",
              "        [ 1.29299584e+00,  1.13473841e+00],\n",
              "        [-6.44300389e-01,  1.14495920e+00],\n",
              "        [-3.90784151e-01,  9.14456623e-01],\n",
              "        [ 1.56110391e+00, -4.70849410e-01],\n",
              "        [ 7.15681337e-01, -7.61277410e-02],\n",
              "        [-2.48121610e+00, -1.36699313e+00],\n",
              "        [ 8.25923398e-01,  1.33663561e+00],\n",
              "        [ 5.01637382e-02, -1.86205461e-01],\n",
              "        [-1.95634261e-02,  1.08597651e+00],\n",
              "        [ 3.71876077e-01,  1.20938305e+00],\n",
              "        [-8.20603757e-01,  7.74998455e-01],\n",
              "        [-5.30929289e-01,  1.37027026e+00],\n",
              "        [-1.14506636e+00, -6.01617601e-01],\n",
              "        [ 1.08570167e+00, -9.29783088e-01],\n",
              "        [-1.07412296e-01,  1.18497910e+00],\n",
              "        [ 1.66128608e+00,  1.59085862e+00],\n",
              "        [ 1.24379850e+00, -1.24186239e+00],\n",
              "        [-6.78200836e-02, -7.11586415e-01],\n",
              "        [ 3.53483649e-01, -1.04684497e+00],\n",
              "        [-6.40393302e-02, -2.09756863e-01],\n",
              "        [-5.10468330e-01,  7.41753970e-01],\n",
              "        [ 2.52094708e+00,  1.11913471e+00],\n",
              "        [ 4.82131302e-01, -7.66786885e-01],\n",
              "        [ 7.17487011e-01,  1.40977888e+00],\n",
              "        [-1.56985091e+00,  1.81721816e-01],\n",
              "        [-2.69641336e-01, -1.31882248e+00],\n",
              "        [ 1.53758454e+00,  4.26999405e-01],\n",
              "        [ 1.25803015e+00, -1.32034853e+00],\n",
              "        [-1.77364406e-01, -9.63714670e-01],\n",
              "        [-1.48038581e+00,  1.05573465e+00],\n",
              "        [-5.98009083e-02,  8.22694316e-01],\n",
              "        [-1.74711514e-01, -2.36960028e+00],\n",
              "        [ 5.35367865e-01,  1.19632359e+00],\n",
              "        [ 9.22557925e-01,  7.22022394e-01],\n",
              "        [ 5.31870945e-01,  6.12922734e-03],\n",
              "        [-1.42672828e+00, -1.68955108e+00],\n",
              "        [-6.53755185e-01,  1.22121044e+00],\n",
              "        [ 2.82678103e-01, -1.52199667e+00],\n",
              "        [-9.22451178e-01,  1.55938011e+00],\n",
              "        [-1.10963737e+00, -7.21441528e-01],\n",
              "        [ 3.60688993e-01, -1.41913281e+00],\n",
              "        [-1.92028794e-01,  6.18618137e-01],\n",
              "        [ 8.65776779e-01, -1.21762329e+00],\n",
              "        [-1.21620968e+00,  6.83049065e-01],\n",
              "        [-2.09114448e-01,  4.01341583e-01],\n",
              "        [-4.73362117e-01,  5.83668048e-01],\n",
              "        [-1.37496427e+00, -2.14360387e+00],\n",
              "        [-1.03777338e+00,  8.71249143e-01],\n",
              "        [-5.66103130e-02, -1.74011165e+00],\n",
              "        [-1.00421449e+00,  9.21219259e-01],\n",
              "        [-2.60818351e-02,  1.01596097e+00],\n",
              "        [-2.79335783e-01, -8.87909018e-01],\n",
              "        [ 1.80089979e-01,  8.68041199e-01],\n",
              "        [ 1.02194219e+00, -5.46483705e-01],\n",
              "        [ 4.69102326e-01,  1.00753548e+00],\n",
              "        [-7.36954920e-01, -1.99090996e+00],\n",
              "        [ 3.80551397e+00,  1.49137866e-01],\n",
              "        [-9.51855250e-01, -2.25623806e+00],\n",
              "        [ 4.51101916e-01,  1.05104320e+00],\n",
              "        [ 2.98144973e-01,  5.57058930e-01],\n",
              "        [ 1.45250817e+00, -1.98062295e+00],\n",
              "        [-1.60947813e+00,  1.00125605e+00],\n",
              "        [ 2.40553070e-01,  7.52439524e-01],\n",
              "        [ 4.70360399e-01,  1.05361224e+00],\n",
              "        [ 1.09315404e+00,  1.25350731e+00],\n",
              "        [-1.80086374e-01, -1.62109929e+00],\n",
              "        [-1.05234752e+00, -1.19191135e+00],\n",
              "        [ 6.91740501e-01,  9.32466520e-01],\n",
              "        [ 2.04669999e+00, -1.01775787e+00],\n",
              "        [-2.07979093e+00,  3.40368771e-01],\n",
              "        [-1.55859064e-01, -9.93383540e-01],\n",
              "        [ 2.74061755e-02, -1.06785991e+00],\n",
              "        [ 8.09768501e-01,  1.25234471e+00],\n",
              "        [-2.77688414e-01, -7.44748537e-01],\n",
              "        [-1.94083970e-01,  1.25165885e+00],\n",
              "        [ 2.68572445e-01,  9.19915554e-01],\n",
              "        [ 2.97069152e-01, -1.18341697e+00],\n",
              "        [-1.06101136e+00,  9.01626186e-01],\n",
              "        [ 1.27208381e-01,  1.33817514e+00],\n",
              "        [ 1.22280969e+00, -6.34011641e-01],\n",
              "        [ 1.65836601e-01,  1.04371696e+00],\n",
              "        [-1.09673065e-01, -9.38314921e-01],\n",
              "        [-8.19227913e-01,  1.13638010e+00],\n",
              "        [-1.40386913e+00, -1.02071773e+00],\n",
              "        [-1.23416151e+00,  1.19439315e+00],\n",
              "        [ 8.49637322e-01, -1.29314599e-01],\n",
              "        [ 9.04075035e-01, -4.19988897e-01],\n",
              "        [ 6.12599632e-01, -6.08357600e-01],\n",
              "        [-8.98607781e-01,  1.32135061e+00],\n",
              "        [-9.15863874e-01,  1.16368824e+00],\n",
              "        [ 2.02584111e+00,  4.63471587e-03],\n",
              "        [-1.45236904e+00,  4.70368522e-01],\n",
              "        [ 7.34556744e-01,  1.12729093e+00],\n",
              "        [ 2.69701928e-01, -3.44847643e-01],\n",
              "        [-2.06423656e+00, -1.49811482e+00],\n",
              "        [-7.02131498e-01, -6.29810247e-01],\n",
              "        [-2.66744646e-01,  1.24707267e+00],\n",
              "        [ 1.24118132e-01, -1.30177445e+00],\n",
              "        [ 3.38379307e-01,  1.31134144e+00],\n",
              "        [-8.19510632e-01,  9.16036256e-01],\n",
              "        [-1.59015462e+00,  7.44042740e-02],\n",
              "        [ 1.03489991e+00,  1.19767297e+00],\n",
              "        [ 8.70593920e-02,  1.16291413e+00],\n",
              "        [-1.64588447e+00, -1.79336505e+00],\n",
              "        [ 1.00844652e+00,  1.30543156e+00],\n",
              "        [ 4.96845017e-01,  8.59549327e-01],\n",
              "        [-2.62825570e+00, -1.17227297e+00],\n",
              "        [-3.98901569e-01, -1.37282347e+00],\n",
              "        [-9.66556680e-02,  1.17882331e+00],\n",
              "        [ 6.10165824e-01, -7.02758659e-01],\n",
              "        [-1.25241081e-01,  7.65571558e-01],\n",
              "        [ 3.01511975e-01,  6.02119507e-01],\n",
              "        [-2.69267911e-01, -2.13216450e-02],\n",
              "        [ 1.52537231e+00, -7.05544863e-01],\n",
              "        [-5.02895716e-01,  1.03914579e+00],\n",
              "        [-9.03762539e-01, -1.97830405e+00],\n",
              "        [ 2.50246022e-01,  1.16369383e+00],\n",
              "        [-7.32381388e-01, -3.16439954e-01],\n",
              "        [-1.07724068e+00, -9.62656450e-01],\n",
              "        [ 1.99083423e+00,  7.39232662e-01],\n",
              "        [ 4.61412128e-01,  1.62721884e+00],\n",
              "        [ 1.17643799e+00, -7.50069105e-02],\n",
              "        [ 5.93103644e-01,  1.20379038e+00],\n",
              "        [-8.76150821e-01,  9.38246134e-01],\n",
              "        [-1.14015107e+00,  6.75329607e-01],\n",
              "        [-1.02974722e+00,  8.08029008e-01],\n",
              "        [-1.55601351e-01, -4.15193470e-01],\n",
              "        [-6.50672262e-01,  1.22474875e+00],\n",
              "        [-2.80759883e-01, -6.54796310e-01],\n",
              "        [ 1.51944832e+00,  6.69433159e-01],\n",
              "        [-4.72235332e-02,  1.12898458e+00],\n",
              "        [-1.44035415e+00,  1.00674819e+00],\n",
              "        [ 8.29727566e-01, -1.71167084e+00],\n",
              "        [ 6.42911514e-01,  9.43444347e-01],\n",
              "        [-1.10907769e+00,  6.14530338e-01],\n",
              "        [ 4.81312605e-01,  9.71724691e-01],\n",
              "        [ 1.15960716e+00, -1.52652878e+00],\n",
              "        [-6.25363136e-01, -2.16919689e+00],\n",
              "        [ 1.10922347e+00, -1.68327194e+00],\n",
              "        [ 3.59507195e-01,  7.53390980e-01],\n",
              "        [ 1.10434286e+00, -1.11628861e+00],\n",
              "        [-6.24272716e-01,  1.25215523e+00],\n",
              "        [-5.14657260e-01,  1.25692322e+00],\n",
              "        [ 1.07186791e+00, -6.00332252e-01],\n",
              "        [-1.78366045e-01,  1.05159727e+00],\n",
              "        [-6.09169793e-01,  1.21864793e+00],\n",
              "        [ 1.61892082e-02,  7.50985464e-01],\n",
              "        [ 4.45047706e-01, -8.43962960e-01],\n",
              "        [-3.44555998e-01,  4.75899767e-03],\n",
              "        [-7.36329438e-01,  8.31344915e-01],\n",
              "        [ 3.76946605e-03, -2.19287900e+00],\n",
              "        [-5.76529670e-01, -9.03522018e-01],\n",
              "        [-1.54098934e+00, -1.19598653e+00],\n",
              "        [-5.01996284e-02,  9.92626678e-01],\n",
              "        [ 1.92377126e-01, -1.50177035e-01],\n",
              "        [ 1.44416641e+00,  3.36156387e-01],\n",
              "        [ 1.61815412e+00, -1.41098154e+00],\n",
              "        [-1.45586427e+00,  1.06759553e+00],\n",
              "        [ 9.89600289e-01,  1.26694159e+00],\n",
              "        [-4.93084898e-01,  7.83595885e-01],\n",
              "        [ 5.41193475e-01, -1.16136205e+00],\n",
              "        [-3.44366268e-01, -1.36434668e+00],\n",
              "        [ 6.82230917e-01,  1.43235646e+00],\n",
              "        [-2.19315340e-01,  1.31915105e+00],\n",
              "        [-9.45670383e-01,  2.91358459e-01],\n",
              "        [ 7.74967979e-01, -5.45248881e-02],\n",
              "        [ 6.65546340e-01, -4.87610678e-01],\n",
              "        [ 9.82494282e-01, -1.33938777e+00],\n",
              "        [ 1.17325348e-01, -1.47735832e+00],\n",
              "        [-2.81881752e-01, -8.84938507e-01],\n",
              "        [ 1.63713564e+00, -2.17432334e+00],\n",
              "        [ 7.87116585e-01, -5.48711063e-01],\n",
              "        [ 1.83689989e-01, -2.78607469e-01],\n",
              "        [-5.89184200e-01, -7.13968242e-01],\n",
              "        [ 6.06949123e-01, -1.92695095e+00],\n",
              "        [ 1.13615505e+00,  9.34159707e-01],\n",
              "        [-5.33350199e-01, -7.01342176e-01],\n",
              "        [-3.55639647e-02,  1.26847710e+00],\n",
              "        [ 1.25180445e+00, -1.31323403e+00],\n",
              "        [-4.83400521e-01,  5.84297393e-01],\n",
              "        [ 1.24289721e+00,  3.87016394e-01],\n",
              "        [ 7.76414305e-01, -7.84042128e-01],\n",
              "        [-6.11094774e-01, -1.75893850e+00],\n",
              "        [ 7.14038234e-01,  1.02249603e+00],\n",
              "        [-1.47834412e+00, -4.58217735e-01],\n",
              "        [ 3.43319427e-01, -1.58022728e+00],\n",
              "        [ 2.22795714e+00,  1.13783995e+00],\n",
              "        [-6.04300483e-01, -1.27031016e+00],\n",
              "        [-3.12358157e-01, -1.47095439e+00],\n",
              "        [-6.74971949e-01,  1.23686691e+00],\n",
              "        [ 1.43637773e+00,  8.88204900e-01],\n",
              "        [-5.09257270e-01,  1.47036827e+00],\n",
              "        [ 1.99009149e+00,  9.04658862e-01],\n",
              "        [-1.33650871e-01, -9.32036395e-01],\n",
              "        [ 3.39483309e-01, -1.53777198e+00],\n",
              "        [-1.10877497e+00,  1.14858995e+00],\n",
              "        [ 2.56817317e+00,  1.00347317e+00],\n",
              "        [ 7.10252376e-01,  1.10546805e+00],\n",
              "        [ 1.12891929e-01,  9.84968701e-01],\n",
              "        [ 4.74916742e-01, -1.14933559e+00],\n",
              "        [-5.62743687e-01,  1.05045366e+00],\n",
              "        [-4.21253088e-01, -8.18917818e-01],\n",
              "        [-2.48854241e+00,  6.00533278e-01],\n",
              "        [-1.10192400e-01, -1.62969750e+00],\n",
              "        [-1.17264210e+00, -2.10489761e+00],\n",
              "        [ 1.16818754e+00, -4.06042985e-01],\n",
              "        [-1.91870264e+00, -9.72408379e-01],\n",
              "        [-1.48656535e+00,  7.84957559e-01],\n",
              "        [ 1.49791013e+00, -9.05810242e-01],\n",
              "        [ 7.44323809e-01, -1.11690801e+00],\n",
              "        [-1.66460869e+00, -9.50342075e-01],\n",
              "        [-1.18822392e+00,  6.05652299e-01],\n",
              "        [-9.28369699e-01,  1.09835499e+00],\n",
              "        [ 1.51880196e-01, -1.13827324e+00],\n",
              "        [ 1.47028284e+00, -1.02889795e+00],\n",
              "        [ 1.30783537e+00,  8.72599493e-01],\n",
              "        [-8.60042024e-01,  6.82399393e-01],\n",
              "        [ 1.10961771e+00, -1.71291812e+00],\n",
              "        [ 1.03949361e+00, -9.53568987e-01],\n",
              "        [ 1.07484879e+00,  8.23433889e-01],\n",
              "        [ 3.31873096e-01,  1.16156475e+00],\n",
              "        [ 1.49376800e-01,  6.48015791e-01],\n",
              "        [-5.68201865e-01,  9.97233331e-01],\n",
              "        [-9.17862713e-01, -1.28247051e+00],\n",
              "        [ 4.17050342e-01,  1.06947274e+00],\n",
              "        [ 9.33550381e-01, -2.31296466e-01],\n",
              "        [ 7.82625350e-02,  7.77665351e-01],\n",
              "        [-5.62944237e-01, -1.81517397e+00],\n",
              "        [ 2.83963318e-01,  1.12920018e+00],\n",
              "        [ 5.10668351e-01,  1.43624362e+00],\n",
              "        [-1.34882284e+00, -2.41377262e+00],\n",
              "        [ 7.58980601e-01,  1.17926749e+00],\n",
              "        [ 4.10510520e-01, -5.62003310e-01],\n",
              "        [-1.29854547e+00, -1.78213954e+00],\n",
              "        [-8.59917131e-01, -7.74332383e-01],\n",
              "        [-1.24458639e+00,  7.30610549e-01],\n",
              "        [-7.59563291e-01, -9.90655407e-01],\n",
              "        [-1.55593397e+00,  1.54091974e+00],\n",
              "        [ 2.01124258e+00, -6.50151870e-01],\n",
              "        [ 1.84385841e+00, -1.19911559e+00],\n",
              "        [-1.15840553e+00,  1.15352059e+00],\n",
              "        [-5.97566850e-01, -2.36224337e-01],\n",
              "        [-4.22725606e-01, -2.40543697e+00],\n",
              "        [ 1.40697557e+00,  8.38761777e-01],\n",
              "        [-3.23073838e-01, -1.53094630e+00],\n",
              "        [-7.97818459e-01,  1.73665182e-01],\n",
              "        [-3.27830527e-01,  1.18556267e+00],\n",
              "        [ 2.89462527e-01,  8.06161465e-01],\n",
              "        [ 6.44335216e-01, -9.07492218e-01],\n",
              "        [ 1.34281872e+00, -1.98908865e+00],\n",
              "        [ 6.65228620e-01,  1.05274437e+00],\n",
              "        [-1.47211524e-01, -6.12836186e-01],\n",
              "        [-3.90186213e-01,  8.66811674e-01],\n",
              "        [ 1.26251243e+00, -1.33798222e+00],\n",
              "        [ 3.05757245e+00,  8.02533863e-02],\n",
              "        [ 8.71930256e-01,  9.44092465e-01],\n",
              "        [ 7.50266447e-01,  4.23964853e-01],\n",
              "        [ 2.18459391e+00,  9.08547003e-01],\n",
              "        [ 5.56595729e-01, -9.68298445e-01],\n",
              "        [-2.84390861e-01,  1.28516740e-01],\n",
              "        [-4.14832677e-01, -7.61685543e-01],\n",
              "        [ 6.13678993e-01, -1.17730862e+00],\n",
              "        [-1.93393136e+00,  1.03763746e+00],\n",
              "        [ 4.51483402e-01,  6.79791225e-01],\n",
              "        [-1.29008702e-01, -6.50680800e-01],\n",
              "        [ 4.20037118e-01,  1.03254396e+00],\n",
              "        [-9.19477385e-01, -8.83574263e-01],\n",
              "        [ 9.05983584e-01,  8.39840174e-01],\n",
              "        [-1.22825580e+00,  6.62205401e-01],\n",
              "        [ 3.19006021e-01,  7.05395797e-01],\n",
              "        [ 5.14712326e-01,  1.11414252e+00],\n",
              "        [ 5.05644320e-01, -1.31135357e+00],\n",
              "        [-6.21091558e-03, -1.53802337e+00],\n",
              "        [-9.26586783e-01,  2.04149817e+00],\n",
              "        [-9.10797208e-01, -1.51408134e+00],\n",
              "        [ 3.27494621e+00, -3.78799528e-01],\n",
              "        [-6.57027257e-01,  1.04703625e+00],\n",
              "        [ 4.04713428e-01,  1.39722625e+00],\n",
              "        [-1.56887718e+00,  1.58366785e+00],\n",
              "        [-7.65561520e-01,  1.13055983e+00],\n",
              "        [-6.38776473e-01, -1.49137041e+00],\n",
              "        [ 1.63904127e-01,  8.16453466e-01],\n",
              "        [-3.38696894e-01,  2.09506699e-01],\n",
              "        [-1.26693058e-01, -7.50666180e-01],\n",
              "        [-9.00916800e-01,  1.09612133e+00],\n",
              "        [ 1.34999576e+00,  5.94932219e-01],\n",
              "        [-1.91133900e-01, -1.35711833e+00],\n",
              "        [ 1.20849816e+00, -5.16103953e-01],\n",
              "        [-1.79255442e-01,  9.04111402e-01],\n",
              "        [ 1.62749494e+00, -1.17694401e+00],\n",
              "        [-1.11206353e+00, -1.69493671e+00],\n",
              "        [-1.40281568e+00, -9.50144300e-01],\n",
              "        [-7.93915442e-02,  5.93908225e-01],\n",
              "        [ 4.86250026e-01, -1.56351910e+00],\n",
              "        [-4.54727548e-01,  8.28009080e-01],\n",
              "        [-2.50691573e+00, -5.61748430e-01],\n",
              "        [-1.72375519e+00, -7.07488608e-01],\n",
              "        [ 6.97383772e-01, -2.26678389e+00],\n",
              "        [ 6.44314599e-01,  9.56992667e-01],\n",
              "        [ 7.70115634e-01,  7.80058089e-01],\n",
              "        [ 6.37641117e-01,  9.24521718e-01],\n",
              "        [-1.12435698e+00, -4.30035232e-01],\n",
              "        [ 2.70581984e-01,  8.32604236e-01],\n",
              "        [-1.47108290e+00,  9.47191900e-01],\n",
              "        [ 1.03397734e+00,  9.94625504e-01],\n",
              "        [-1.54337318e+00, -5.27867198e-01],\n",
              "        [-1.27959872e-01,  1.42780257e+00],\n",
              "        [-1.30422169e-01, -8.92018698e-01],\n",
              "        [-1.28541366e+00,  8.89537258e-01],\n",
              "        [-2.58962352e-01,  9.36627693e-01],\n",
              "        [ 2.97567420e-02, -1.70729600e+00],\n",
              "        [-9.89379259e-01,  1.43420494e+00],\n",
              "        [ 8.73755570e-01,  7.88411817e-01],\n",
              "        [-2.78095935e+00,  1.33528844e+00],\n",
              "        [ 1.34258818e+00,  9.03842338e-01],\n",
              "        [ 6.21830757e-01,  1.03110956e+00],\n",
              "        [ 4.82659146e-01, -1.18185819e+00],\n",
              "        [-8.83476976e-01,  1.09421850e+00],\n",
              "        [ 3.56311070e-01, -1.83422288e+00],\n",
              "        [-9.49084956e-01,  1.13001262e+00],\n",
              "        [-1.29386870e+00,  8.96543019e-01],\n",
              "        [-2.91308358e-01, -1.32297240e+00],\n",
              "        [-5.15713362e-01, -2.05668156e+00],\n",
              "        [-1.80173222e-01,  8.91152061e-01],\n",
              "        [-6.46615721e-02,  7.29392228e-01],\n",
              "        [ 1.02885868e+00,  1.03520193e+00],\n",
              "        [-2.43518124e+00, -1.33467556e+00],\n",
              "        [-8.88840612e-01,  9.57599953e-01],\n",
              "        [-6.74156049e-01,  8.03037965e-01],\n",
              "        [-6.36989768e-01,  8.95222556e-01],\n",
              "        [-3.92251702e-01, -5.99675561e-01],\n",
              "        [-4.61139539e-01, -7.41682510e-01],\n",
              "        [ 3.59454556e-02, -4.94537729e-01],\n",
              "        [ 1.25901523e+00,  1.11166169e+00],\n",
              "        [ 1.55592533e+00,  9.77496170e-01],\n",
              "        [ 8.37403896e-01,  7.07465258e-01],\n",
              "        [-9.67562269e-02, -1.04634812e+00],\n",
              "        [ 1.98167116e-01,  1.19834281e+00],\n",
              "        [ 3.70332689e-01, -2.35719846e+00],\n",
              "        [-8.72351116e-01,  6.33199395e-01],\n",
              "        [ 2.27937491e+00, -5.31454264e-01],\n",
              "        [-1.61810048e-01,  1.17513901e+00],\n",
              "        [-8.55522699e-01, -4.78157321e-01],\n",
              "        [ 7.74771870e-01, -1.12994088e+00],\n",
              "        [ 4.42338898e-01, -7.90399307e-01],\n",
              "        [-2.21627523e+00, -1.43020809e+00],\n",
              "        [-9.97925058e-01,  7.41648174e-01],\n",
              "        [-1.00235028e+00, -7.98731516e-01],\n",
              "        [ 7.26888772e-01,  1.01551074e+00],\n",
              "        [-1.00263655e+00,  6.72766167e-01],\n",
              "        [-9.70534473e-01,  1.13151763e+00],\n",
              "        [-6.37157920e-01, -4.27618581e-01],\n",
              "        [-5.12638604e-01, -1.66997181e+00],\n",
              "        [-1.67530495e+00,  1.03949695e+00],\n",
              "        [-1.18857664e+00,  1.33689522e+00],\n",
              "        [ 1.50096157e+00, -1.67580194e+00],\n",
              "        [ 1.43609074e+00,  5.39197880e-01],\n",
              "        [-7.19309633e-01, -9.10266737e-01],\n",
              "        [-7.03010715e-01, -8.36794524e-01],\n",
              "        [ 6.27158849e-02,  1.13288371e+00],\n",
              "        [-1.89499918e-01,  8.55965739e-01],\n",
              "        [-1.05960887e+00,  1.27313945e+00],\n",
              "        [-1.24643954e+00,  1.49863868e-01],\n",
              "        [-2.94792699e-01, -6.86119747e-01],\n",
              "        [ 7.24363038e-01, -3.74497572e-01],\n",
              "        [-1.74344996e+00,  9.97776869e-01],\n",
              "        [ 1.55798065e-01,  9.80926148e-01],\n",
              "        [ 2.97793904e-01,  9.73662817e-01],\n",
              "        [ 1.32115979e+00,  1.08700327e+00],\n",
              "        [ 1.65294623e+00, -1.25610734e+00],\n",
              "        [-8.71517413e-02, -1.67901807e+00],\n",
              "        [ 1.64094544e+00, -7.78994384e-01],\n",
              "        [-3.73398803e-02, -1.92251617e+00],\n",
              "        [ 1.05421974e+00, -1.32045158e+00],\n",
              "        [ 5.25962696e-01, -2.00777506e+00],\n",
              "        [ 3.17664339e-01,  1.14073734e+00],\n",
              "        [ 1.15606866e-01,  6.01664637e-01],\n",
              "        [ 5.75533547e-01, -1.77035812e+00],\n",
              "        [-1.14914412e-01, -1.66416399e-01],\n",
              "        [ 2.33610938e-02, -1.84518665e+00],\n",
              "        [-1.60869714e+00,  1.04500028e+00],\n",
              "        [-1.69731484e-02, -1.73171939e+00]]),\n",
              " array([0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1.,\n",
              "        1., 1., 1., 1., 0., 0., 0., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1.,\n",
              "        0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0., 1.,\n",
              "        0., 0., 0., 1., 0., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
              "        1., 0., 1., 0., 1., 0., 0., 0., 1., 1., 0., 1., 1., 1., 1., 0., 0.,\n",
              "        1., 0., 0., 0., 0., 1., 0., 1., 1., 0., 1., 1., 0., 1., 0., 1., 0.,\n",
              "        1., 0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 0., 1., 0., 1., 1., 0.,\n",
              "        1., 1., 0., 1., 1., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 1., 0.,\n",
              "        0., 1., 1., 0., 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 0., 1., 1.,\n",
              "        1., 0., 0., 0., 1., 0., 1., 1., 0., 1., 1., 1., 0., 0., 1., 0., 0.,\n",
              "        0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
              "        1., 0., 0., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1.,\n",
              "        0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0., 0., 1.,\n",
              "        1., 1., 1., 0., 1., 0., 1., 0., 1., 1., 0., 1., 0., 0., 0., 1., 0.,\n",
              "        1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 1., 0., 0., 1., 0., 1., 0.,\n",
              "        0., 1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 0., 1., 1., 1., 1.,\n",
              "        0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 0., 0., 1., 1., 0., 0.,\n",
              "        1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 1., 1., 0., 1., 1., 1.,\n",
              "        0., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 0.,\n",
              "        0., 1., 1., 1., 0., 1., 1., 1., 0., 0., 0., 1., 1., 1., 0., 1., 0.,\n",
              "        1., 0., 1., 0., 0., 0., 0., 1., 0., 1., 1., 1., 0., 0., 1., 1., 0.,\n",
              "        1., 0., 0., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 1., 1., 0., 0., 0., 1., 0.]),\n",
              " <keras.engine.sequential.Sequential at 0x7f595c2a9e48>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "vU-OBrdHiemz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "df9f3dc6-80df-457b-a44d-5a2b3c74af46"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"4\", archs(2)[2], 10, split=0.25, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 4\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "\n",
            "Loss on validation set:0.11974764256888872 Accuracy on validation set: 0.9475\n",
            "\n",
            "Loss on validation set:0.1238727893908289 Accuracy on validation set: 0.9525\n",
            "\n",
            "Loss on validation set:0.12106918001426628 Accuracy on validation set: 0.9525\n",
            "\n",
            "Loss on validation set:0.12560683763137664 Accuracy on validation set: 0.945\n",
            "\n",
            "Loss on validation set:0.1268986032070671 Accuracy on validation set: 0.95\n",
            "\n",
            "Avg. validation accuracy:0.9494999999999999\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9FSRiPrbjpAE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1170
        },
        "outputId": "c21731e1-9061-4c97-a155-aae08b87c10e"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"4\", archs(2)[1], 10, split=0.25, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 4\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "\n",
            "Loss on validation set:0.12641835761191714 Accuracy on validation set: 0.9525\n",
            "\n",
            "Loss on validation set:0.1255026402005899 Accuracy on validation set: 0.9575\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-8adc3e635528>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_keras_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marchs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/content/code_for_hw8_keras.py\u001b[0m in \u001b[0;36mrun_keras_2d\u001b[0;34m(data_name, layers, epochs, display, split, verbose, trials)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvacc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtacc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m                run_keras(X_train, y_train, X_val, y_val, X_test, y_test, layers, epochs,\n\u001b[0;32m--> 150\u001b[0;31m                          split=split, verbose=verbose)\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0mval_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mtest_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/code_for_hw8_keras.py\u001b[0m in \u001b[0;36mrun_keras\u001b[0;34m(X_train, y_train, X_val, y_val, X_test, y_test, layers, epochs, split, verbose)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         model.fit(X_train, y_train, epochs=epochs, batch_size=batch, validation_data=(X_val, y_val),\n\u001b[0;32m--> 103\u001b[0;31m                   callbacks=[history], verbose=verbose)\n\u001b[0m\u001b[1;32m    104\u001b[0m     \u001b[0;31m# Evaluate the model on validation data, if any\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX_val\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msplit\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "wx2DorR4j6DI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1170
        },
        "outputId": "5e35014d-07de-4d27-bc33-fb4160941c8a"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"4\", archs(2)[0], 10, split=0.25, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 4\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "\n",
            "Loss on validation set:0.6934201847016811 Accuracy on validation set: 0.505\n",
            "\n",
            "Loss on validation set:0.6936828118562698 Accuracy on validation set: 0.48\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-6039b5d44d91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_keras_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marchs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/content/code_for_hw8_keras.py\u001b[0m in \u001b[0;36mrun_keras_2d\u001b[0;34m(data_name, layers, epochs, display, split, verbose, trials)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvacc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtacc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m                run_keras(X_train, y_train, X_val, y_val, X_test, y_test, layers, epochs,\n\u001b[0;32m--> 150\u001b[0;31m                          split=split, verbose=verbose)\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0mval_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mtest_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/code_for_hw8_keras.py\u001b[0m in \u001b[0;36mrun_keras\u001b[0;34m(X_train, y_train, X_val, y_val, X_test, y_test, layers, epochs, split, verbose)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         model.fit(X_train, y_train, epochs=epochs, batch_size=batch, validation_data=(X_val, y_val),\n\u001b[0;32m--> 103\u001b[0;31m                   callbacks=[history], verbose=verbose)\n\u001b[0m\u001b[1;32m    104\u001b[0m     \u001b[0;31m# Evaluate the model on validation data, if any\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX_val\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msplit\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "-MJnN7oSkRX1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "9665ffa4-9226-44bd-f5d2-ff3851a7caa6"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3\", archs(2)[1], 10, split=0.25, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (200, 2) y (200,) classes {0.0, 1.0}\n",
            "\n",
            "Loss on validation set:0.10492607745535189 Accuracy on validation set: 0.975\n",
            "\n",
            "Loss on validation set:0.10413181638906935 Accuracy on validation set: 0.975\n",
            "\n",
            "Loss on validation set:0.1029467943843649 Accuracy on validation set: 0.97\n",
            "\n",
            "Loss on validation set:0.10477239789319653 Accuracy on validation set: 0.97\n",
            "\n",
            "Loss on validation set:0.10651358334413544 Accuracy on validation set: 0.965\n",
            "\n",
            "Avg. validation accuracy:0.9709999999999999\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "C5Twe02Aksvr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4628
        },
        "outputId": "781210cd-89a8-4080-b804-b59f4d77ce04"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3\", [Dense(input_dim=2, units=200, activation='relu'),\\\n",
        "             Dense(units=200, activation='relu'),\\\n",
        "             Dense(units=2, activation=\"softmax\")], 100, split=0.25, display=True, verbose=True, trials=1)\n",
        "\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (400, 2) y (400,) classes {0.0, 1.0}\n",
            "Convert from -1,1 to 0,1\n",
            "Loading X (200, 2) y (200,) classes {0.0, 1.0}\n",
            "Train on 400 samples, validate on 200 samples\n",
            "Epoch 1/100\n",
            "400/400 [==============================] - 7s 18ms/step - loss: 0.1211 - acc: 0.9700 - val_loss: 0.1131 - val_acc: 0.9500\n",
            "Epoch 2/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0677 - acc: 0.9800 - val_loss: 0.1264 - val_acc: 0.9650\n",
            "Epoch 3/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0647 - acc: 0.9750 - val_loss: 0.1157 - val_acc: 0.9700\n",
            "Epoch 4/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0570 - acc: 0.9825 - val_loss: 0.1264 - val_acc: 0.9600\n",
            "Epoch 5/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0661 - acc: 0.9775 - val_loss: 0.1230 - val_acc: 0.9650\n",
            "Epoch 6/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0549 - acc: 0.9800 - val_loss: 0.1157 - val_acc: 0.9700\n",
            "Epoch 7/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0544 - acc: 0.9775 - val_loss: 0.1347 - val_acc: 0.9650\n",
            "Epoch 8/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0541 - acc: 0.9800 - val_loss: 0.1314 - val_acc: 0.9700\n",
            "Epoch 9/100\n",
            "400/400 [==============================] - 3s 7ms/step - loss: 0.0548 - acc: 0.9825 - val_loss: 0.1299 - val_acc: 0.9700\n",
            "Epoch 10/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0531 - acc: 0.9850 - val_loss: 0.1412 - val_acc: 0.9700\n",
            "Epoch 11/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0547 - acc: 0.9850 - val_loss: 0.1143 - val_acc: 0.9700\n",
            "Epoch 12/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0507 - acc: 0.9875 - val_loss: 0.1252 - val_acc: 0.9650\n",
            "Epoch 13/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0471 - acc: 0.9825 - val_loss: 0.1294 - val_acc: 0.9700\n",
            "Epoch 14/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0483 - acc: 0.9800 - val_loss: 0.1329 - val_acc: 0.9650\n",
            "Epoch 15/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0474 - acc: 0.9750 - val_loss: 0.1425 - val_acc: 0.9700\n",
            "Epoch 16/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0490 - acc: 0.9800 - val_loss: 0.1139 - val_acc: 0.9650\n",
            "Epoch 17/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0520 - acc: 0.9850 - val_loss: 0.1428 - val_acc: 0.9650\n",
            "Epoch 18/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0475 - acc: 0.9850 - val_loss: 0.1467 - val_acc: 0.9700\n",
            "Epoch 19/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0471 - acc: 0.9825 - val_loss: 0.1365 - val_acc: 0.9650\n",
            "Epoch 20/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0446 - acc: 0.9850 - val_loss: 0.1609 - val_acc: 0.9500\n",
            "Epoch 21/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0462 - acc: 0.9825 - val_loss: 0.1326 - val_acc: 0.9700\n",
            "Epoch 22/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0431 - acc: 0.9850 - val_loss: 0.1525 - val_acc: 0.9650\n",
            "Epoch 23/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0428 - acc: 0.9875 - val_loss: 0.1795 - val_acc: 0.9700\n",
            "Epoch 24/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0509 - acc: 0.9825 - val_loss: 0.1213 - val_acc: 0.9700\n",
            "Epoch 25/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0524 - acc: 0.9875 - val_loss: 0.1371 - val_acc: 0.9700\n",
            "Epoch 26/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0408 - acc: 0.9875 - val_loss: 0.1599 - val_acc: 0.9700\n",
            "Epoch 27/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0477 - acc: 0.9850 - val_loss: 0.1469 - val_acc: 0.9700\n",
            "Epoch 28/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0444 - acc: 0.9825 - val_loss: 0.1362 - val_acc: 0.9650\n",
            "Epoch 29/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0454 - acc: 0.9850 - val_loss: 0.1639 - val_acc: 0.9650\n",
            "Epoch 30/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0536 - acc: 0.9800 - val_loss: 0.1630 - val_acc: 0.9750\n",
            "Epoch 31/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0530 - acc: 0.9850 - val_loss: 0.1675 - val_acc: 0.9700\n",
            "Epoch 32/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0432 - acc: 0.9825 - val_loss: 0.1513 - val_acc: 0.9700\n",
            "Epoch 33/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0406 - acc: 0.9875 - val_loss: 0.1720 - val_acc: 0.9650\n",
            "Epoch 34/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0445 - acc: 0.9850 - val_loss: 0.1471 - val_acc: 0.9750\n",
            "Epoch 35/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0516 - acc: 0.9825 - val_loss: 0.1561 - val_acc: 0.9700\n",
            "Epoch 36/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0445 - acc: 0.9850 - val_loss: 0.1886 - val_acc: 0.9700\n",
            "Epoch 37/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0438 - acc: 0.9850 - val_loss: 0.1671 - val_acc: 0.9700\n",
            "Epoch 38/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0430 - acc: 0.9850 - val_loss: 0.1775 - val_acc: 0.9700\n",
            "Epoch 39/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0497 - acc: 0.9800 - val_loss: 0.1396 - val_acc: 0.9750\n",
            "Epoch 40/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0450 - acc: 0.9825 - val_loss: 0.1765 - val_acc: 0.9700\n",
            "Epoch 41/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0379 - acc: 0.9875 - val_loss: 0.1845 - val_acc: 0.9700\n",
            "Epoch 42/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0398 - acc: 0.9800 - val_loss: 0.1780 - val_acc: 0.9700\n",
            "Epoch 43/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0379 - acc: 0.9900 - val_loss: 0.1888 - val_acc: 0.9700\n",
            "Epoch 44/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0390 - acc: 0.9850 - val_loss: 0.2023 - val_acc: 0.9750\n",
            "Epoch 45/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0806 - acc: 0.9850 - val_loss: 0.3342 - val_acc: 0.9450\n",
            "Epoch 46/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0630 - acc: 0.9875 - val_loss: 0.2061 - val_acc: 0.9750\n",
            "Epoch 47/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0512 - acc: 0.9775 - val_loss: 0.1608 - val_acc: 0.9700\n",
            "Epoch 48/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0400 - acc: 0.9875 - val_loss: 0.1986 - val_acc: 0.9750\n",
            "Epoch 49/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0410 - acc: 0.9900 - val_loss: 0.2105 - val_acc: 0.9750\n",
            "Epoch 50/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0382 - acc: 0.9850 - val_loss: 0.1866 - val_acc: 0.9700\n",
            "Epoch 51/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0342 - acc: 0.9900 - val_loss: 0.2264 - val_acc: 0.9700\n",
            "Epoch 52/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0439 - acc: 0.9850 - val_loss: 0.1936 - val_acc: 0.9700\n",
            "Epoch 53/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0416 - acc: 0.9850 - val_loss: 0.1706 - val_acc: 0.9700\n",
            "Epoch 54/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0455 - acc: 0.9900 - val_loss: 0.1924 - val_acc: 0.9700\n",
            "Epoch 55/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0438 - acc: 0.9825 - val_loss: 0.1939 - val_acc: 0.9700\n",
            "Epoch 56/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0393 - acc: 0.9900 - val_loss: 0.2029 - val_acc: 0.9700\n",
            "Epoch 57/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0462 - acc: 0.9850 - val_loss: 0.1801 - val_acc: 0.9650\n",
            "Epoch 58/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0446 - acc: 0.9875 - val_loss: 0.2379 - val_acc: 0.9650\n",
            "Epoch 59/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0686 - acc: 0.9875 - val_loss: 0.1918 - val_acc: 0.9700\n",
            "Epoch 60/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0411 - acc: 0.9850 - val_loss: 0.1944 - val_acc: 0.9700\n",
            "Epoch 61/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0385 - acc: 0.9875 - val_loss: 0.2031 - val_acc: 0.9700\n",
            "Epoch 62/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0360 - acc: 0.9875 - val_loss: 0.2288 - val_acc: 0.9750\n",
            "Epoch 63/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0378 - acc: 0.9900 - val_loss: 0.2100 - val_acc: 0.9700\n",
            "Epoch 64/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0371 - acc: 0.9900 - val_loss: 0.2160 - val_acc: 0.9700\n",
            "Epoch 65/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0464 - acc: 0.9800 - val_loss: 0.1701 - val_acc: 0.9700\n",
            "Epoch 66/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0469 - acc: 0.9800 - val_loss: 0.1918 - val_acc: 0.9700\n",
            "Epoch 67/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0429 - acc: 0.9825 - val_loss: 0.1992 - val_acc: 0.9700\n",
            "Epoch 68/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0369 - acc: 0.9900 - val_loss: 0.2172 - val_acc: 0.9700\n",
            "Epoch 69/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0375 - acc: 0.9900 - val_loss: 0.2083 - val_acc: 0.9700\n",
            "Epoch 70/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0316 - acc: 0.9825 - val_loss: 0.3389 - val_acc: 0.9500\n",
            "Epoch 71/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0942 - acc: 0.9850 - val_loss: 0.2236 - val_acc: 0.9700\n",
            "Epoch 72/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0380 - acc: 0.9875 - val_loss: 0.2252 - val_acc: 0.9700\n",
            "Epoch 73/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0380 - acc: 0.9900 - val_loss: 0.2022 - val_acc: 0.9700\n",
            "Epoch 74/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0396 - acc: 0.9875 - val_loss: 0.1944 - val_acc: 0.9700\n",
            "Epoch 75/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0396 - acc: 0.9850 - val_loss: 0.2099 - val_acc: 0.9750\n",
            "Epoch 76/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0354 - acc: 0.9850 - val_loss: 0.1995 - val_acc: 0.9700\n",
            "Epoch 77/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0362 - acc: 0.9900 - val_loss: 0.2114 - val_acc: 0.9700\n",
            "Epoch 78/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0418 - acc: 0.9850 - val_loss: 0.2045 - val_acc: 0.9750\n",
            "Epoch 79/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0374 - acc: 0.9875 - val_loss: 0.2207 - val_acc: 0.9700\n",
            "Epoch 80/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0530 - acc: 0.9825 - val_loss: 0.2217 - val_acc: 0.9700\n",
            "Epoch 81/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0420 - acc: 0.9850 - val_loss: 0.2373 - val_acc: 0.9700\n",
            "Epoch 82/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0539 - acc: 0.9875 - val_loss: 0.1933 - val_acc: 0.9750\n",
            "Epoch 83/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0398 - acc: 0.9875 - val_loss: 0.1900 - val_acc: 0.9700\n",
            "Epoch 84/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0350 - acc: 0.9900 - val_loss: 0.2147 - val_acc: 0.9750\n",
            "Epoch 85/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0512 - acc: 0.9825 - val_loss: 0.1967 - val_acc: 0.9750\n",
            "Epoch 86/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0349 - acc: 0.9900 - val_loss: 0.2088 - val_acc: 0.9700\n",
            "Epoch 87/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0345 - acc: 0.9900 - val_loss: 0.2239 - val_acc: 0.9750\n",
            "Epoch 88/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0350 - acc: 0.9875 - val_loss: 0.2512 - val_acc: 0.9750\n",
            "Epoch 89/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0379 - acc: 0.9875 - val_loss: 0.2057 - val_acc: 0.9700\n",
            "Epoch 90/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0353 - acc: 0.9875 - val_loss: 0.2129 - val_acc: 0.9700\n",
            "Epoch 91/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0340 - acc: 0.9900 - val_loss: 0.2211 - val_acc: 0.9700\n",
            "Epoch 92/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0331 - acc: 0.9900 - val_loss: 0.2471 - val_acc: 0.9700\n",
            "Epoch 93/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0450 - acc: 0.9875 - val_loss: 0.1876 - val_acc: 0.9650\n",
            "Epoch 94/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0424 - acc: 0.9825 - val_loss: 0.1974 - val_acc: 0.9650\n",
            "Epoch 95/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0391 - acc: 0.9850 - val_loss: 0.2306 - val_acc: 0.9700\n",
            "Epoch 96/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0428 - acc: 0.9825 - val_loss: 0.2470 - val_acc: 0.9750\n",
            "Epoch 97/100\n",
            "400/400 [==============================] - 3s 8ms/step - loss: 0.0385 - acc: 0.9900 - val_loss: 0.2000 - val_acc: 0.9650\n",
            "Epoch 98/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0375 - acc: 0.9850 - val_loss: 0.2292 - val_acc: 0.9700\n",
            "Epoch 99/100\n",
            "400/400 [==============================] - 4s 9ms/step - loss: 0.0351 - acc: 0.9900 - val_loss: 0.2365 - val_acc: 0.9700\n",
            "Epoch 100/100\n",
            "400/400 [==============================] - 3s 9ms/step - loss: 0.0354 - acc: 0.9875 - val_loss: 0.2263 - val_acc: 0.9700\n",
            "\n",
            "Loss on validation set:0.22629417419262218 Accuracy on validation set: 0.97\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc4AAAFDCAYAAACzyO62AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUCgqgAqcCoVCoVBUABU4FQqFQqGoAP8PZoxrkSIVQ7gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhDpBEARBlAkk1AmCIAiiTPj/yG+XFBTyyokAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Avg. validation accuracy:0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JkOF8-vnsilo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "6d27ba5d-6921-41f0-c3b4-e9add9d3b8de"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3class\",archs(3)[0], 10, split=0.5, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3class\n",
            "Loading X (800, 2) y (800,) classes {0.0, 1.0, 2.0}\n",
            "\n",
            "Loss on validation set:0.37580104152453714 Accuracy on validation set: 0.8975\n",
            "\n",
            "Loss on validation set:0.3980204207263887 Accuracy on validation set: 0.8925\n",
            "\n",
            "Loss on validation set:0.3947797438420821 Accuracy on validation set: 0.89\n",
            "\n",
            "Loss on validation set:0.38497665359871464 Accuracy on validation set: 0.895\n",
            "\n",
            "Loss on validation set:0.43127132682595404 Accuracy on validation set: 0.8825\n",
            "\n",
            "Avg. validation accuracy:0.8915000000000001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fcxeXu6stKQ7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "c8ebf48a-9317-4204-b5ae-619715acd4cb"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3class\",archs(3)[1], 10, split=0.5, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3class\n",
            "Loading X (800, 2) y (800,) classes {0.0, 1.0, 2.0}\n",
            "\n",
            "Loss on validation set:0.19425822995934594 Accuracy on validation set: 0.93\n",
            "\n",
            "Loss on validation set:0.18095577861745638 Accuracy on validation set: 0.9425\n",
            "\n",
            "Loss on validation set:0.1841980099960631 Accuracy on validation set: 0.94\n",
            "\n",
            "Loss on validation set:0.18057322938599554 Accuracy on validation set: 0.9325\n",
            "\n",
            "Loss on validation set:0.1741165961263575 Accuracy on validation set: 0.9475\n",
            "\n",
            "Avg. validation accuracy:0.9385\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "turfJJUWtKrq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "9d0cbb17-50cf-445c-b496-d9913d15396d"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3class\",archs(3)[2], 10, split=0.5, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3class\n",
            "Loading X (800, 2) y (800,) classes {0.0, 1.0, 2.0}\n",
            "\n",
            "Loss on validation set:0.1440884950258268 Accuracy on validation set: 0.9575\n",
            "\n",
            "Loss on validation set:0.15587048970753137 Accuracy on validation set: 0.9475\n",
            "\n",
            "Loss on validation set:0.15045232152040244 Accuracy on validation set: 0.9475\n",
            "\n",
            "Loss on validation set:0.1533177834802728 Accuracy on validation set: 0.945\n",
            "\n",
            "Loss on validation set:0.14883993604469434 Accuracy on validation set: 0.9525\n",
            "\n",
            "Avg. validation accuracy:0.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "k3GcqN7jtKvv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "e3e2c038-7ace-418a-b03a-d40aa322edb8"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3class\",archs(3)[3], 10, split=0.5, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3class\n",
            "Loading X (800, 2) y (800,) classes {0.0, 1.0, 2.0}\n",
            "\n",
            "Loss on validation set:0.142916172365197 Accuracy on validation set: 0.9575\n",
            "\n",
            "Loss on validation set:0.13673068349145695 Accuracy on validation set: 0.9525\n",
            "\n",
            "Loss on validation set:0.13630020045745384 Accuracy on validation set: 0.9575\n",
            "\n",
            "Loss on validation set:0.14101611332239244 Accuracy on validation set: 0.96\n",
            "\n",
            "Loss on validation set:0.14030548646441987 Accuracy on validation set: 0.955\n",
            "\n",
            "Avg. validation accuracy:0.9564999999999999\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "T1VEU2xptKy-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "263bc596-73e4-408c-f65b-cf4ba30de878"
      },
      "cell_type": "code",
      "source": [
        "X_train, y, model=run_keras_2d(\"3class\",archs(3)[4], 10, split=0.5, display=False, verbose=False, trials=5)\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3class\n",
            "Loading X (800, 2) y (800,) classes {0.0, 1.0, 2.0}\n",
            "\n",
            "Loss on validation set:0.20895454745500308 Accuracy on validation set: 0.9425\n",
            "\n",
            "Loss on validation set:0.1793179938930818 Accuracy on validation set: 0.9525\n",
            "\n",
            "Loss on validation set:0.20870531987995267 Accuracy on validation set: 0.9525\n",
            "\n",
            "Loss on validation set:0.1976152191465852 Accuracy on validation set: 0.955\n",
            "\n",
            "Loss on validation set:0.2039231977829474 Accuracy on validation set: 0.9525\n",
            "\n",
            "Avg. validation accuracy:0.951\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vN96c03xtK3e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1553
        },
        "outputId": "5dde2bd7-b7e4-408f-e479-3ce4ac72cfca"
      },
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "layer=archs(3)[0]\n",
        "X_train, y, model=run_keras_2d(\"3class\", layer, 10, display=True, split=0.25, verbose=True, trials=1)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keras FC: dataset= 3class\n",
            "Loading X (800, 2) y (800,) classes {0.0, 1.0, 2.0}\n",
            "Train on 600 samples, validate on 200 samples\n",
            "Epoch 1/10\n",
            "600/600 [==============================] - 8s 13ms/step - loss: 1.9456 - acc: 0.0350 - val_loss: 1.5078 - val_acc: 0.0350\n",
            "Epoch 2/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 1.2304 - acc: 0.1667 - val_loss: 0.9616 - val_acc: 0.3850\n",
            "Epoch 3/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.8367 - acc: 0.8050 - val_loss: 0.6907 - val_acc: 0.9000\n",
            "Epoch 4/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.6379 - acc: 0.9017 - val_loss: 0.5484 - val_acc: 0.9300\n",
            "Epoch 5/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.5286 - acc: 0.9100 - val_loss: 0.4639 - val_acc: 0.9300\n",
            "Epoch 6/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.4626 - acc: 0.9100 - val_loss: 0.4089 - val_acc: 0.9250\n",
            "Epoch 7/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.4199 - acc: 0.9083 - val_loss: 0.3731 - val_acc: 0.9250\n",
            "Epoch 8/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.3911 - acc: 0.9083 - val_loss: 0.3485 - val_acc: 0.9250\n",
            "Epoch 9/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.3714 - acc: 0.9067 - val_loss: 0.3315 - val_acc: 0.9250\n",
            "Epoch 10/10\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.3577 - acc: 0.9100 - val_loss: 0.3193 - val_acc: 0.9250\n",
            "\n",
            "Loss on validation set:0.31927799158147535 Accuracy on validation set: 0.925\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAFnCAYAAABXZmVyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPnYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niYiIisz/A862pqrewX+2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY3hLklQwhrckSQVjeEuSVDCfmxx3dkvmtqMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Avg. validation accuracy:0.925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "My2yPR_TtK7M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "weight=model.layers[0].get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SCk1bHj32DlJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c936fe09-5377-40a3-cd4b-a6b43c1add0c"
      },
      "cell_type": "code",
      "source": [
        "z=[[-1,0], [1,0], [0,-11], [0,1], [-1,-1], [-1,1], [1,1], [1,-1]]\n",
        "for zi in z:\n",
        "  predict=\n",
        "weight[0].shape"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "nPMKADq9213h",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction=model.predict(z)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rEBqe3K-3Z4T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7937323a-3323-4a5a-b625-c17bde102fcd"
      },
      "cell_type": "code",
      "source": [
        "prediction.shape"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "XrEE753r3V80",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "df0e6293-16ca-4215-8069-ca7683a18a29"
      },
      "cell_type": "code",
      "source": [
        "np.argmax(prediction,axis=1)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 0, 2, 0, 2, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "LCJ_8IkU2mky",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "024c23a2-5d6b-4485-9794-446cc45eba21"
      },
      "cell_type": "code",
      "source": [
        "z=np.array([[-1,0], [1,0], [0,-11], [0,1], [-1,-1], [-1,1], [1,1], [1,-1]])\n",
        "weight[0].T@z[0]+weight[1]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.57703805, -1.52869733,  0.79963088])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "Zh4u39OCjLza",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Weight sharing (OPTIONAL)\n",
        "\n",
        "** Note: You can click the arrow on the left of this text block to collapse/expand this optional section and all its code blocks **\n",
        "\n",
        "In the lab we designed a CNN that can count the number of objects in 1 dimensional images, where each black pixel is represented by a value of 0 and each white pixel is represented by a value of 1. Recall that an object is a consecutive sequence of black pixels ($0$'s). For example, the sequence $0100110$ contains three objects. \n",
        "\n",
        "Here we want to see how hard/easy it is to train such a network from data.  Our network architecture will be as follows: \n",
        "\n",
        "* The first layer is convolutional and you will implement it using the Keras `Conv1D` function, with a kernel of size 2 and stride of 1 with ReLu activation.\n",
        "\n",
        "* The second layer is a fully connected `Dense` layer which has a scalar output. \n",
        "\n",
        "Here is sample usage of the `Conv1D` and`Dense` layers. \n",
        "\n",
        "`layer1=keras.layers.Conv1D(filters=?, kernel_size=?, strides=?,use_bias=False, activation=?, batch_size=1, input_shape=?, padding='same')`\n",
        "\n",
        "`Dense(units=?, activation=?, use_bias=False)`\n",
        "\n",
        "You need to fill in the parameters marked with `?` based on the problem specifications. Note also that in Keras, depending on your implementation, you may be forced to use *three* layers to implement such a network, where one intermediary `Flatten` layer is used to flatten the output of the convolutional layer, before being passed to the dense layer.\n",
        "\n",
        "Refer to the <a href=\"https://keras.io/layers/convolutional/\">Conv 1D</a>, <a href=\"https://keras.io/layers/core/\">Dense</a> and <a href=\"https://keras.io/layers/core/#flatten\">Flatten</a> descriptions in the Keras documentation to see the available parameter options.\n",
        "\n",
        "In this exercise, we fix the structure and want to learn the best combination of weights from data. In the homework code, we have provided functions `train_neural_counter` and `get_image_data_1d`. You can use them to generate data and train the above neural network in Keras to answer the following questions. We assume that the images in our data set are randomly generated. The probability of a pixel being white is $0.1$. We work with mean squared error as the loss function for this problem. We have provided template code which you can fill in, to perform the training."
      ]
    },
    {
      "metadata": {
        "id": "Bz-TGSRI2C4b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yrcXA596yWek",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4A)</b> We call a $2\\times 1$ filter an *edge detector* if it has weights $[-1,1]$ or $[1,-1]$ (i.e., it can detect a left or right edge in a given image). We call it an *averaging filter* if takes average of nearby pixels. \n",
        "\n",
        "Assuming that the second layer in our network is the sum function (i.e., all of its weights are equal to 1), which of the following filters gives a (nearly) local minima? You may assume that we are working with $1024\\times 1$ images. \n",
        "\n",
        "(Although you can answer the question without any coding, you may also empirically verify your claims. The function `set_weights` that can be applied to keras layers might be useful.)"
      ]
    },
    {
      "metadata": {
        "id": "wKa8iMv_j3ek",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4B)</b> What is (approximately) the expected loss of the network on $1024\\times 1$ images if the convolutional layer is an averaging filter and second layer is the sum function (without a bias term)? (Note that you can answer the question theoretically or through coding, depending on your preference.)"
      ]
    },
    {
      "metadata": {
        "id": "IKxedp-qFXJe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oKPcB588ok8a",
        "colab_type": "code",
        "outputId": "2fb81c30-7ceb-43bf-b9a0-68e7d5f1f189",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "# Code template if you would like to check 4B) through code\n",
        "\n",
        "imsize = 1024\n",
        "prob_white = 0.1\n",
        "\n",
        "num_filters = None  # Your code\n",
        "kernel_size = None  # Your code\n",
        "strides = None  # Your code\n",
        "activation_conv = None  # Your code\n",
        "\n",
        "(X_train,Y_train,X_val,Y_val,X_test,Y_test) = get_image_data_1d(1000,imsize,prob_white)\n",
        "\n",
        "layer1=keras.layers.Conv1D(filters=num_filters, kernel_size=kernel_size, \\\n",
        "       strides=strides, use_bias=False, activation=activation_conv, batch_size=1, input_shape=(imsize,1), padding='same')\n",
        "\n",
        "activation_dense = None  # Your code\n",
        "num_units = None  # Your code\n",
        "layer3=Dense(units=num_units, activation=activation_dense, use_bias=False)\n",
        "\n",
        "layers=[layer1,Flatten(),layer3]\n",
        "\n",
        "# This is how we create the model using our layers\n",
        "model=Sequential()\n",
        "for layer in layers:\n",
        "    model.add(layer)\n",
        "        \n",
        "model.compile(loss='mse', optimizer=Adam()) \n",
        "\n",
        "# Set the weights of the layers to desired values\n",
        "# We give you the lines to use for this part\n",
        "model.layers[0].set_weights([np.array([1/2,1/2]).reshape(2,1,1)])\n",
        "model.layers[-1].set_weights([np.ones(imsize).reshape(imsize,1)])\n",
        "\n",
        "model.evaluate(X_test,Y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "1000/1000 [==============================] - 0s 164us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "102.36525"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "js3OYsbwj7Ms",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4C)</b> Now suppose we add a bias term of $-10$ to the last layer. What is (approximately) the expected quadratic loss? (Note that you can answer the question theoretically or through coding, depending on your preference.)"
      ]
    },
    {
      "metadata": {
        "id": "AKynxhF1klga",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Edit code from 4B) with the bias"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3GCLr8qmj-Hk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4D)</b> Averaging type filters are abundant and form a nearly flat valley of local minima for this problem. It is difficult for the network to find alternative solutions on its own. We need to force our way out of these bad minima and towards a better solution, i.e., an edge detector. To force the first layer to behave as an edge detector, we need to choose a proper **kernel regularizer**. Consider the following functions\n",
        "\n",
        "$f_1=\\sum_i |w_i|$, $f_2=\\sum_i |w_i^2|$, $f_3=|\\sum_{i} w_i|$. Which one of the choices is likely to guide the network to find an edge detector at the convolution layer?\n",
        "\n",
        "\n",
        "<a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>"
      ]
    },
    {
      "metadata": {
        "id": "5aubU6Q6kwOI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Implement your choice of regularizers from above in the code (complete the function `filter_reg`). Do not allow any bias in the layers for the rest of the problem. The code generates some random test and training data sets and trains the model on these data. Run a few learning trials (5 or more) for each data set and answer the following questions based on the performance of your model.\n",
        "\n",
        "**IMPORTANT**: When implementing `filter_reg`, you should use the keras backend operations, imported as \"K\" in the code. So for example, `K.sum` and `K.abs`, rather than `np.sum` and `np.abs`. This is because the `weights` argument is NOT a numpy object, but rather an internal Keras object!"
      ]
    },
    {
      "metadata": {
        "id": "yOLZf_JsuTLn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Implement filter_reg\n",
        "\n",
        "def filter_reg(weights):\n",
        "    # We scale the output of the filter by lam\n",
        "    lam=1000\n",
        "    filter_result = None  # Your code\n",
        "    return lam * filter_result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pYRwd0eJkAdh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4E)</b> For $1024\\times 1$ images and training set of size $1000$, is the network **without any regularization** likely to find models that have a mean square error lower than 8 on the test data?"
      ]
    },
    {
      "metadata": {
        "id": "1KiCbZmksXO6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Code template if you would like to check 4B) through code\n",
        "\n",
        "imsize = 1024\n",
        "prob_white = 0.1\n",
        "\n",
        "data=get_image_data_1d(1000, imsize, prob_white)\n",
        "trials=5\n",
        "for trial in range(trials):\n",
        "  \n",
        "    num_filters = None  # Your code\n",
        "    kernel_size = None  # Your code\n",
        "    strides = None  # Your code\n",
        "    activation_conv = None  # Your code\n",
        "\n",
        "    layer1=keras.layers.Conv1D(filters=num_filters, kernel_size=kernel_size, \\\n",
        "    strides=strides, use_bias=False, activation=activation_conv, batch_size=1, \\\n",
        "    input_shape=(imsize,1),padding='same')\n",
        "    \n",
        "    activation_dense = None  # Your code\n",
        "    num_units = None  # Your code\n",
        "\n",
        "    layer3=Dense(units=num_units, activation=activation_dense, use_bias=False)\n",
        "    \n",
        "    layers=[layer1,Flatten(),layer3]\n",
        "    model,err = train_neural_counter(layers, data, 'mse')\n",
        "    \n",
        "    model.layers[0].get_weights()[0]\n",
        "    np.mean(model.layers[-1].get_weights()[0])\n",
        "    print(err)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g1vcUEL-vW9D",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### For parts F) to J), simply edit your code from E) with the necessary changes."
      ]
    },
    {
      "metadata": {
        "id": "w_25ygQJkD5F",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4F)</b> Repeat the same experiment, but now with the regularizer you implemented. Try different regularization parameters. Which choice of regularization parameter gives the best prediction results?"
      ]
    },
    {
      "metadata": {
        "id": "vNAChIqylIlt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Edit code from 4E), using your filter as the kernel_regularizer in the Conv1D layer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Rs44ze96kHZZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4G)</b> With the above choice of regularization parameter, what is the mean square error of the best network that you find on the test data? Try a few trials (5 or more) for each data test and report the value of the best network. \n"
      ]
    },
    {
      "metadata": {
        "id": "IAN0k9wylOmz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### We expect the training to be easier when there are fewer parameters to learn. Consider images of size $128\\times 1$ for the rest of the problem."
      ]
    },
    {
      "metadata": {
        "id": "ZnktFwXRkKNF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4H)</b> Instead of resorting to regularization again, we may instead find a way to reduce the number of parameters. What additional layer can you add to the output of the convolution layer to reduce the number of parameters to be learned without losing any relevant information?\n",
        "\n",
        "<a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>"
      ]
    },
    {
      "metadata": {
        "id": "zXgOqKtRkNRP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4I)</b> Add the layer you suggested above to your network and run some tests with data sets of size 1000 on $128\\times 1$ images.  How many parameters are left to learn with the new structure?"
      ]
    },
    {
      "metadata": {
        "id": "e8FRQawHkPG9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b>4J)</b> Mark your observations on the two structures (not using regularization).\n",
        "<a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>"
      ]
    },
    {
      "metadata": {
        "id": "I-iTDCrHySde",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "eQGlJLxI__4A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 5) MNIST (Digit Classification)\n",
        "\n",
        "In this section, we'll be looking at the MNIST data set seen already in problem 2. This time, we look at the *complete* MNIST problem where our networks will take an image of *any* digit from $0-9$ as input (recall that problem 2 only looked at digits $0$ and $1$) and try to predict that digit. Also, we will now use out-of-the-box neural network implementations using Keras and Tensorflow. State-of-the-art systems have error rates of less that one half of one percent on this data set (see <a href=\"http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#4d4e495354\">this list</a>).  We'll be happy with an error rate less than 2% since we don't have all year...\n",
        "<br>\n",
        "\n",
        "You can access the MNIST data for this problem using:\n",
        "<br><code>train, validation = get_MNIST_data()</code>\n",
        "<br>\n",
        "\n",
        "You can run the fully connected MNIST model, using:\n",
        "<br><code>run_keras_fc_mnist(train, validation, layers, epochs, split=0.1, trials=5)</code>\n",
        "<br>\n",
        "\n",
        "And, you can run the CNN MNIST test, using:\n",
        "<br><code>run_keras_cnn_mnist(train, validation, layers, epochs, split=0.1, trials=5)</code>\n",
        "<br>\n",
        "\n",
        "For all following experiments, please run for 5 trials (use `trials=5`) and report the average accuracy.\n",
        "<br>\n",
        "\n",
        "A word of warning, if you have a machine with a single core and/or very little RAM, you'll be better off running on an Athena workstation. If your solutions are not being accepted, and you are confident in your approach, try with more trials. Also, \n",
        "<br>\n",
        "\n",
        "You will need to design your own `layers` to feed to `run_keras_fc_mnist` and `run_keras_cnn_mnist`, which will be different than the ones specified by `archs()`. For instance, `layers=[Dense(input_dim=64, units=4, activation=\"softmax\")]` defines a single layer with 64 inputs, 4 output units, and softmax activation. Also, we advise you to use the option `verbose=True` when unsure about the progress made during training of your models.\n",
        "<br>"
      ]
    },
    {
      "metadata": {
        "id": "dx1jt6P9AUk1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5A)</b> Look at the code and indicate what the difference is between <code>run_keras_fc_mnist</code> and <code>run_keras_cnn_mnist</code>? <a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>"
      ]
    },
    {
      "metadata": {
        "id": "Q5moSfb7CcXd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def run_keras_fc_mnist(train, test, layers, epochs, split=0.1, verbose=True, trials=1):\n",
        "    (X_train, y1), (X_val, y2) = train, test\n",
        "    # Flatten the images\n",
        "    m = X_train.shape[1]\n",
        "    X_train = X_train.reshape((X_train.shape[0], m*m))\n",
        "    X_val = X_val.reshape((X_val.shape[0], m*m))\n",
        "    # Categorize the labels\n",
        "    num_classes = 10\n",
        "    y_train = np_utils.to_categorical(y1, num_classes)\n",
        "    y_val = np_utils.to_categorical(y2, num_classes)\n",
        "    # Train, use split for validation\n",
        "    val_acc, test_acc = 0, 0\n",
        "    for trial in range(trials):\n",
        "        # Reset the weights\n",
        "        # See https://github.com/keras-team/keras/issues/341\n",
        "        session = K.get_session()\n",
        "        for layer in layers:\n",
        "            for v in layer.__dict__:\n",
        "                v_arg = getattr(layer, v)\n",
        "                if hasattr(v_arg, 'initializer'):\n",
        "                    initializer_func = getattr(v_arg, 'initializer')\n",
        "                    initializer_func.run(session=session)\n",
        "        # Run the model\n",
        "        model, history, vacc, tacc = \\\n",
        "                run_keras(X_train, y_train, X_val, y_val, None, None, layers, epochs, split=split, verbose=verbose)\n",
        "        val_acc += vacc if vacc else 0\n",
        "        test_acc += tacc if tacc else 0\n",
        "    if val_acc:\n",
        "        print (\"\\nAvg. validation accuracy:\"  + str(val_acc/trials))\n",
        "    if test_acc:\n",
        "        print (\"\\nAvg. test accuracy:\"  + str(test_acc/trials))\n",
        "\n",
        "def run_keras_cnn_mnist(train, test, layers, epochs, split=0.1, verbose=True, trials=1):\n",
        "    # Load the dataset\n",
        "    (X_train, y1), (X_val, y2) = train, test\n",
        "    # Add a final dimension indicating the number of channels (only 1 here)\n",
        "    m = X_train.shape[1]\n",
        "    X_train = X_train.reshape((X_train.shape[0], m, m, 1))\n",
        "    X_val = X_val.reshape((X_val.shape[0], m, m, 1))\n",
        "    # Categorize the labels\n",
        "    num_classes = 10\n",
        "    y_train = np_utils.to_categorical(y1, num_classes)\n",
        "    y_val = np_utils.to_categorical(y2, num_classes)\n",
        "    # Train, use split for validation\n",
        "    val_acc, test_acc = 0, 0\n",
        "    for trial in range(trials):\n",
        "        # Reset the weights\n",
        "        # See https://github.com/keras-team/keras/issues/341\n",
        "        session = K.get_session()\n",
        "        for layer in layers:\n",
        "            for v in layer.__dict__:\n",
        "                v_arg = getattr(layer, v)\n",
        "                if hasattr(v_arg, 'initializer'):\n",
        "                    initializer_func = getattr(v_arg, 'initializer')\n",
        "                    initializer_func.run(session=session)\n",
        "        # Run the model\n",
        "        model, history, vacc, tacc = \\\n",
        "                run_keras(X_train, y_train, X_val, y_val, None, None, layers, epochs, split=split, verbose=verbose)\n",
        "        val_acc += vacc if vacc else 0\n",
        "        test_acc += tacc if tacc else 0\n",
        "    if val_acc:\n",
        "        print (\"\\nAvg. validation accuracy:\"  + str(val_acc/trials))\n",
        "    if test_acc:\n",
        "        print (\"\\nAvg. test accuracy:\"  + str(test_acc/trials))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4sGfqAbICbmE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5B)</b> Using one epoch of training, what is the accuracy of a network **with no hidden units** (using the <code>run_keras_fc_mnist</code> method) on this data? Hint: this is expected to be terrible. If it's still not working, run for more trials. Remember to use 10 output units (the network predicts a digit from 0-9) and softmax activation!\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "H1VAxZ17DtPQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "outputId": "a3d7c721-10bf-483b-886b-2c4163b151a3"
      },
      "cell_type": "code",
      "source": [
        "layers = [Dense(input_dim=784, units=10, activation=\"softmax\")]  # Your code\n",
        "train, validation = get_MNIST_data()\n",
        "run_keras_fc_mnist(train, validation, layers, 1, split=0.1, verbose=True, trials=10)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 220us/step - loss: 7.3552 - acc: 0.5378 - val_loss: 6.2711 - val_acc: 0.6081\n",
            "\n",
            "Loss on validation set:6.271083640289307 Accuracy on validation set: 0.6081\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 217us/step - loss: 8.5004 - acc: 0.4671 - val_loss: 7.5803 - val_acc: 0.5270\n",
            "\n",
            "Loss on validation set:7.580250917053223 Accuracy on validation set: 0.527\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 223us/step - loss: 6.7111 - acc: 0.5786 - val_loss: 5.6738 - val_acc: 0.6447\n",
            "\n",
            "Loss on validation set:5.673832307052613 Accuracy on validation set: 0.6447\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 221us/step - loss: 10.6255 - acc: 0.3378 - val_loss: 10.3084 - val_acc: 0.3579\n",
            "\n",
            "Loss on validation set:10.308383099365235 Accuracy on validation set: 0.3579\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 222us/step - loss: 7.5906 - acc: 0.5243 - val_loss: 6.1305 - val_acc: 0.6171\n",
            "\n",
            "Loss on validation set:6.1304636009216305 Accuracy on validation set: 0.6171\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 224us/step - loss: 8.8001 - acc: 0.4507 - val_loss: 6.3978 - val_acc: 0.6005\n",
            "\n",
            "Loss on validation set:6.397847781372071 Accuracy on validation set: 0.6005\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 224us/step - loss: 8.5205 - acc: 0.4664 - val_loss: 7.2486 - val_acc: 0.5482\n",
            "\n",
            "Loss on validation set:7.24859765625 Accuracy on validation set: 0.5482\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 13s 223us/step - loss: 10.2398 - acc: 0.3606 - val_loss: 9.3751 - val_acc: 0.4155\n",
            "\n",
            "Loss on validation set:9.37509881439209 Accuracy on validation set: 0.4155\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 14s 229us/step - loss: 5.7586 - acc: 0.6364 - val_loss: 4.5275 - val_acc: 0.7152\n",
            "\n",
            "Loss on validation set:4.527505477905273 Accuracy on validation set: 0.7152\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 14s 226us/step - loss: 8.7586 - acc: 0.4530 - val_loss: 6.7414 - val_acc: 0.5768\n",
            "\n",
            "Loss on validation set:6.74144854888916 Accuracy on validation set: 0.5768\n",
            "\n",
            "Avg. validation accuracy:0.5611\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5Gndf-ASDEe_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5C)</b> When creating the keras layer, pass in the following argument to Dense:\n",
        "<code>kernel_initializer=VarianceScaling(scale=0.001, mode='fan_in', distribution='normal', seed=None)</code> and repeat the test.  What is the accuracy now?\n"
      ]
    },
    {
      "metadata": {
        "id": "x2CP5WJQELJA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "outputId": "59c22986-5ae3-4278-fd1f-36a3bb5f3ac1"
      },
      "cell_type": "code",
      "source": [
        "layers = [Dense(input_dim=784, units=10, activation=\"softmax\",\\\n",
        "          kernel_initializer=VarianceScaling(scale=0.001, mode='fan_in', distribution='normal', seed=None))]  # Your code\n",
        "train, validation = get_MNIST_data()\n",
        "run_keras_fc_mnist(train, validation, layers, 1, split=0.1, verbose=True, trials=10)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 252us/step - loss: 6.8316 - acc: 0.5686 - val_loss: 5.7266 - val_acc: 0.6418\n",
            "\n",
            "Loss on validation set:5.726605661010742 Accuracy on validation set: 0.6418\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 246us/step - loss: 3.6564 - acc: 0.7598 - val_loss: 3.3125 - val_acc: 0.7870\n",
            "\n",
            "Loss on validation set:3.312474801635742 Accuracy on validation set: 0.787\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 244us/step - loss: 6.0079 - acc: 0.6177 - val_loss: 5.8303 - val_acc: 0.6344\n",
            "\n",
            "Loss on validation set:5.830264462280273 Accuracy on validation set: 0.6344\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 244us/step - loss: 4.6595 - acc: 0.6998 - val_loss: 4.2099 - val_acc: 0.7346\n",
            "\n",
            "Loss on validation set:4.209945277023316 Accuracy on validation set: 0.7346\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 242us/step - loss: 4.2092 - acc: 0.7283 - val_loss: 4.0051 - val_acc: 0.7462\n",
            "\n",
            "Loss on validation set:4.005124110031128 Accuracy on validation set: 0.7462\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 243us/step - loss: 4.7293 - acc: 0.6958 - val_loss: 4.6994 - val_acc: 0.7014\n",
            "\n",
            "Loss on validation set:4.699429852294922 Accuracy on validation set: 0.7014\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 244us/step - loss: 5.7748 - acc: 0.6306 - val_loss: 5.5131 - val_acc: 0.6528\n",
            "\n",
            "Loss on validation set:5.513072610473633 Accuracy on validation set: 0.6528\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 243us/step - loss: 5.8902 - acc: 0.6249 - val_loss: 5.7349 - val_acc: 0.6405\n",
            "\n",
            "Loss on validation set:5.734931440734863 Accuracy on validation set: 0.6405\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 246us/step - loss: 5.8625 - acc: 0.6255 - val_loss: 5.5704 - val_acc: 0.6498\n",
            "\n",
            "Loss on validation set:5.570393853759765 Accuracy on validation set: 0.6498\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 247us/step - loss: 6.1446 - acc: 0.6081 - val_loss: 5.8963 - val_acc: 0.6300\n",
            "\n",
            "Loss on validation set:5.896321369552612 Accuracy on validation set: 0.63\n",
            "\n",
            "Avg. validation accuracy:0.6818500000000001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xsQ31e1lDE6u",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5D)</b> Now, linearly scale the data so that the pixel values are between 0 and 1 and repeat your test with the original layer (no VarianceScaling). What is the accuracy now?"
      ]
    },
    {
      "metadata": {
        "id": "5b-RcZu1EPaj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "outputId": "0e31ea1d-d21f-4559-a4c5-39027d8c1c34"
      },
      "cell_type": "code",
      "source": [
        "layers = [Dense(input_dim=784, units=10, activation=\"softmax\")]  # Your code\n",
        "train, validation = get_MNIST_data()\n",
        "\n",
        "# Scale the images\n",
        "train2 =(train[0]/255.,train[1])  # Your code\n",
        "validation2 = (validation[0]/255,validation[1])  # Your code\n",
        "\n",
        "run_keras_fc_mnist(train2, validation2, layers, 1, split=0.1, verbose=True, trials=10)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 265us/step - loss: 0.4667 - acc: 0.8793 - val_loss: 0.3078 - val_acc: 0.9151\n",
            "\n",
            "Loss on validation set:0.3077592942535877 Accuracy on validation set: 0.9151\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 15s 256us/step - loss: 0.4690 - acc: 0.8778 - val_loss: 0.3173 - val_acc: 0.9107\n",
            "\n",
            "Loss on validation set:0.31729373716712 Accuracy on validation set: 0.9107\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 261us/step - loss: 0.4679 - acc: 0.8768 - val_loss: 0.3081 - val_acc: 0.9136\n",
            "\n",
            "Loss on validation set:0.3080778224647045 Accuracy on validation set: 0.9136\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 260us/step - loss: 0.4746 - acc: 0.8745 - val_loss: 0.3104 - val_acc: 0.9138\n",
            "\n",
            "Loss on validation set:0.3103947720170021 Accuracy on validation set: 0.9138\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 263us/step - loss: 0.4652 - acc: 0.8782 - val_loss: 0.3088 - val_acc: 0.9155\n",
            "\n",
            "Loss on validation set:0.30883601899147034 Accuracy on validation set: 0.9155\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 263us/step - loss: 0.4702 - acc: 0.8778 - val_loss: 0.3073 - val_acc: 0.9150\n",
            "\n",
            "Loss on validation set:0.30729789862036705 Accuracy on validation set: 0.915\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 271us/step - loss: 0.4699 - acc: 0.8768 - val_loss: 0.3086 - val_acc: 0.9165\n",
            "\n",
            "Loss on validation set:0.3086392650783062 Accuracy on validation set: 0.9165\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 264us/step - loss: 0.4714 - acc: 0.8757 - val_loss: 0.3100 - val_acc: 0.9160\n",
            "\n",
            "Loss on validation set:0.3099584287703037 Accuracy on validation set: 0.916\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 265us/step - loss: 0.4701 - acc: 0.8776 - val_loss: 0.3070 - val_acc: 0.9158\n",
            "\n",
            "Loss on validation set:0.30702034830451014 Accuracy on validation set: 0.9158\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 16s 266us/step - loss: 0.4687 - acc: 0.8770 - val_loss: 0.3088 - val_acc: 0.9151\n",
            "\n",
            "Loss on validation set:0.3088168408870697 Accuracy on validation set: 0.9151\n",
            "\n",
            "Avg. validation accuracy:0.91471\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6Hs0_pgSFHfn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1445
        },
        "outputId": "fb41fc07-4262-4d93-fecd-cb266835af59"
      },
      "cell_type": "code",
      "source": [
        "train2[0][1]"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.51, 1.59, 2.53, 1.59, 0.5 , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.48, 2.38, 2.52, 2.52, 2.52, 2.37, 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.54, 2.27, 2.53, 2.52, 2.39, 2.33, 2.52, 0.57, 0.06,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.1 , 0.6 , 2.24, 2.52, 2.53, 2.52, 2.02, 0.84, 2.52, 2.53, 1.22,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        1.63, 2.52, 2.52, 2.52, 2.53, 2.52, 2.52, 0.96, 1.89, 2.53, 1.67,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.51,\n",
              "        2.38, 2.53, 2.53, 1.9 , 1.14, 2.53, 2.28, 0.47, 0.79, 2.55, 1.68,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.48, 2.38,\n",
              "        2.52, 2.52, 1.79, 0.12, 0.75, 1.21, 0.21, 0.  , 0.  , 2.53, 2.43,\n",
              "        0.5 , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.38, 1.65, 2.53,\n",
              "        2.33, 2.08, 0.84, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 2.53, 2.52,\n",
              "        1.65, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.07, 1.78, 2.52, 2.4 ,\n",
              "        0.71, 0.19, 0.28, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 2.53, 2.52,\n",
              "        1.95, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.57, 2.52, 2.52, 0.63,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 2.53, 2.52,\n",
              "        1.95, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 1.98, 2.53, 1.9 , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 2.55, 2.53,\n",
              "        1.96, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.76, 2.46, 2.52, 1.12, 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 2.53, 2.52,\n",
              "        1.48, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.85, 2.52, 2.3 , 0.25, 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.07, 1.35, 2.53, 1.86,\n",
              "        0.12, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.85, 2.52, 2.23, 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.07, 1.31, 2.52, 2.25, 0.71,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.85, 2.52, 1.45, 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.48, 1.65, 2.52, 1.73, 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.86, 2.53, 2.25, 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 1.14, 2.38, 2.53, 1.62, 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.85, 2.52, 2.49, 1.46, 0.48,\n",
              "        0.29, 0.85, 1.78, 2.25, 2.53, 2.23, 1.67, 0.56, 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.85, 2.52, 2.52, 2.52, 2.29,\n",
              "        2.15, 2.52, 2.52, 2.52, 1.96, 1.3 , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.28, 1.99, 2.52, 2.52, 2.53,\n",
              "        2.52, 2.52, 2.33, 1.45, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.25, 1.28, 2.52, 2.53,\n",
              "        2.52, 1.41, 0.37, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
              "        0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "metadata": {
        "id": "S4aBsxAzDFBY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5E)</b> What is happening? <a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>\n"
      ]
    },
    {
      "metadata": {
        "id": "KrYGfcOLEr0f",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Important: <b>Always scale the data like in 5D) for subsequent problems.</b>\n"
      ]
    },
    {
      "metadata": {
        "id": "mHoyqJdqDFH5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5F)</b> Using this same architecture, evaluate validation accuracy for number of training epochs in [5, 10, 15]."
      ]
    },
    {
      "metadata": {
        "id": "q8PlbWS_EwTv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "c9065cc2-85c3-4fd9-9c53-c560f22defda"
      },
      "cell_type": "code",
      "source": [
        "train, validation = get_MNIST_data()\n",
        "\n",
        "# Scale the images\n",
        "train2 =(train[0]/255.,train[1])  # Your code\n",
        "validation2 = (validation[0]/255,validation[1])  # Your code\n",
        "\n",
        "for epochs in [5,10,15]:\n",
        "    layers = [Dense(input_dim=784, units=10, activation=\"softmax\")]  # Your code\n",
        "    run_keras_fc_mnist(train2, validation2, layers, epochs, split=0.1, verbose=False, trials=5)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Loss on validation set:0.2697895421296358 Accuracy on validation set: 0.9252\n",
            "\n",
            "Loss on validation set:0.27193705541193486 Accuracy on validation set: 0.9257\n",
            "\n",
            "Loss on validation set:0.26653761529624465 Accuracy on validation set: 0.9264\n",
            "\n",
            "Loss on validation set:0.2669355450749397 Accuracy on validation set: 0.926\n",
            "\n",
            "Loss on validation set:0.2646467423021793 Accuracy on validation set: 0.9265\n",
            "\n",
            "Avg. validation accuracy:0.9259600000000001\n",
            "\n",
            "Loss on validation set:0.2661864712908864 Accuracy on validation set: 0.9272\n",
            "\n",
            "Loss on validation set:0.26212519440054893 Accuracy on validation set: 0.9276\n",
            "\n",
            "Loss on validation set:0.2663315407693386 Accuracy on validation set: 0.9258\n",
            "\n",
            "Loss on validation set:0.2689012592703104 Accuracy on validation set: 0.9267\n",
            "\n",
            "Loss on validation set:0.26332459056675434 Accuracy on validation set: 0.9291\n",
            "\n",
            "Avg. validation accuracy:0.92728\n",
            "\n",
            "Loss on validation set:0.2670408170968294 Accuracy on validation set: 0.9272\n",
            "\n",
            "Loss on validation set:0.2674353663176298 Accuracy on validation set: 0.9266\n",
            "\n",
            "Loss on validation set:0.26600145495980976 Accuracy on validation set: 0.9294\n",
            "\n",
            "Loss on validation set:0.2661422142326832 Accuracy on validation set: 0.9268\n",
            "\n",
            "Loss on validation set:0.26547270493209363 Accuracy on validation set: 0.9286\n",
            "\n",
            "Avg. validation accuracy:0.9277200000000001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6zJk4u2-DFNi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5G)</b> \n",
        "\n",
        "<b> 5G a)</b>With the validation accuracy that you just saw on per digit basis using $15$ epochs, and assuming each digit is read independently from the others, what is the probability of reading a 5 digit zip code correctly?<br>\n",
        "\n",
        "<b> 5G b)</b>Now, assume that the accuracy is 0.9985, what is the probability of reading a zip code correctly?<br>\n",
        "b.\n",
        "\n",
        "This is why people care about dropping the error rates to what at first sound like ridiculous values.\n",
        "\n",
        "<a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>"
      ]
    },
    {
      "metadata": {
        "id": "vCPTuz-tDFTg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5H)</b> Using one epoch of training, try a single hidden layer with ReLU and gradually increase the units (128, 256, 512, 1024) units.  What are the accuracies?"
      ]
    },
    {
      "metadata": {
        "id": "bmpbP_VHFoh1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1680
        },
        "outputId": "6f3fdeb6-9c19-4445-ae6d-48dd531b50bd"
      },
      "cell_type": "code",
      "source": [
        "train, validation = get_MNIST_data()\n",
        "\n",
        "# Scale the images\n",
        "train2 =(train[0]/255.,train[1])  # Your code\n",
        "validation2 = (validation[0]/255,validation[1])  # Your code\n",
        "\n",
        "\n",
        "for num in [128,256,512,1024]:\n",
        "    layers = [Dense(input_dim=784, units=num, activation='relu'),\\\n",
        "             Dense(units=10, activation=\"softmax\")]  # Your code\n",
        "    run_keras_fc_mnist(train2, validation2, layers, 1, split=0.1, verbose=True, trials=5)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-115-b423c1d2bbaa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1024\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mlayers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m784\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m             \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"softmax\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# Your code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mrun_keras_fc_mnist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-54-3bee48562ca5>\u001b[0m in \u001b[0;36mrun_keras_fc_mnist\u001b[0;34m(train, test, layers, epochs, split, verbose, trials)\u001b[0m\n\u001b[1;32m     22\u001b[0m                     \u001b[0minitializer_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m# Run the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvacc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;34m=\u001b[0m                 \u001b[0mrun_keras\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0mval_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mtest_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/code_for_hw8_keras.py\u001b[0m in \u001b[0;36mrun_keras\u001b[0;34m(X_train, y_train, X_val, y_val, X_test, y_test, layers, epochs, split, verbose)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         model.fit(X_train, y_train, epochs=epochs, batch_size=batch, validation_data=(X_val, y_val),\n\u001b[0;32m--> 103\u001b[0;31m                   callbacks=[history], verbose=verbose)\n\u001b[0m\u001b[1;32m    104\u001b[0m     \u001b[0;31m# Evaluate the model on validation data, if any\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX_val\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msplit\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2696\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2697\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_make_callable_from_options'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2698\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_sparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2699\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mget_session\u001b[0;34m()\u001b[0m\n\u001b[1;32m    197\u001b[0m                 \u001b[0;31m# not already marked as initialized.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 is_initialized = session.run(\n\u001b[0;32m--> 199\u001b[0;31m                     [tf.is_variable_initialized(v) for v in candidate_vars])\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0muninitialized_vars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_initialized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcandidate_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    927\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 929\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    930\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1150\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1152\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1153\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1328\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1329\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1332\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1335\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1336\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       \u001b[0;31m# Ensure any changes to the graph are reflected in the runtime.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1317\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m   1319\u001b[0m           options, feed_dict, fetch_list, target_list, run_metadata)\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_extend_graph\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1350\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1351\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session_run_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1352\u001b[0;31m       \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExtendSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1354\u001b[0m   \u001b[0;31m# The threshold to run garbage collection to delete dead tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "gEM5mZi5DFYS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5I)</b> Now, try a network with two layers, again using one epoch, with 512 units in the first hidden layer and and 256 units in the second hidden layer.  What is the accuracy?"
      ]
    },
    {
      "metadata": {
        "id": "6cwp1VR7F06q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "d910b7c6-32e1-486a-eea7-be4d139fc186"
      },
      "cell_type": "code",
      "source": [
        "train, validation = get_MNIST_data()\n",
        "\n",
        "# Scale the images\n",
        "train2 =(train[0]/255.,train[1])  # Your code\n",
        "validation2 = (validation[0]/255,validation[1])  # Your code\n",
        "\n",
        "layers = [Dense(input_dim=784, units=512, activation='relu'),\\\n",
        "             Dense(units=256, activation='relu'),\\\n",
        "             Dense(units=10, activation=\"softmax\")]  # Your code\n",
        "run_keras_fc_mnist(train2, validation2, layers, 1, split=0.1, verbose=False, trials=5)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Loss on validation set:0.10872747318260371 Accuracy on validation set: 0.9663\n",
            "\n",
            "Loss on validation set:0.09849569575996138 Accuracy on validation set: 0.9697\n",
            "\n",
            "Loss on validation set:0.08107373610874638 Accuracy on validation set: 0.9738\n",
            "\n",
            "Loss on validation set:0.09131545753749087 Accuracy on validation set: 0.9719\n",
            "\n",
            "Loss on validation set:0.1170585597563535 Accuracy on validation set: 0.9626\n",
            "\n",
            "Avg. validation accuracy:0.9688599999999999\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TmnNUT2nDFdi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5J)</b> Build a convolutional network with the following structure:\n",
        "\n",
        "<ul>\n",
        "<li> A convolutional layer with 32 filters of size 3 × 3, with a ReLU activation\n",
        "<li> A max pooling layer with size 2 × 2\n",
        "<li> A convolutional layer with 64 filters of size 3 × 3, with ReLU activation\n",
        "<li> A max pooling layer with size 2 × 2\n",
        "<li> A flatten layer\n",
        "<li> A fully connected layer with 128 neurons, with ReLU activation\n",
        "<li> A dropout layer with drop probability 0.5\n",
        "<li> A fully-connected layer with 10 neurons with softmax\n",
        "</ul>\n",
        "Train it on MNIST for one epoch, using <code>run_keras_cnn_mnist</code>.  What is the accuracy on the validation set?\n",
        "\n",
        "If you have time to run the training for more epochs, try it, you should see improvement.\n"
      ]
    },
    {
      "metadata": {
        "id": "3-sPW8xKF7EZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "7ab10f58-b323-429b-d251-aa67ec1c5e40"
      },
      "cell_type": "code",
      "source": [
        "train, validation = get_MNIST_data()\n",
        "\n",
        "# Scale the images\n",
        "train2 =(train[0]/255.,train[1])  # Your code\n",
        "validation2 = (validation[0]/255,validation[1])  # Your code\n",
        "\n",
        "layers = [Conv2D(filters=32, kernel_size=(3,3),strides=1, padding='valid', activation='relu'),\\\n",
        "          MaxPooling2D(pool_size=(2, 2), padding='valid'),\\\n",
        "          Conv2D(filters=64, kernel_size=(3,3),strides=1, padding='valid', activation='relu'),\\\n",
        "          MaxPooling2D(pool_size=(2, 2),padding='valid'),\\\n",
        "          Flatten(),\\\n",
        "          Dense(input_dim=784, units=128, activation='relu'),\\\n",
        "          Dropout(rate=0.5),\\\n",
        "          Dense(units=10, activation=\"softmax\")]  # Your code  # Your code\n",
        "\n",
        "run_keras_cnn_mnist(train2, validation2, layers, 1, split=0.1, verbose=False, trials=5)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Loss on validation set:0.04111385222440003 Accuracy on validation set: 0.9867\n",
            "\n",
            "Loss on validation set:0.05111614862738643 Accuracy on validation set: 0.9831\n",
            "\n",
            "Loss on validation set:0.05460453747764695 Accuracy on validation set: 0.9823\n",
            "\n",
            "Loss on validation set:0.04390376804274274 Accuracy on validation set: 0.986\n",
            "\n",
            "Loss on validation set:0.048305233104340733 Accuracy on validation set: 0.9838\n",
            "\n",
            "Avg. validation accuracy:0.9843799999999998\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KjmqgGvIDFiS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5K)</b> Now, let's compare the performance of a fully connected model and a CNN on data where the characters have been shifted randomly so that they are no longer centered.  \n",
        "\n",
        "You can build such a data set by calling: <code>train_20, validation_20 = get_MNIST_data(shift=20)</code>. Remember to scale it appropriately.\n",
        "\n",
        "<b>Note that each image is now 48x48, so you will need to change your layer definitions</b>.\n",
        "Run your two-hidden-layer FC architecture from above (problem 5I) on this data and then run the CNN architecture from above (problem 5J), both for one epoch. Report your results.\n"
      ]
    },
    {
      "metadata": {
        "id": "uvfiyrN9Gf7X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1646
        },
        "outputId": "9decbd1f-8930-4a80-dd3e-7d60cf0e0112"
      },
      "cell_type": "code",
      "source": [
        "train_20, validation_20 = get_MNIST_data(shift=20) # Your code (fill in the shift)\n",
        "\n",
        "# Scale the images\n",
        "train_20 = (train_20[0]/255., train_20[1])  # Your code\n",
        "validation_20 = (validation_20[0]/255., validation_20[1])  # Your code\n",
        "\n",
        "layers_fc = [Dense(input_dim=2304, units=512, activation='relu'),\\\n",
        "             Dense(units=256, activation='relu'),\\\n",
        "             Dense(units=10, activation=\"softmax\")]  # Your code\n",
        "\n",
        "run_keras_fc_mnist(train_20, validation_20, layers_fc, 1, split=0.1, verbose=True, trials=5)\n",
        "\n",
        "layers_cnn = [Conv2D(filters=32, kernel_size=(3,3),activation='relu'),\\\n",
        "          MaxPooling2D(pool_size=(2, 2)),\\\n",
        "          Conv2D(filters=64, kernel_size=(3,3),activation='relu'),\\\n",
        "          MaxPooling2D(pool_size=(2, 2)),\\\n",
        "          Flatten(),\\\n",
        "          Dense(units=2304, activation='relu'),\\\n",
        "          Dropout(rate=0.5),\\\n",
        "          Dense(units=10, activation=\"softmax\")]# Your code\n",
        "\n",
        "run_keras_cnn_mnist(train_20, validation_20, layers_cnn, 1, split=0.1, verbose=True, trials=5)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 29s 484us/step - loss: 0.7522 - acc: 0.7526 - val_loss: 0.4040 - val_acc: 0.8725\n",
            "\n",
            "Loss on validation set:0.40399787731170655 Accuracy on validation set: 0.8725\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 29s 491us/step - loss: 0.7478 - acc: 0.7544 - val_loss: 0.4139 - val_acc: 0.8685\n",
            "\n",
            "Loss on validation set:0.4138757236480713 Accuracy on validation set: 0.8685\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 30s 499us/step - loss: 0.7486 - acc: 0.7541 - val_loss: 0.3900 - val_acc: 0.8759\n",
            "\n",
            "Loss on validation set:0.39002202274799347 Accuracy on validation set: 0.8759\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 30s 502us/step - loss: 0.7524 - acc: 0.7517 - val_loss: 0.3958 - val_acc: 0.8763\n",
            "\n",
            "Loss on validation set:0.39575207282304764 Accuracy on validation set: 0.8763\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 30s 499us/step - loss: 0.7439 - acc: 0.7539 - val_loss: 0.4188 - val_acc: 0.8677\n",
            "\n",
            "Loss on validation set:0.4188155241727829 Accuracy on validation set: 0.8677\n",
            "\n",
            "Avg. validation accuracy:0.87218\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "60000/60000 [==============================] - 73s 1ms/step - loss: 0.3577 - acc: 0.8825 - val_loss: 0.1127 - val_acc: 0.9647\n",
            "\n",
            "Loss on validation set:0.11271913922652602 Accuracy on validation set: 0.9647\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/1\n",
            "47264/60000 [======================>.......] - ETA: 15s - loss: 0.4271 - acc: 0.8585"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-118-f5c261a3cb9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mlayers_cnn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0mMaxPooling2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0mMaxPooling2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0mFlatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2304\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m          \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"softmax\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;31m# Your code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mrun_keras_cnn_mnist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers_cnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-54-3bee48562ca5>\u001b[0m in \u001b[0;36mrun_keras_cnn_mnist\u001b[0;34m(train, test, layers, epochs, split, verbose, trials)\u001b[0m\n\u001b[1;32m     54\u001b[0m                     \u001b[0minitializer_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0;31m# Run the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvacc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;34m=\u001b[0m                 \u001b[0mrun_keras\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m         \u001b[0mval_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0mtest_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtacc\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/code_for_hw8_keras.py\u001b[0m in \u001b[0;36mrun_keras\u001b[0;34m(X_train, y_train, X_val, y_val, X_test, y_test, layers, epochs, split, verbose)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         model.fit(X_train, y_train, epochs=epochs, batch_size=batch, validation_data=(X_val, y_val),\n\u001b[0;32m--> 103\u001b[0;31m                   callbacks=[history], verbose=verbose)\n\u001b[0m\u001b[1;32m    104\u001b[0m     \u001b[0;31m# Evaluate the model on validation data, if any\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX_val\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msplit\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "1iLmfPHaC2d8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<b> 5L)</b> Some possible conclusions. <a href=\"https://lms.mitx.mit.edu/courses/course-v1:MITx+6.036+2019_Spring/courseware/Week8/week8_homework/\">Refer to Catsoop.</a>"
      ]
    }
  ]
}