baseline_cnn-graphing_experiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code is from [this blog post](https://victorzhou.com/blog/keras-cnn-tutorial/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\n",
    "from keras.utils import to_categorical\n",
    "from keras import callbacks as cb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = mnist.train_images() \n",
    "train_labels = mnist.train_labels()\n",
    "test_images = mnist.test_images()\n",
    "test_labels = mnist.test_labels()\n",
    "\n",
    "# Normalize the images.\n",
    "train_images = (train_images / 255) - 0.5\n",
    "test_images = (test_images / 255) - 0.5\n",
    "\n",
    "# Reshape the images.\n",
    "train_images = np.expand_dims(train_images, axis=3)\n",
    "test_images = np.expand_dims(test_images, axis=3)\n",
    "\n",
    "num_filters = 8\n",
    "filter_size = 3\n",
    "pool_size = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Build the model.\n",
    "model = Sequential([\n",
    "  Conv2D(num_filters, filter_size, input_shape=(28, 28, 1)),\n",
    "  MaxPooling2D(pool_size=pool_size),\n",
    "  Flatten(),\n",
    "  Dense(10, activation='softmax'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compile the model.\n",
    "model.compile(\n",
    "  'adam',\n",
    "  loss='categorical_crossentropy',\n",
    "  metrics=['accuracy'],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class testCallback(cb.Callback):\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        print('epoch finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adapted from https://stackoverflow.com/questions/43178668/record-the-computation-time-for-each-epoch-in-keras-during-model-fit\n",
    "class TimeHistory(cb.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.time = 0\n",
    "\n",
    "    def on_epoch_begin(self, epoch, logs={}):\n",
    "        self.epoch_time_start = time.time()\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        self.time += time.time() - self.epoch_time_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\tensorflow_core\\python\\ops\\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/6\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\ajidot\\anaconda3\\envs\\defaults\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "60000/60000 [==============================] - 8s 137us/step - loss: 0.3683 - acc: 0.8959 - val_loss: 0.2237 - val_acc: 0.9356\n",
      "Epoch 2/6\n",
      "60000/60000 [==============================] - 8s 138us/step - loss: 0.1955 - acc: 0.9437 - val_loss: 0.1539 - val_acc: 0.9568\n",
      "Epoch 3/6\n",
      "60000/60000 [==============================] - 8s 137us/step - loss: 0.1366 - acc: 0.9603 - val_loss: 0.1262 - val_acc: 0.9625\n",
      "Epoch 4/6\n",
      "60000/60000 [==============================] - 8s 136us/step - loss: 0.1065 - acc: 0.9687 - val_loss: 0.0992 - val_acc: 0.9711\n",
      "Epoch 5/6\n",
      "60000/60000 [==============================] - 8s 136us/step - loss: 0.0900 - acc: 0.9733 - val_loss: 0.0943 - val_acc: 0.9711\n",
      "Epoch 6/6\n",
      "60000/60000 [==============================] - 8s 137us/step - loss: 0.0797 - acc: 0.9764 - val_loss: 0.0823 - val_acc: 0.9744\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "time_callback = TimeHistory()\n",
    "# Train the model.\n",
    "result = model.fit(\n",
    "  train_images,\n",
    "  to_categorical(train_labels),\n",
    "  epochs=6,\n",
    "  validation_data=(test_images, to_categorical(test_labels)),\n",
    "  callbacks=[time_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'val_loss': [0.2237384033590555, 0.1538520780801773, 0.12617362432330848, 0.09922690825238824, 0.09432859496492893, 0.08231114954538643], 'val_acc': [0.9356, 0.9568, 0.9625, 0.9711, 0.9711, 0.9744], 'loss': [0.36825620981852214, 0.19546316243708134, 0.13655511018087466, 0.1064669977940619, 0.08999748490663866, 0.07965323837126295], 'acc': [0.8958833333333334, 0.9437333333333333, 0.9602833333333334, 0.9686833333333333, 0.9732833333333333, 0.9763833333333334]}\n",
      "49.1949999332428\n"
     ]
    }
   ],
   "source": [
    "print(result.history)\n",
    "time_spent = time_callback.time\n",
    "print(time_spent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1.1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(matplotlib.__version__)\n",
    "\n",
    "random_vals = (50 - np.random.rand(30)) / 50\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(range(1,7), result.history['val_acc'], label=\"baseline Adam\")\n",
    "legend = ax.legend(loc='lower right', shadow=True, fontsize='medium')\n",
    "ax.set(xlabel='epochs')\n",
    "ax.grid()\n",
    "\n",
    "fig.savefig(\"baseline_adam.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ['baseline Adam']\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [time_spent]\n",
    "\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Time (sec)')\n",
    "plt.title('SGD with Momentum Training Time')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}