Siamese/Siamese_MNIST_keras.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Siamese Networks\n",
        "\n",
        "\n",
        "## Introduction\n",
        "\n",
        "Let's start with what are siamese networks? \n",
        "\n",
        "A siamese neural network consists of same network typically a CNN, which accept distinct inputs and generates embeddings as output. We add different loss function to encourage brining similar images together and different images apart in embedding space. Siamese NNs are popular among tasks that involve finding similarity or a relationship between two comparable things. We apply different loss functions to encourage such behaviour. Siamese networks typically share the same base network (CNN) which can be used as embedding extractor i.e. 3 inputs for triplet loss which will generate 3 outputs of embedding size of 128 or 2 inputs for contrastive loss will generate 2 outputs of embedding size of 128. \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## Types of Siamese Networks\n",
        "\n",
        "Siamese Networks comes in two flavours: using triplet loss and another using contrastive loss. Let's visit each of them.\n",
        "\n",
        "\n",
        "- **Triplets Loss**\n",
        "\n",
        "Triplets, are basically 3 images (yeah, no brainer!) which are anchor, positive and negative. What are these? **The anchor is any example from dataset. The positive is any example other than anchor belonging to same class as anchor.** And finally, **negative is any example belonging to class other than the anchor. In triplet loss, what we do lot's of triplets.** There are different ways to sample or known as mining triplets and this is what makes and breaks triplet loss approach. For some distance on the embedding space d, the loss of a triplet (a,p,n) is\n",
        "\n",
        "$$\\mathcal{L} = max(d(a, p) - d(a, n) + margin, 0)$$\n",
        "\n",
        "\n",
        "There are different such as easy triplets, semi-hard triplets, hard triplets. **Easy triplets are triplets which as loss 0**, because $$d(a, p) + margin < d(a,n)$$ **Semi-hard triplets are triplets where the negative is not closer to the anchor than the positive, but which still have positive loss,** i.e. $$d(a, p) < d(a, n) < d(a, p) + margin$$ **Hard Triplets are triplets where the negative is closer to the anchor than the positive**, i.e. $$d(a,n) < d(a,p)$$\n",
        "\n",
        "\n",
        "In the original Facenet paper, they pick a random semi-hard negative for every pair of anchor and positive, and train on these triplets and also according to paper, selecting the hardest negatives can in practice lead to badlocal minima early on in training. \"Additionally, the selected triplets can be considered moderate triplets, since they are the hardest within a small subset of the data, which is exactly what is best for learning with the triplet loss\", according to [this](https://arxiv.org/abs/1703.07737) paper. So, there is no thumb rule but starting with semi-hard yields good results and poor with easy.\n",
        "\n",
        "\n",
        "Now having decided what strategy to use for batching minings, comes the challenge of training on batch of triplets. There are two way in which batches of triplets can be trained : (i) **Offline triplet mining, where we produce triplets offline, at the beginning of each epoch for instance.** We compute all the embeddings on the training set, and then only select hard or semi-hard triplets. We can then train one epoch on these triplets. (ii) **Online triplet mining, where we compute useful triplets on the fly, for each batch of inputs.** This technique gives you more triplets for a single batch of inputs, and doesn’t require any offline mining. It is therefore much more efficient. Details of each method are described in [this](https://arxiv.org/abs/1703.07737) paper.\n",
        "\n",
        " The motivation is that the triplet loss encourages all examples belonging to same class i.e. of one  identity to be projected onto a single point in the embedding  space.\n",
        "\n",
        "\n",
        "- **Contrastive Loss**\n",
        "\n",
        "This the simplest among the two. Here, we sample two pairs of images, one **positive** and another **negative** or also known as, **similar** and **different**  pairs unlike above where we sample triplets. The two pairs i.e. **similar which contains any two images belonging to similar class** and **different where any two images belong to different class.** So, we create a lot many such similar and different pairs and pass it to any CNN architecture without the head(classification layer) and use the dimension of penultimate layer or add any number of linear layers  to obtain embeddings of size, say 128. Different name for loss suggests, we are not using our typical classification loss such as NLL or cross entropy for classification as there is no classification layer. So, how can we train our model (as in, what to backpropogate)? The answer is contrastive loss. **Contrastive Loss is a distance-based loss where such losses try to ensure that semantically similar examples are embedded close together.** This is what makes them special for our case, where we want similar images to be present closer in the embedding space and push different pairs away. We can also train by adding cross entropy as loss function with labels as 0 for similar images and 1 for different images. To learn more about this loss function, please read the paper linked below.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## Further Readings\n",
        "\n",
        "[Signature Verification using a \"Siamese\" Time Delay Neural Network ](https://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network.pdf)\n",
        "\n",
        "[Siamese Neural Networks for One-shot Image Recognition](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)\n",
        "\n",
        "[Facenet paper introducing Triplets](https://arxiv.org/abs/1503.03832)\n",
        "\n",
        "[Contrastive Loss paper](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf)\n",
        "\n",
        "[In defense of Triplet Loss for Person Re-identification](https://arxiv.org/abs/1703.07737)\n",
        "\n",
        "[Andrew Ng's Triplet Loss Lecture](https://www.coursera.org/learn/convolutional-neural-networks/lecture/HuUtN/triplet-loss)\n",
        "\n",
        "[Triplet Loss and Online Triplet Mining in TensorFlow](https://omoindrot.github.io/triplet-loss)\n",
        "\n",
        "---\n",
        "\n",
        "## Experiements\n",
        "\n",
        "Here we will start small with everyone's favourite dataset MNIST. (Yay!!) We will implement both triplet loss and contrastive loss siamese networks on MNIST and obtain the projections using t-SNE on embeddings trained on siamese model to see how different cluster are formed and how far are they separated indicating they have been successful in perform their task.\n",
        "\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dudeperf3ct/DL_notebooks/blob/master/Siamese/Siamese_MNIST_keras.ipynb)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w7JO0NtG9l-e",
        "colab_type": "text"
      },
      "source": [
        "# Keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDK__uIFgu0F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "import random\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import time\n",
        "import keras.backend as K\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Model, load_model\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Lambda, Layer, Input, concatenate, Concatenate\n",
        "from functools import partial, update_wrapper\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.rcParams['image.cmap'] = 'gray'\n",
        "\n",
        "np.random.seed(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vkvuz_y-9lfn",
        "colab_type": "code",
        "outputId": "6c3e27ae-d92c-4c22-88de-f6ce827a0095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "(train_X, train_Y), (val_X, val_Y) = mnist.load_data()\n",
        "print ('Training shape:', train_X.shape, train_Y.shape)\n",
        "print ('Validation shape:', val_X.shape, val_Y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training shape: (60000, 28, 28) (60000,)\n",
            "Validation shape: (10000, 28, 28) (10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3IXRFGWh7AL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_size = 28\n",
        "input_shape = (28, 28, 1)\n",
        "num_classes = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbOgH4O-h64i",
        "colab_type": "code",
        "outputId": "cfe37948-9666-4143-c0d2-33d0cfeb9ef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "train_x = train_X.astype('float32')\n",
        "val_x = val_X.astype('float32')\n",
        "train_x /= 255\n",
        "val_x /= 255\n",
        "\n",
        "train_y = to_categorical(train_Y, num_classes=num_classes)\n",
        "val_y = to_categorical(val_Y, num_classes=num_classes)\n",
        "print ('Training shape:', train_x.shape, train_y.shape)\n",
        "print ('Validation shape:', val_x.shape, val_y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training shape: (60000, 28, 28) (60000, 10)\n",
            "Validation shape: (10000, 28, 28) (10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJGrb5UP9li1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def siamese_network():\n",
        "    \n",
        "    inp = Input(shape=(28, 28, 1))\n",
        "    x = Conv2D(32, (3, 3), activation='relu', input_shape=input_shape)(inp)\n",
        "    x = Conv2D(64, (3, 3), activation='relu')(x)\n",
        "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    x = Flatten()(x)\n",
        "    x = Dense(128, activation='relu')(x)\n",
        "    norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(x)\n",
        "    \n",
        "    return Model(inputs=inp, outputs=norm_layer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qC7W70atQuy",
        "colab_type": "code",
        "outputId": "9adee29b-7678-45d1-aba8-9863f334eb93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "siamese_model = siamese_network()\n",
        "siamese_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               1179776   \n",
            "_________________________________________________________________\n",
            "norm_layer (Lambda)          (None, 128)               0         \n",
            "=================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3d4uT_pFvKs",
        "colab_type": "text"
      },
      "source": [
        "## Method - 1 (Triplet Loss)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcWqeIyss9mX",
        "colab_type": "text"
      },
      "source": [
        "### FInd hard positives and negatives using embedding distance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_Ux_GY3Gv60M",
        "colab": {}
      },
      "source": [
        "num_images_by_class = []\n",
        "\n",
        "for i in range(len(np.unique(train_Y))):\n",
        "    \n",
        "    num_images_by_class.append(len(train_Y[train_Y == i]))\n",
        "    \n",
        "num_images_by_class = np.array(num_images_by_class)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oF26umBysFPZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Form batches by randomly sampling P classes (person identities), \n",
        "#and then randomly sampling K images of each class (person)\n",
        "#resulting in a batch of PK images.\n",
        "#Choose B, P, and K in such a way that 3B is close to PK\n",
        "#P = 42, K = 4, B = 96\n",
        "\n",
        "#For each sample a in the batch, we can select the hardest positive \n",
        "#and the hardest negative samples within the batch when forming the triplets \n",
        "#for computing the loss, which we call Batch Hard\n",
        "\n",
        "#The selected triplets can be considered moderate triplets, \n",
        "#since they are the hardest within a small subset of the data, \n",
        "#which is exactly what is best for learning with the triplet loss\n",
        "\n",
        "class BatchHard:\n",
        "    \n",
        "    def __init__(self, batch_size, P):\n",
        "        self.P = P\n",
        "        self.batch_size = batch_size\n",
        "        \n",
        "    def get_batch(self):\n",
        "        \n",
        "        #randomly sample P classes based on probability distribution of classes\n",
        "        P_classes = np.random.choice(np.arange(train_y.shape[1]), p=num_images_by_class/num_images_by_class.sum(), size=self.P)\n",
        "        #print (P_classes)\n",
        "        batch_samples = []\n",
        "        anchors = []\n",
        "        positives = []\n",
        "        negatives = []\n",
        "        j = 0        \n",
        "\n",
        "        #for each class in P\n",
        "        for i in P_classes:\n",
        "            #find the instances of same class\n",
        "            lbl_index = np.where(train_Y == i)\n",
        "            #sample 1 images of each classes\n",
        "            random_index = np.random.choice(lbl_index[0], size=1)\n",
        "            #batch of P images\n",
        "            batch_samples.append(train_X[random_index])\n",
        "            j += 1\n",
        "\n",
        "        batch_samples = np.squeeze(batch_samples)\n",
        "        batch = batch_samples.reshape((-1, 28, 28, 1))\n",
        "\n",
        "        #pass the batch of P images to siamese network to predict embeddings for each image\n",
        "        pred_batch_samples = siamese_model.predict(batch)\n",
        "\n",
        "        #find all hardest positive and hard negative for each a in batch\n",
        "        for i in range(len(batch_samples)):\n",
        "            #print ('Current:', P_classes[i])\n",
        "            similar_class = np.where(P_classes == P_classes[i])[0]\n",
        "            diff_class = np.where(P_classes != P_classes[i])[0]\n",
        "            #print ('Similar Class:', similar_class)\n",
        "            #print ('Different Class:', diff_class)\n",
        "\n",
        "            a = pred_batch_samples[i]\n",
        "            anchors.append(batch_samples[i])\n",
        "            #print ('Anchor predicted embedding:', a, a.shape)\n",
        "\n",
        "            # find hard positive\n",
        "            positive_dist = []\n",
        "            #print ('Positive predicted embedding:')\n",
        "            for sim_index in similar_class:\n",
        "                #print (pred_batch_samples[sim_index], pred_batch_samples[sim_index].shape)\n",
        "                positive_dist.append(np.linalg.norm(a-pred_batch_samples[sim_index]))\n",
        "            #print ('Positive:', positive_dist, similar_class[np.argmax(np.array(positive_dist))])\n",
        "            positives.append(batch_samples[similar_class[np.argmax(np.array(positive_dist))]])\n",
        "\n",
        "            # find hard negative\n",
        "            negative_dist = []\n",
        "            for diff_index in diff_class:\n",
        "                negative_dist.append(np.linalg.norm(a-pred_batch_samples[diff_index]))\n",
        "            #print ('Negative:', negative_dist, diff_class[np.argmin(np.array(negative_dist))])\n",
        "            negatives.append(batch_samples[diff_class[np.argmin(np.array(negative_dist))]])\n",
        "        anchors = np.array(anchors)\n",
        "        anchors = anchors.reshape(-1, anchors.shape[-2], anchors.shape[-1])\n",
        "        positives = np.array(positives)\n",
        "        positives = positives.reshape(-1, positives.shape[-2], positives.shape[-1])\n",
        "        negatives = np.array(negatives)\n",
        "        negatives = negatives.reshape(-1, negatives.shape[-2], negatives.shape[-1])\n",
        "        self.triplets = [np.expand_dims(anchors, axis=-1), np.expand_dims(positives, axis=-1), np.expand_dims(negatives, axis=-1)]\n",
        "\n",
        "        return np.array(self.triplets)\n",
        "\n",
        "    def show_samples(self, anchors, positives, negatives):\n",
        "        rand_index = random.sample(list(np.arange(anchors.shape[0])), 4) \n",
        "        fig, ax = plt.subplots(4, 3)\n",
        "        ax[0][0].imshow(np.squeeze(anchors[rand_index[0]]))\n",
        "        ax[0][1].imshow(np.squeeze(positives[rand_index[0]]))\n",
        "        ax[0][2].imshow(np.squeeze(negatives[rand_index[0]]))\n",
        "        ax[1][0].imshow(np.squeeze(anchors[rand_index[1]]))\n",
        "        ax[1][1].imshow(np.squeeze(positives[rand_index[1]]))\n",
        "        ax[1][2].imshow(np.squeeze(negatives[rand_index[1]]))\n",
        "        ax[2][0].imshow(np.squeeze(anchors[rand_index[2]]))\n",
        "        ax[2][1].imshow(np.squeeze(positives[rand_index[2]]))\n",
        "        ax[2][2].imshow(np.squeeze(negatives[rand_index[2]]))\n",
        "        ax[3][0].imshow(np.squeeze(anchors[rand_index[3]]))\n",
        "        ax[3][1].imshow(np.squeeze(positives[rand_index[3]]))\n",
        "        ax[3][2].imshow(np.squeeze(negatives[rand_index[3]]))\n",
        "        plt.show()\n",
        "    \n",
        "#     # data generator for triplets\n",
        "#     def batch_generator(self):\n",
        "#         i = 0\n",
        "#         while True:\n",
        "#             a_embed = np.array(self.triplets[0][i:i+self.batch_size])\n",
        "#             p_embed = np.array(self.triplets[1][i:i+self.batch_size])\n",
        "#             n_embed = np.array(self.triplets[2][i:i+self.batch_size])\n",
        "#             print (np.array(self.triplets).shape)\n",
        "#             print (i, i+self.batch_size)\n",
        "#             print (a_embed.shape, p_embed.shape, n_embed.shape)\n",
        "#             i += self.batch_size\n",
        "\n",
        "#             yield (np.concatenate([a_embed, p_embed, n_embed], axis=0), np.zeros(3*self.batch_size))\n",
        "            \n",
        "#     # return data generator for triplets\n",
        "#     def get_generator(self):\n",
        "#         gen = self.batch_generator()\n",
        "#         return gen"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UPi_1oHY9iEp",
        "colab": {}
      },
      "source": [
        "class TripletLossLayer(Layer):\n",
        "    \n",
        "    def __init__(self, alpha, **kwargs):\n",
        "        self.alpha = alpha\n",
        "        super(TripletLossLayer, self).__init__(**kwargs)\n",
        "    \n",
        "    def triplet_loss(self, inputs):\n",
        "        a, p, n = inputs\n",
        "        p_dist = K.sum(K.square(a-p), axis=1)\n",
        "        n_dist = K.sum(K.square(a-n), axis=1)\n",
        "        return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)\n",
        "    \n",
        "    def call(self, inputs):\n",
        "        loss = self.triplet_loss(inputs)\n",
        "        self.add_loss(loss)\n",
        "        return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AbrVTLvc9iFj",
        "colab": {}
      },
      "source": [
        "a_inp = Input(shape=(28, 28, 1))\n",
        "p_inp = Input(shape=(28, 28, 1))\n",
        "n_inp = Input(shape=(28, 28, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cXJOGqVI9iF7",
        "colab": {}
      },
      "source": [
        "a_embed = siamese_model(a_inp)\n",
        "p_embed = siamese_model(p_inp)\n",
        "n_embed = siamese_model(n_inp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "18Vpidva9iGT",
        "colab": {}
      },
      "source": [
        "# Layer that computes the triplet loss from anchor, positive and negative embedding vectors\n",
        "triplet_loss_layer = TripletLossLayer(alpha=0.2, name='triplet_loss_layer')([a_embed, p_embed, n_embed])\n",
        "\n",
        "# Model that can be trained with anchor, positive negative images\n",
        "model = Model(inputs=[a_inp, p_inp, n_inp], outputs=triplet_loss_layer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "f4d963a6-719b-4f69-feb3-d17abdfa944c",
        "id": "eXxt2z_19iGt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "model.summary()\n",
        "model.compile(loss=None, optimizer='adam')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_7 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_8 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_9 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_3 (Model)                 (None, 128)          1198592     input_7[0][0]                    \n",
            "                                                                 input_8[0][0]                    \n",
            "                                                                 input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "triplet_loss_layer (TripletLoss [(None, 128), (None, 0           model_3[4][0]                    \n",
            "                                                                 model_3[5][0]                    \n",
            "                                                                 model_3[6][0]                    \n",
            "==================================================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPabMKMK9q2M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 60\n",
        "P = 20000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8sV3rgnd9ZkS",
        "colab": {}
      },
      "source": [
        "batches = BatchHard(batch_size, P)\n",
        "triplets = batches.get_batch()\n",
        "a = np.array(triplets[0])\n",
        "p = np.array(triplets[1])\n",
        "n = np.array(triplets[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUPldkf-yvn2",
        "colab_type": "code",
        "outputId": "6f4202d3-6260-4e56-b064-0560fabb46c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1822
        }
      },
      "source": [
        "model.fit([a, p, n], None, epochs=50, batch_size=batch_size, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "20000/20000 [==============================] - 4s 224us/step - loss: 5.3933\n",
            "Epoch 2/50\n",
            "20000/20000 [==============================] - 4s 195us/step - loss: 1.0430\n",
            "Epoch 3/50\n",
            "20000/20000 [==============================] - 4s 188us/step - loss: 0.5894\n",
            "Epoch 4/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.3798\n",
            "Epoch 5/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.2910\n",
            "Epoch 6/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.1995\n",
            "Epoch 7/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.1708\n",
            "Epoch 8/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.1668\n",
            "Epoch 9/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.1227\n",
            "Epoch 10/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.1190\n",
            "Epoch 11/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.1274\n",
            "Epoch 12/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0916\n",
            "Epoch 13/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0794\n",
            "Epoch 14/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0690\n",
            "Epoch 15/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0707\n",
            "Epoch 16/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0730\n",
            "Epoch 17/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0615\n",
            "Epoch 18/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0599\n",
            "Epoch 19/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0570\n",
            "Epoch 20/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0635\n",
            "Epoch 21/50\n",
            "20000/20000 [==============================] - 4s 195us/step - loss: 0.0656\n",
            "Epoch 22/50\n",
            "20000/20000 [==============================] - 4s 198us/step - loss: 0.0525\n",
            "Epoch 23/50\n",
            "20000/20000 [==============================] - 4s 198us/step - loss: 0.0456\n",
            "Epoch 24/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0441\n",
            "Epoch 25/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0392\n",
            "Epoch 26/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0418\n",
            "Epoch 27/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0373\n",
            "Epoch 28/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0391\n",
            "Epoch 29/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0362\n",
            "Epoch 30/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0363\n",
            "Epoch 31/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0292\n",
            "Epoch 32/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0348\n",
            "Epoch 33/50\n",
            "20000/20000 [==============================] - 4s 189us/step - loss: 0.0329\n",
            "Epoch 34/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0384\n",
            "Epoch 35/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0290\n",
            "Epoch 36/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0262\n",
            "Epoch 37/50\n",
            "20000/20000 [==============================] - 4s 190us/step - loss: 0.0325\n",
            "Epoch 38/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0309\n",
            "Epoch 39/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0238\n",
            "Epoch 40/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0286\n",
            "Epoch 41/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0231\n",
            "Epoch 42/50\n",
            "20000/20000 [==============================] - 4s 194us/step - loss: 0.0230\n",
            "Epoch 43/50\n",
            "20000/20000 [==============================] - 4s 199us/step - loss: 0.0198\n",
            "Epoch 44/50\n",
            "20000/20000 [==============================] - 4s 199us/step - loss: 0.0242\n",
            "Epoch 45/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0181\n",
            "Epoch 46/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0163\n",
            "Epoch 47/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0242\n",
            "Epoch 48/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0204\n",
            "Epoch 49/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0195\n",
            "Epoch 50/50\n",
            "20000/20000 [==============================] - 4s 191us/step - loss: 0.0141\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc51df652b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCK_pC-D-oPr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4X7n3gLb-om8"
      },
      "source": [
        "#### t-SNE visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "8d9bfd5b-6bdd-4aac-c950-e2b288184d34",
        "id": "BuVwtQAX-onF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import random\n",
        "from keras.models import Model\n",
        "from keras.preprocessing import image\n",
        "np.random.seed(42)\n",
        "\n",
        "arr = train_X[:512]\n",
        "lbl = train_Y[:512]\n",
        "print (arr.shape, lbl.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 28, 28) (512,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "c3ee3c01-cd09-462c-98e3-5eabddd7cdc7",
        "id": "64rr_7NF-onz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_7 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_8 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_9 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_3 (Model)                 (None, 128)          1198592     input_7[0][0]                    \n",
            "                                                                 input_8[0][0]                    \n",
            "                                                                 input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "triplet_loss_layer (TripletLoss [(None, 128), (None, 0           model_3[4][0]                    \n",
            "                                                                 model_3[5][0]                    \n",
            "                                                                 model_3[6][0]                    \n",
            "==================================================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "6a60d4b9-0434-4ddd-ad52-03bd010cc08e",
        "id": "_MEqWgc0-ooh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "model.layers[3].summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               1179776   \n",
            "_________________________________________________________________\n",
            "norm_layer (Lambda)          (None, 128)               0         \n",
            "=================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "emDMeRB5-opF",
        "colab": {}
      },
      "source": [
        "model.save_weights('weights.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "eb8505ce-049c-485c-d714-0f6e0cdc5091",
        "id": "ROEVX8Ra-opa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "tmp_model = Model(a_inp, a_embed)\n",
        "tmp_model.summary()\n",
        "tmp_model.load_weights('weights.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_7 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "model_3 (Model)              (None, 128)               1198592   \n",
            "=================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "da0ec138-5346-446e-9c97-e7d5f3286ef5",
        "id": "qUYMwy4D-oqF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "X_train_trm = tmp_model.predict(train_x.reshape(-1,28,28,1))\n",
        "\n",
        "Classifier_input = Input((128,))\n",
        "Classifier_output = Dense(10, activation='softmax')(Classifier_input)\n",
        "Classifier_model = Model(Classifier_input, Classifier_output)\n",
        "\n",
        "\n",
        "Classifier_model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "Classifier_model.fit(X_train_trm, train_y, epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 5s 83us/step - loss: 2.2380 - acc: 0.2377\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 2.1292 - acc: 0.4381\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 2.0372 - acc: 0.5031\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 1.9579 - acc: 0.5386\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 1.8887 - acc: 0.5583\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 1.8280 - acc: 0.5679\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 1.7739 - acc: 0.5774\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 1.7260 - acc: 0.5857\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 5s 90us/step - loss: 1.6829 - acc: 0.5913\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 5s 88us/step - loss: 1.6439 - acc: 0.5978\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc51d949860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "dcf2233a-ef76-4cf6-9413-ce3d9ea215db",
        "id": "rQW4BOLw-oql",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "img_arr = arr.reshape(*arr.shape, 1)\n",
        "img_arr.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "27554b1d-78eb-4023-9fd6-a849a4f5e8e9",
        "id": "pVEW3q-B-orI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "preds = tmp_model.predict(img_arr, batch_size=50)\n",
        "print (preds.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 128)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ea_pxxg7-orn",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.patheffects as PathEffects\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Define our own plot function\n",
        "def scatter(x, labels, subtitle=None):\n",
        "    # We choose a color palette with seaborn.\n",
        "    palette = np.array(sns.color_palette(\"hls\", 10))\n",
        "\n",
        "    # We create a scatter plot.\n",
        "    f = plt.figure(figsize=(8, 8))\n",
        "    ax = plt.subplot(aspect='equal')\n",
        "    sc = ax.scatter(x[:,0], x[:,1], lw=0, s=40,\n",
        "                    c=palette[labels.astype(np.int)])\n",
        "    plt.xlim(-25, 25)\n",
        "    plt.ylim(-25, 25)\n",
        "    ax.axis('off')\n",
        "    ax.axis('tight')\n",
        "\n",
        "    # We add the labels for each digit.\n",
        "    txts = []\n",
        "    for i in range(10):\n",
        "        # Position of each label.\n",
        "        xtext, ytext = np.median(x[labels == i, :], axis=0)\n",
        "        txt = ax.text(xtext, ytext, str(i), fontsize=24)\n",
        "        txt.set_path_effects([\n",
        "            PathEffects.Stroke(linewidth=5, foreground=\"w\"),\n",
        "            PathEffects.Normal()])\n",
        "        txts.append(txt)\n",
        "        \n",
        "    if subtitle != None:\n",
        "        plt.suptitle(subtitle)\n",
        "        \n",
        "    plt.savefig(subtitle)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yOZ2A1xR-osB",
        "colab": {}
      },
      "source": [
        "x_train_flat = train_x.reshape(-1,784)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "a538feb4-062b-427d-cc21-e23110bdb5fa",
        "id": "PLpwXzMp-osb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "tsne = TSNE()\n",
        "train_tsne_embeds = tsne.fit_transform(x_train_flat[:512])\n",
        "scatter(train_tsne_embeds, train_Y[:512], \"Samples from Training Data\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W/QEcA/wXnxAnO/vd+KS2kuxEtBoSrFsZFzNPUrWZ+2KdsleFU6GmAQOyts8F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cDTTz/NxIkTAcLqZuMHbrDyPTHdssqRP/ueSCvBQBDlEKTl3gaId3i5ceknypivHreh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\nxlpRFEVRqhw11oqiKIpS5fw/MXPx1ECr5jQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uPoFlU12-os4",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "3927926a-d8a5-4661-ed93-4d7c4b648d33",
        "id": "MyBCJQVM-otG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "l = np.array(lbl)\n",
        "l[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KE-L6jfD-oto",
        "colab": {}
      },
      "source": [
        "preds_trans = TSNE().fit_transform(preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9f2f285d-b84b-46a0-8031-70bd9c31bbdd",
        "id": "6BQTa8PJ-ot1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "scatter(preds_trans, train_Y[:512], \"Training Data After TNN\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9TQEtEjDOJO9p4LIYqhYS22uqne7E0LJiIlJv6BG3SPD0iLFerGNIFle3ISJSdxTQIsHz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\nFVd3yXX2z3I9jaBFJGV4A52nsMVXfoUdOZe4i00vYBKwsR6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3/N6Weh4v0mzoGbSINKbr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\nZQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HNE1gKKs-ouO",
        "colab": {}
      },
      "source": [
        "preds_df = pd.DataFrame(np.concatenate((preds_trans, l[:, None]), axis=1), columns=['x','y','labels'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "dfe40106-1903-4a14-9577-80c71aea5fc5",
        "id": "lSq3g9ZN-oud",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "preds_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6.717429</td>\n",
              "      <td>8.386817</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-28.196423</td>\n",
              "      <td>15.490483</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16.823631</td>\n",
              "      <td>1.653309</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.417643</td>\n",
              "      <td>-22.085928</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.743126</td>\n",
              "      <td>19.940912</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           x          y  labels\n",
              "0  -6.717429   8.386817     5.0\n",
              "1 -28.196423  15.490483     0.0\n",
              "2  16.823631   1.653309     4.0\n",
              "3   2.417643 -22.085928     1.0\n",
              "4  10.743126  19.940912     9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "7544df19-6e50-4978-ddd3-ad11ffaac1ab",
        "id": "EpB7wvoW-ou0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "sns.lmplot('x', 'y', data=preds_df, hue='labels', fit_reg=False, legend=False, height=10)\n",
        "plt.grid(visible=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DgAwA14mJFABwc6EHGQCuExMpAODmQkAGgG3AUg8AuHnQYgEAAABkEJABAACADAIyAAAA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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jTP86zwA-ovN",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGSogN5ery0Y",
        "colab_type": "text"
      },
      "source": [
        "### Finding hard positives and negatives using image distance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V-xflGhyv83u",
        "colab": {}
      },
      "source": [
        "num_images_by_class = []\n",
        "\n",
        "for i in range(len(np.unique(train_Y))):\n",
        "    \n",
        "    num_images_by_class.append(len(train_Y[train_Y == i]))\n",
        "    \n",
        "num_images_by_class = np.array(num_images_by_class)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DS8duOwuWZ54",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Form batches by randomly sampling P classes (person identities), \n",
        "#and then randomly sampling K images of each class (person)\n",
        "#resulting in a batch of PK images.\n",
        "#Choose B, P, and K in such a way that 3B is close to PK\n",
        "#P = 42, K = 4, B = 96\n",
        "\n",
        "#For each sample a in the batch, we can select the hardest positive \n",
        "#and the hardest negative samples within the batch when forming the triplets \n",
        "#for computing the loss, which we call Batch Hard\n",
        "\n",
        "#The selected triplets can be considered moderate triplets, \n",
        "#since they are the hardest within a small subset of the data, \n",
        "#which is exactly what is best for learning with the triplet loss\n",
        "\n",
        "class BatchHard:\n",
        "    \n",
        "    def __init__(self, batch_size, P, N):\n",
        "        self.P = P\n",
        "        self.N = N\n",
        "        self.batch_size = batch_size\n",
        "        \n",
        "    def get_batch(self):\n",
        "        \n",
        "        #randomly sample P classes based on probability distribution of classes\n",
        "        P_classes = np.random.choice(np.arange(train_y.shape[1]), p=num_images_by_class/num_images_by_class.sum(), size=self.P)\n",
        "        batch_samples = [[] for _ in range(self.P)]\n",
        "        anchors = [[] for _ in range(self.P)]\n",
        "        positives = [[] for _ in range(self.P)]\n",
        "        negatives = [[] for _ in range(self.P)]\n",
        "        j = 0        \n",
        "        \n",
        "        #for each class in P\n",
        "        for i in P_classes:\n",
        "            #find the instances of same class\n",
        "            lbl_index = np.where(train_Y == i)\n",
        "            #sample K images of each classes\n",
        "            random_index = np.random.choice(lbl_index[0], size=self.N)\n",
        "            #batch of PK images\n",
        "            batch_samples[j].append(train_X[random_index])\n",
        "            j += 1\n",
        "\n",
        "        batch_samples = np.squeeze(batch_samples)\n",
        "        \n",
        "        #find all hardest positive and hard negative for each a in batch\n",
        "        for i in range(len(batch_samples)):\n",
        "            similar_class = np.where(P_classes == P_classes[i])[0]\n",
        "            \n",
        "            j = 0\n",
        "            while(j < self.N):\n",
        "                a = batch_samples[i][j]\n",
        "                anchors[i].append(a)\n",
        "                \n",
        "                #find hard positive\n",
        "                positive_dist = [[] for _ in range(self.P)]\n",
        "                for p in range(self.P):\n",
        "                    if p not in similar_class :\n",
        "                        positive_dist[p].append([0]*self.N)           \n",
        "                    else:\n",
        "                        for k in range(self.N):\n",
        "                            positive_dist[p].append(np.linalg.norm(a-batch_samples[p][k]))\n",
        "                    positive_dist[p] = np.squeeze(np.array(positive_dist[p]))   \n",
        "                positives[i].append(batch_samples[np.argmax(np.array(positive_dist)) // self.N][np.argmax(np.array(positive_dist)) % self.N])\n",
        "                \n",
        "                #find hard negative             \n",
        "                negative_dist = [[] for _ in range(self.P)]\n",
        "                for p in range(self.P):\n",
        "                    if p in similar_class :\n",
        "                        negative_dist[p].append([np.nan]*self.N)           \n",
        "                    else:\n",
        "                        for k in range(self.N):\n",
        "                            negative_dist[p].append(np.linalg.norm(a-batch_samples[p][k]))\n",
        "                    negative_dist[p] = np.squeeze(np.array(negative_dist[p]))                \n",
        "\n",
        "                negatives[i].append(batch_samples[np.nanargmin(np.array(negative_dist)) // self.N][np.nanargmin(np.array(negative_dist)) % self.N])\n",
        "                j+=1\n",
        " \n",
        "        anchors = np.array(anchors)\n",
        "        anchors = anchors.reshape(-1, anchors.shape[-2], anchors.shape[-1])\n",
        "        positives = np.array(positives)\n",
        "        positives = positives.reshape(-1, positives.shape[-2], positives.shape[-1])\n",
        "        negatives = np.array(negatives)\n",
        "        negatives = negatives.reshape(-1, negatives.shape[-2], negatives.shape[-1])\n",
        "        self.triplets = [np.expand_dims(anchors, axis=-1), np.expand_dims(positives, axis=-1), np.expand_dims(negatives, axis=-1)]\n",
        "        \n",
        "        return np.array(self.triplets)\n",
        " \n",
        "    def show_samples(self, anchors, positives, negatives):\n",
        "        print (anchors.shape, anchors[0].shape, anchors[0][0].shape)\n",
        "        fig, ax = plt.subplots(4, 3)\n",
        "        ax[0][0].imshow(np.squeeze(anchors[0]))\n",
        "        ax[0][1].imshow(np.squeeze(positives[0]))\n",
        "        ax[0][2].imshow(np.squeeze(negatives[0]))\n",
        "        ax[1][0].imshow(np.squeeze(anchors[1]))\n",
        "        ax[1][1].imshow(np.squeeze(positives[1]))\n",
        "        ax[1][2].imshow(np.squeeze(negatives[1]))\n",
        "        ax[2][0].imshow(np.squeeze(anchors[2]))\n",
        "        ax[2][1].imshow(np.squeeze(positives[2]))\n",
        "        ax[2][2].imshow(np.squeeze(negatives[2]))\n",
        "        ax[3][0].imshow(np.squeeze(anchors[3]))\n",
        "        ax[3][1].imshow(np.squeeze(positives[3]))\n",
        "        ax[3][2].imshow(np.squeeze(negatives[3]))\n",
        "        plt.show()\n",
        "\n",
        "#     # data generator for triplets\n",
        "#     def batch_generator(self):\n",
        "#         i = 0\n",
        "#         while True:\n",
        "#             a_embed = np.array(self.triplets[0][i:i+self.batch_size])\n",
        "#             p_embed = np.array(self.triplets[1][i:i+self.batch_size])\n",
        "#             n_embed = np.array(self.triplets[2][i:i+self.batch_size])\n",
        "#             print (i, i+self.batch_size)\n",
        "#             print (a_embed.shape, p_embed.shape, n_embed.shape)\n",
        "#             print (a_embed)\n",
        "#             i += self.batch_size\n",
        "\n",
        "#             yield [a_embed, p_embed, n_embed], None\n",
        "            \n",
        "#     # return data generator for triplets\n",
        "#     def get_generator(self):\n",
        "#         gen = self.batch_generator()\n",
        "#         return gen       "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5Da3uTHjG_s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class TripletLossLayer(Layer):\n",
        "    \n",
        "    def __init__(self, alpha, **kwargs):\n",
        "        self.alpha = alpha\n",
        "        super(TripletLossLayer, self).__init__(**kwargs)\n",
        "    \n",
        "    def triplet_loss(self, inputs):\n",
        "        a, p, n = inputs\n",
        "        p_dist = K.sum(K.square(a-p), axis=1)\n",
        "        n_dist = K.sum(K.square(a-n), axis=1)\n",
        "        return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)\n",
        "    \n",
        "    def call(self, inputs):\n",
        "        loss = self.triplet_loss(inputs)\n",
        "        self.add_loss(loss)\n",
        "        return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWD0sba29lpQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a_inp = Input(shape=(28, 28, 1))\n",
        "p_inp = Input(shape=(28, 28, 1))\n",
        "n_inp = Input(shape=(28, 28, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wzo6VUzss1J2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a_embed = siamese_model(a_inp)\n",
        "p_embed = siamese_model(p_inp)\n",
        "n_embed = siamese_model(n_inp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvte8p7a96zK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Layer that computes the triplet loss from anchor, positive and negative embedding vectors\n",
        "triplet_loss_layer = TripletLossLayer(alpha=0.2, name='triplet_loss_layer')([a_embed, p_embed, n_embed])\n",
        "\n",
        "# Model that can be trained with anchor, positive negative images\n",
        "model = Model(inputs=[a_inp, p_inp, n_inp], outputs=triplet_loss_layer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6V1ZYNsX_Zf",
        "colab_type": "code",
        "outputId": "c962a010-5192-4c55-ab88-6e579a90b3f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "model.summary()\n",
        "model.compile(loss=None, optimizer='adam')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_13 (InputLayer)           (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_14 (InputLayer)           (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_15 (InputLayer)           (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_10 (Model)                (None, 128)          1198592     input_13[0][0]                   \n",
            "                                                                 input_14[0][0]                   \n",
            "                                                                 input_15[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "triplet_loss_layer (TripletLoss [(None, 128), (None, 0           model_10[1][0]                   \n",
            "                                                                 model_10[2][0]                   \n",
            "                                                                 model_10[3][0]                   \n",
            "==================================================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyDb9OQ1iAmr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32\n",
        "epochs = 0\n",
        "P = 500\n",
        "N = 40\n",
        "batches = BatchHard(batch_size, P, N)\n",
        "num_samples = P*N"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIJeoPLPwW-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "triplets = batches.get_batch()\n",
        "a = np.array(triplets[0])\n",
        "p = np.array(triplets[1])\n",
        "n = np.array(triplets[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_Sm8uvKwhGE",
        "colab_type": "code",
        "outputId": "86be3d14-dce5-43e7-9fcb-70fce1ce70b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1822
        }
      },
      "source": [
        "model.fit([a, p, n], None, epochs=50, batch_size=batch_size, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "20000/20000 [==============================] - 6s 285us/step - loss: 1.1472\n",
            "Epoch 2/50\n",
            "20000/20000 [==============================] - 5s 256us/step - loss: 0.3572\n",
            "Epoch 3/50\n",
            "20000/20000 [==============================] - 5s 256us/step - loss: 0.2159\n",
            "Epoch 4/50\n",
            "20000/20000 [==============================] - 5s 255us/step - loss: 0.1425\n",
            "Epoch 5/50\n",
            "20000/20000 [==============================] - 5s 254us/step - loss: 0.0870\n",
            "Epoch 6/50\n",
            "20000/20000 [==============================] - 5s 254us/step - loss: 0.0732\n",
            "Epoch 7/50\n",
            "20000/20000 [==============================] - 5s 254us/step - loss: 0.0639\n",
            "Epoch 8/50\n",
            "20000/20000 [==============================] - 5s 253us/step - loss: 0.0618\n",
            "Epoch 9/50\n",
            "20000/20000 [==============================] - 5s 265us/step - loss: 0.0430\n",
            "Epoch 10/50\n",
            "20000/20000 [==============================] - 5s 271us/step - loss: 0.0384\n",
            "Epoch 11/50\n",
            "20000/20000 [==============================] - 5s 255us/step - loss: 0.0436\n",
            "Epoch 12/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0422\n",
            "Epoch 13/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0380\n",
            "Epoch 14/50\n",
            "20000/20000 [==============================] - 5s 253us/step - loss: 0.0242\n",
            "Epoch 15/50\n",
            "20000/20000 [==============================] - 5s 253us/step - loss: 0.0319\n",
            "Epoch 16/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0229\n",
            "Epoch 17/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0191\n",
            "Epoch 18/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0209\n",
            "Epoch 19/50\n",
            "20000/20000 [==============================] - 5s 254us/step - loss: 0.0214\n",
            "Epoch 20/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0169\n",
            "Epoch 21/50\n",
            "20000/20000 [==============================] - 5s 253us/step - loss: 0.0198\n",
            "Epoch 22/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0145\n",
            "Epoch 23/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0177\n",
            "Epoch 24/50\n",
            "20000/20000 [==============================] - 5s 253us/step - loss: 0.0120\n",
            "Epoch 25/50\n",
            "20000/20000 [==============================] - 5s 270us/step - loss: 0.0132\n",
            "Epoch 26/50\n",
            "20000/20000 [==============================] - 5s 271us/step - loss: 0.0120\n",
            "Epoch 27/50\n",
            "20000/20000 [==============================] - 5s 253us/step - loss: 0.0126\n",
            "Epoch 28/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0083\n",
            "Epoch 29/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0125\n",
            "Epoch 30/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0098\n",
            "Epoch 31/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0116\n",
            "Epoch 32/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0110\n",
            "Epoch 33/50\n",
            "20000/20000 [==============================] - 5s 251us/step - loss: 0.0106\n",
            "Epoch 34/50\n",
            "20000/20000 [==============================] - 5s 251us/step - loss: 0.0080\n",
            "Epoch 35/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0082\n",
            "Epoch 36/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0100\n",
            "Epoch 37/50\n",
            "20000/20000 [==============================] - 5s 253us/step - loss: 0.0093\n",
            "Epoch 38/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0102\n",
            "Epoch 39/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0078\n",
            "Epoch 40/50\n",
            "20000/20000 [==============================] - 5s 254us/step - loss: 0.0085\n",
            "Epoch 41/50\n",
            "20000/20000 [==============================] - 5s 271us/step - loss: 0.0091\n",
            "Epoch 42/50\n",
            "20000/20000 [==============================] - 5s 268us/step - loss: 0.0063\n",
            "Epoch 43/50\n",
            "20000/20000 [==============================] - 5s 250us/step - loss: 0.0066\n",
            "Epoch 44/50\n",
            "20000/20000 [==============================] - 5s 251us/step - loss: 0.0071\n",
            "Epoch 45/50\n",
            "20000/20000 [==============================] - 5s 251us/step - loss: 0.0071\n",
            "Epoch 46/50\n",
            "20000/20000 [==============================] - 5s 254us/step - loss: 0.0082\n",
            "Epoch 47/50\n",
            "20000/20000 [==============================] - 6s 276us/step - loss: 0.0041\n",
            "Epoch 48/50\n",
            "20000/20000 [==============================] - 5s 270us/step - loss: 0.0070\n",
            "Epoch 49/50\n",
            "20000/20000 [==============================] - 5s 252us/step - loss: 0.0088\n",
            "Epoch 50/50\n",
            "20000/20000 [==============================] - 5s 251us/step - loss: 0.0035\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f024ada5c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSCev98Yc26G",
        "colab_type": "text"
      },
      "source": [
        "#### t-SNE visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWZ4IrfJjOyV",
        "colab_type": "code",
        "outputId": "413fa616-6465-456d-e2cd-48d30c185aca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import random\n",
        "from keras.models import Model\n",
        "from keras.preprocessing import image\n",
        "np.random.seed(42)\n",
        "\n",
        "arr = train_X[:512]\n",
        "lbl = train_Y[:512]\n",
        "print (arr.shape, lbl.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 28, 28) (512,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X36ykhnXYCDR",
        "colab_type": "code",
        "outputId": "01185f3c-0b70-4c13-fa7a-c7b661f32f4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_13 (InputLayer)           (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_14 (InputLayer)           (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_15 (InputLayer)           (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_10 (Model)                (None, 128)          1198592     input_13[0][0]                   \n",
            "                                                                 input_14[0][0]                   \n",
            "                                                                 input_15[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "triplet_loss_layer (TripletLoss [(None, 128), (None, 0           model_10[1][0]                   \n",
            "                                                                 model_10[2][0]                   \n",
            "                                                                 model_10[3][0]                   \n",
            "==================================================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KE6J8RycZbj7",
        "colab_type": "code",
        "outputId": "b4b4dc16-23a2-4e79-b153-5b3639ab9290",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "model.layers[3].summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_12 (InputLayer)        (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 128)               1179776   \n",
            "_________________________________________________________________\n",
            "norm_layer (Lambda)          (None, 128)               0         \n",
            "=================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zkkq9I2nIeRz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_weights('weights.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cEyUZV_l1sF",
        "colab_type": "code",
        "outputId": "befc04a0-a47a-4bf6-9372-e9e1fd6c1c5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "tmp_model = Model(a_inp, a_embed)\n",
        "tmp_model.summary()\n",
        "tmp_model.load_weights('weights.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_13 (InputLayer)        (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "model_10 (Model)             (None, 128)               1198592   \n",
            "=================================================================\n",
            "Total params: 1,198,592\n",
            "Trainable params: 1,198,592\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5o8JaqhvJBpz",
        "colab_type": "code",
        "outputId": "5c413be4-4844-419e-9fb0-2fe60571a00a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "X_train_trm = tmp_model.predict(train_x.reshape(-1,28,28,1))\n",
        "\n",
        "Classifier_input = Input((128,))\n",
        "Classifier_output = Dense(10, activation='softmax')(Classifier_input)\n",
        "Classifier_model = Model(Classifier_input, Classifier_output)\n",
        "\n",
        "\n",
        "Classifier_model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "Classifier_model.fit(X_train_trm, train_y, epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 1.6081 - acc: 0.8052\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 4s 73us/step - loss: 0.8593 - acc: 0.9072\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.5675 - acc: 0.9113\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.4340 - acc: 0.9136\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.3649 - acc: 0.9162\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 0.3258 - acc: 0.9177\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 5s 88us/step - loss: 0.3019 - acc: 0.9194\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 5s 81us/step - loss: 0.2860 - acc: 0.9207\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.2750 - acc: 0.9218\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.2669 - acc: 0.9227\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f024a913588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIp-KwNGK5cr",
        "colab_type": "code",
        "outputId": "e4669830-7f7f-4481-a266-b7d7ee00d864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "img_arr = arr.reshape(*arr.shape, 1)\n",
        "img_arr.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "p8_C1iE4Rkgd",
        "outputId": "dc0f9273-81bd-4fb5-f48f-fba7470b15af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "preds = tmp_model.predict(img_arr, batch_size=50)\n",
        "print (preds.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 128)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iy5lX34Rc2AK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.patheffects as PathEffects\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Define our own plot function\n",
        "def scatter(x, labels, subtitle=None):\n",
        "    # We choose a color palette with seaborn.\n",
        "    palette = np.array(sns.color_palette(\"hls\", 10))\n",
        "\n",
        "    # We create a scatter plot.\n",
        "    f = plt.figure(figsize=(8, 8))\n",
        "    ax = plt.subplot(aspect='equal')\n",
        "    sc = ax.scatter(x[:,0], x[:,1], lw=0, s=40,\n",
        "                    c=palette[labels.astype(np.int)])\n",
        "    plt.xlim(-25, 25)\n",
        "    plt.ylim(-25, 25)\n",
        "    ax.axis('off')\n",
        "    ax.axis('tight')\n",
        "\n",
        "    # We add the labels for each digit.\n",
        "    txts = []\n",
        "    for i in range(10):\n",
        "        # Position of each label.\n",
        "        xtext, ytext = np.median(x[labels == i, :], axis=0)\n",
        "        txt = ax.text(xtext, ytext, str(i), fontsize=24)\n",
        "        txt.set_path_effects([\n",
        "            PathEffects.Stroke(linewidth=5, foreground=\"w\"),\n",
        "            PathEffects.Normal()])\n",
        "        txts.append(txt)\n",
        "        \n",
        "    if subtitle != None:\n",
        "        plt.suptitle(subtitle)\n",
        "        \n",
        "    plt.savefig(subtitle)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ar4IiWHzLdmG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train_flat = train_x.reshape(-1,784)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lWAbvNQPyuX",
        "colab_type": "code",
        "outputId": "ffcafc6d-6d14-40e6-8bf2-4d1b3d523cda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "tsne = TSNE()\n",
        "train_tsne_embeds = tsne.fit_transform(x_train_flat[:512])\n",
        "scatter(train_tsne_embeds, train_Y[:512], \"Samples from Training Data\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RnbIfZbw+F5+sZOfw9e/AMZlLVOqU/cPFzDnhPhsAm+AHnR3sYuZAWX9aCJFvpGYtwC/t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cDTTz/NxIkTAcLqZuMHbrDyPTHdssqRP/ueSCvBQBDlEKTl3gaId3i5ceknypivHreh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\nxlpRFEVRqhw11oqiKIpS5fw/MXPx1ECr5jQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1F9pBXy2L5dG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqeYhPZe-1jM",
        "colab_type": "code",
        "outputId": "0241564f-584a-40b6-ce20-bffe5b2af4ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "l = np.array(lbl)\n",
        "l[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeqfeAeB-1uW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds_trans = TSNE().fit_transform(preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoIbS6i0L70V",
        "colab_type": "code",
        "outputId": "3ce88ace-5833-4e45-ffcd-01f1af7d5dff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "scatter(preds_trans, train_Y[:512], \"Training Data After TNN\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QTp06AVC4Zmt+mKK71rdOTcw3wO4Q6UsAGzNCtSW1B2cwQTZUWWAekYdTjBmY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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntbEvsKz-14A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds_df = pd.DataFrame(np.concatenate((preds_trans, l[:, None]), axis=1), columns=['x','y','labels'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfyppVdU-2CK",
        "colab_type": "code",
        "outputId": "0169b4f0-4734-475a-ab71-c34388878772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "preds_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-32.830582</td>\n",
              "      <td>-3.567229</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27.243023</td>\n",
              "      <td>-15.246427</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20.956648</td>\n",
              "      <td>9.557454</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.588186</td>\n",
              "      <td>30.425179</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-20.081406</td>\n",
              "      <td>9.931993</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           x          y  labels\n",
              "0 -32.830582  -3.567229     5.0\n",
              "1  27.243023 -15.246427     0.0\n",
              "2  20.956648   9.557454     4.0\n",
              "3  11.588186  30.425179     1.0\n",
              "4 -20.081406   9.931993     9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Id2IgNqWAvDP",
        "colab_type": "code",
        "outputId": "ba27eeaf-6210-45ad-fcfe-eaf291b62816",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "sns.lmplot('x', 'y', data=preds_df, hue='labels', fit_reg=False, legend=False, height=10)\n",
        "plt.grid(visible=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y+XlpYMB9qlBw16Llvvd6bzIAoDWoIGNbWs/4tvv6e/R7B/bKygXjwPN0SzFU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\njXLbEy+Sw0+xJLd9Y0txHMpxPKV/Ly0WR5l7jC1j6dg2x3Gy6nJ+vfR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qoOwnK6lNck/B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\nS5IkSTX/D3t58NK6+titAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WvTiDRNPyqQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDEzAKRPPzfg",
        "colab_type": "text"
      },
      "source": [
        "## Method - 2 (Contrastive Loss)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PgNiwLpRld1e",
        "colab": {}
      },
      "source": [
        "num_images_by_class = []\n",
        "\n",
        "for i in range(len(np.unique(train_Y))):\n",
        "    \n",
        "    num_images_by_class.append(len(train_Y[train_Y == i]))\n",
        "    \n",
        "num_images_by_class = np.array(num_images_by_class)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9bMjp0kPymt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class SampleBatch:\n",
        "    \n",
        "    def __init__(self, num_pairs, batch_size):\n",
        "        self.batch_size = batch_size\n",
        "        self.P = num_pairs\n",
        "        \n",
        "    def get_batch(self):\n",
        "        P_classes = np.random.choice(np.arange(train_y.shape[1]), p=num_images_by_class/num_images_by_class.sum(), size=self.P)\n",
        "\n",
        "        batch_samples = [[] for _ in range(self.P)]\n",
        "        pairs1 = [[] for _ in range(self.P)]\n",
        "        pairs2 = [[] for _ in range(self.P)]\n",
        "\n",
        "\n",
        "        for i in range(len(P_classes)):\n",
        "            lbl = P_classes[i]\n",
        "\n",
        "            same_class_index = np.where(train_Y == lbl)[0]\n",
        "            diff_class_index = np.where(train_Y != lbl)[0]\n",
        "\n",
        "            pos_index = random.sample(list(same_class_index), k=3)\n",
        "            neg_index = random.sample(list(diff_class_index), k=1)\n",
        "\n",
        "            pairs1[i].append(np.expand_dims(np.array(train_x[pos_index[0]]), axis=-1))\n",
        "            pairs1[i].append(np.expand_dims(np.array(train_x[pos_index[1]]), axis=-1))\n",
        "            pairs1[i].append(1)\n",
        "            pairs2[i].append(np.expand_dims(np.array(train_x[pos_index[2]]), axis=-1))\n",
        "            pairs2[i].append(np.expand_dims(np.array(train_x[neg_index[0]]), axis=-1))\n",
        "            pairs2[i].append(0)\n",
        "            pairs1[i] = np.array(pairs1[i])\n",
        "            pairs2[i] = np.array(pairs2[i])\n",
        "            \n",
        "        self.duplets = np.vstack([pairs1, pairs2])\n",
        "        \n",
        "        return self.duplets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T-sy_MQhEovo",
        "colab": {}
      },
      "source": [
        "img1 = Input(shape=(28, 28, 1))\n",
        "img2 = Input(shape=(28, 28, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "c2a19650-4889-4bda-dce8-644fe820b6a0",
        "id": "HrYTBi4fEowH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "siamese_model = siamese_network()\n",
        "siamese_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 4)                 36868     \n",
            "_________________________________________________________________\n",
            "norm_layer (Lambda)          (None, 4)                 0         \n",
            "=================================================================\n",
            "Total params: 55,684\n",
            "Trainable params: 55,684\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FDjDaNjuEowX",
        "colab": {}
      },
      "source": [
        "img1_embed = siamese_model(img1)\n",
        "img2_embed = siamese_model(img2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HIONVqMGYed",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def euclidean_distance(vects):\n",
        "    x, y = vects\n",
        "    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)\n",
        "    return K.sqrt(K.maximum(sum_square, K.epsilon()))\n",
        "\n",
        "\n",
        "def eucl_dist_output_shape(shapes):\n",
        "    shape1, shape2 = shapes\n",
        "    return (shape1[0], 1)\n",
        "\n",
        "\n",
        "def contrastive_loss(y_true, y_pred):\n",
        "    '''Contrastive loss from Hadsell-et-al.'06\n",
        "    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n",
        "    '''\n",
        "    margin = 1\n",
        "    square_pred = K.square(y_pred)\n",
        "    margin_square = K.square(K.maximum(margin - y_pred, 0))\n",
        "    return K.mean(y_true * square_pred + (1 - y_true) * margin_square)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKG3jVbpGYlP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compute_accuracy(y_true, y_pred):\n",
        "    '''Compute classification accuracy with a fixed threshold on distances.\n",
        "    '''\n",
        "    pred = y_pred.ravel() < 0.5\n",
        "    return np.mean(pred == y_true)\n",
        "\n",
        "\n",
        "def accuracy(y_true, y_pred):\n",
        "    '''Compute classification accuracy with a fixed threshold on distances.\n",
        "    '''\n",
        "    return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZD0StdCtEowh",
        "colab": {}
      },
      "source": [
        "distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([img1_embed, img2_embed])\n",
        "\n",
        "# Model that can be trained with anchor, positive negative images\n",
        "model = Model(inputs=[img1, img2], outputs=distance)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "6dcbb340-cf13-44a2-de17-d6f78f182a53",
        "id": "Q-wRAl0lEows",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "model.summary()\n",
        "model.compile(loss=contrastive_loss, optimizer='adam', metrics=[accuracy])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_3 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_2 (Model)                 (None, 4)            55684       input_2[0][0]                    \n",
            "                                                                 input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, 1)            0           model_2[1][0]                    \n",
            "                                                                 model_2[2][0]                    \n",
            "==================================================================================================\n",
            "Total params: 55,684\n",
            "Trainable params: 55,684\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3kWLg3NjEow1",
        "colab": {}
      },
      "source": [
        "batch_size = 128\n",
        "epochs = 10\n",
        "num_pairs = 10000\n",
        "batches = SampleBatch(num_pairs, batch_size)\n",
        "trn_pairs = batches.get_batch()\n",
        "num_samples = num_pairs\n",
        "\n",
        "trn_pairs1 = np.array(list(trn_pairs[:, 0])).reshape((2*num_pairs, 28, 28, 1))\n",
        "trn_pairs2 = np.array(list(trn_pairs[:, 1])).reshape((2*num_pairs, 28, 28, 1))\n",
        "trn_y = trn_pairs[:, 2]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "400849b8-c2bf-44da-9e27-4dd5936091a1",
        "id": "sZc_37OAEow9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "model.fit([trn_pairs1, trn_pairs2], trn_y,\n",
        "          batch_size=batch_size,\n",
        "          epochs=epochs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/10\n",
            "20000/20000 [==============================] - 5s 238us/step - loss: 0.1412 - accuracy: 0.8034\n",
            "Epoch 2/10\n",
            "20000/20000 [==============================] - 2s 99us/step - loss: 0.0810 - accuracy: 0.9120\n",
            "Epoch 3/10\n",
            "20000/20000 [==============================] - 2s 99us/step - loss: 0.0621 - accuracy: 0.9370\n",
            "Epoch 4/10\n",
            "20000/20000 [==============================] - 2s 100us/step - loss: 0.0558 - accuracy: 0.9453\n",
            "Epoch 5/10\n",
            "20000/20000 [==============================] - 2s 99us/step - loss: 0.0500 - accuracy: 0.9551\n",
            "Epoch 6/10\n",
            "20000/20000 [==============================] - 2s 100us/step - loss: 0.0464 - accuracy: 0.9627\n",
            "Epoch 7/10\n",
            "20000/20000 [==============================] - 2s 101us/step - loss: 0.0460 - accuracy: 0.9644\n",
            "Epoch 8/10\n",
            "20000/20000 [==============================] - 2s 99us/step - loss: 0.0484 - accuracy: 0.9625\n",
            "Epoch 9/10\n",
            "20000/20000 [==============================] - 2s 100us/step - loss: 0.0486 - accuracy: 0.9605\n",
            "Epoch 10/10\n",
            "20000/20000 [==============================] - 2s 100us/step - loss: 0.0505 - accuracy: 0.9616\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f8273b2b160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrbBADuBc2lY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Q30i7g8Ya47i"
      },
      "source": [
        "### t-SNE visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "387c0e2d-f4ee-4cbc-d8f7-4f822f93c173",
        "id": "AQxvCp8ka47t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import random\n",
        "from keras.models import Model\n",
        "from keras.preprocessing import image\n",
        "np.random.seed(42)\n",
        "\n",
        "arr = train_X[:512]\n",
        "lbl = train_Y[:512]\n",
        "print (arr.shape, lbl.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 28, 28) (512,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "eb228657-6e80-4f17-a684-d4a25cb435aa",
        "id": "A1_XMrrka48L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_3 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_2 (Model)                 (None, 4)            55684       input_2[0][0]                    \n",
            "                                                                 input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, 1)            0           model_2[1][0]                    \n",
            "                                                                 model_2[2][0]                    \n",
            "==================================================================================================\n",
            "Total params: 55,684\n",
            "Trainable params: 55,684\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d09335c1-9992-49ad-f1f6-d06eb657744b",
        "id": "PvPsVkdGa48b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "model.layers[2].summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 4)                 36868     \n",
            "_________________________________________________________________\n",
            "norm_layer (Lambda)          (None, 4)                 0         \n",
            "=================================================================\n",
            "Total params: 55,684\n",
            "Trainable params: 55,684\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zJYS5FC-a48s",
        "colab": {}
      },
      "source": [
        "model.save_weights('weights.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d3b377f9-d755-4e9a-9487-feec94ea32e5",
        "id": "Hzar55Gla480",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "tmp_model = Model(img1, img1_embed)\n",
        "tmp_model.summary()\n",
        "tmp_model.load_weights('weights.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "model_2 (Model)              (None, 4)                 55684     \n",
            "=================================================================\n",
            "Total params: 55,684\n",
            "Trainable params: 55,684\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e770e032-f63b-425a-e8e1-398cf863bc2e",
        "id": "EvL7KIg4a49N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "img_arr = arr.reshape(*arr.shape, 1)\n",
        "img_arr.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "1fb4d77b-4970-43bb-84dc-11305236b48e",
        "id": "tSuZEqw8a49h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "preds = tmp_model.predict(img_arr, batch_size=50)\n",
        "print (preds.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(512, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2PRdemzga491",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.patheffects as PathEffects\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Define our own plot function\n",
        "def scatter(x, labels, subtitle=None):\n",
        "    # We choose a color palette with seaborn.\n",
        "    palette = np.array(sns.color_palette(\"hls\", 10))\n",
        "\n",
        "    # We create a scatter plot.\n",
        "    f = plt.figure(figsize=(8, 8))\n",
        "    ax = plt.subplot(aspect='equal')\n",
        "    sc = ax.scatter(x[:,0], x[:,1], lw=0, s=40,\n",
        "                    c=palette[labels.astype(np.int)])\n",
        "    plt.xlim(-25, 25)\n",
        "    plt.ylim(-25, 25)\n",
        "    ax.axis('off')\n",
        "    ax.axis('tight')\n",
        "\n",
        "    # We add the labels for each digit.\n",
        "    txts = []\n",
        "    for i in range(10):\n",
        "        # Position of each label.\n",
        "        xtext, ytext = np.median(x[labels == i, :], axis=0)\n",
        "        txt = ax.text(xtext, ytext, str(i), fontsize=24)\n",
        "        txt.set_path_effects([\n",
        "            PathEffects.Stroke(linewidth=5, foreground=\"w\"),\n",
        "            PathEffects.Normal()])\n",
        "        txts.append(txt)\n",
        "        \n",
        "    if subtitle != None:\n",
        "        plt.suptitle(subtitle)\n",
        "        \n",
        "    plt.savefig(subtitle)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1XFfo1bJa498",
        "colab": {}
      },
      "source": [
        "x_train_flat = train_x.reshape(-1,784)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "1ef76cec-a0e3-4c4e-c9d8-eb8cf4e7bdfe",
        "id": "dBdfN60ga4-H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "tsne = TSNE()\n",
        "train_tsne_embeds = tsne.fit_transform(x_train_flat[:512])\n",
        "scatter(train_tsne_embeds, train_Y[:512], \"Samples from Training Data\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qja7FZXHHnLkPOD3Hpmfi2h6RY/9dgC2oyLh+OcexNwN/j8sav5WkjPs1cCJ+\nlrXHI1a/Uk/n3Rv/xJjuhvgEOORZPx3mDKBT1iETbrR65/q4thBCFIuW2Aedb7zvD7kK49q+F3Pm\nc3wQPhZYCnTAB51VwD/i2l4GEHNmAL4fdCdgMnBL5oQmrUnKuNOB+zKKTkoZd2nE6lvqeu6I1a+l\njOuP75ddFLF6AkAEuNy4P+H7hNNP0ouAC+p6TSGEKDYt8Qm6Pz7btmdG8VJgh7i2k/Mck2sc6ovA\naXFtZ4X7rA98BvTL2GcRsGNc26/rqfrNQso3J8+g6iIji4G+EZt73HXSma2B7YDPotquc7Lc5cYN\nBUrx7//jN1o9e13PJYQQxarFPUHHtZ0Rc2Z34Gr8Q9f3+MlIFoRTeX4e13btykExvz5vrlWp9gMy\n+/1OpnJwBugG/B4/7Kc16UPuFcC64vuJP8ssTDqj8EtgnkH45Jt05gHgjHWZfONGq8sosnWmw3We\nt8LffMxv6voIIZq/lpgkBr75eRHQH59w8yr+iW8C8EPMmcx5qXuQO/GoPT7gpG2R51qb17m2zc+s\n8E+2hfiM42yH44evZb7Pp1E1ma/ZSTqjwik5v8PPNvdD0pmLc+y3QdKZs5POXJB0ZrPGrqcQovlp\nqQH6XOB3VLy+dlQsF7keMDrmTHoRhC/JHVQ+jGs7M+Pnt/Jc662YM4NjzrwQc2ZhzJnPYs6YOta/\nqIUzel2GH3aW6ZqI1bmyjXMt5VldeXNyInAOFZ+vjsBfwwlOAEg6sxswFYjjpw51SWeOa+yKCiGa\nl5YaoI+sYXsb/HAs8E/Jn1I52EwHzow5c1LMmbtjzpyHn8XpxazzfIQf1/w6cAi+yXsb4PGYM4fU\n6RUUuYjVo/HTnt6Nn/pyz4jV/8ize74m3+xx4s1RlZEBOcrvxn820kqAu5LOdG6wWgkhmr0W1wcd\nKmRt5vRkEI9SdQanl/GZwiMzyn4P7IZfEzqCf/JO4Pumc/XHnkvhk5o0S2F29YQCdr0fiOGz49NW\nUnmu8+ZqaXXl4ZrS2+XYni5/r4HqJYRo5lpqgH4A2Kea7UuB/8ac2YTczawn45vFM2n8RCVXAWPT\nheGqVbnkm0q01Ylq+2XSmQPxk7Vsh8+yvyKqbY2JXi5hjsIH9x7Ac8BfdWlFkl8ReAA4JqtsBfDv\n8N+Lyb1M6Rp8S40QQuTUIgN0XFsbc2YjfD/phsBMoDP+S/4LfKD9MebMduROEMs3K9VOOcpeBG7K\nUy5CUW3HA7vW5hiXMNljrYfj/w/qrfvA3OC2xc8qtwd+vusb7BW64OlYo9q+mnTmZPw63Jvgs8sv\nimrrwu2rk87cRtXPyKNRbXPy7xEwAAAgAElEQVSuIy6EENBy+6CJa3s7fj3kHnFt++JXquod13ZI\nXNv0FJ+fA9NyHJ5vYYIvsgvi2n6OvxEozyh+DfjrOlZdVLgsR9nBLmG2qY+Tmxvcevj/q33xWfsD\ngX+bG1x1i1ZUEdX2YWAzoEtU222i2iaztt+MT1p8G/gQvzrXaXV+AUKIFq3FTVRSWzGfYfssFU3S\n3+MTfB4BBmXsOgfYKa7td3nO0x/fRz0trm0h/bLC2xi/1OJOwGD8WPOuwLIlsz8ZvOynMrVk9kRW\nLao0QdyhutTWuX/f3ODOwo/PzvaKvULnWolMCCEaTasP0AAxZzri+6xXAa/FtV0dzhx2Dn795snA\nHXFtv2/CarY0bYB78U+SuboZKln602f89MmDrF4yeznQX5faOmeAmxvcZcCNOTal7BU633re6yzs\nh0/PXW6j2j5T39cQQrQcEqBFUzmBikQqli9fzpdffsl3333HokWL6Ny5M9tttx1a67UHlK9czNzP\nRt/cZ8dzczV915q5wQ3Fd3Nku9JeoW+oj2ukJf1QvTuyiq+JanttfV5HCNFySIAWTeUO4LyFCxdy\nwAEH8MEHH7B69eoqO2211Vacf/75xGIxlFKsXv7z4umv/al084PuexXAJcyBwPn4ZMCxwC26tPAl\nPc0N7iJ8Alc6MfAl4Ah7hS5kqF5Bkn4t8h+pmsm9FOgb1faX+rpWY4g5sx5+FrguwLNxbac2cZWE\nACDpzFAgip/pMBHVRTXio9YkQIumcjcQmzNnDhtuuCElJSUMGTKELbfcks6dOzNr1izef/99lizx\nE5Ndcskl3HKLXyhr9kf3BIu+f+M4/HCmp6jcRP6uLrW7ZV/M3ODOBc7D5xq8CPzJXqFnhtv64Cdd\nmWav0J/U9wtN+vyEfEOqto36RMNmIebMTvjEuvQ0uAFwblzbeNPVSghIOvNnILNF6jvg19E8eUPN\ngQTodRDzk0+cjE8imwA8Ftd2RbUHtQIuYXYFBgBv6VKbb9nPtL8DFyxdupRx48axzz770LVr10o7\nzJ8/n7PPPpsnnniCdu3aMXXqVPr378+yeV/zw5t/dsDP5B76tqcutW+kfwiD851Z+3wODLNX6DW1\nfJm1lnSmBP9lkb3YygKgX1TbfJOdFJ2YM19SOXkS/Jju9eK68JYLIepT0pktgW+oms/ySFTbE5ug\nSvWixQ6zKkTMmeExZy6POXN6zJnuBR6zIX6Kz3/gF4D4F/BazDdjtkouYbq7hHkTeAewwHcuYapd\nozkI1rwG0LlzZ0aNGlUlOAOsv/763HXXXbRv355Vq1bxzDM+p6pD9/7gJ47ZLM/pN836+bwc+2yD\nzx5vcFFty4E/UnkoXgBc1syCcweqBmfw3yNnNXJ1hMi0J7mTTfdu5HrUq1YboGPOXA1MxM9udR/w\ndcyZgQUcej5VV7balaqzSTVrSWc6JZ05IOnMyHC5yOpcAeye8XNb4G8uYbbOtbNLmJFTnv3t3+d+\n/iirl82jfMXCRUGw5u1VS376YsHUV1jgXlq7b+/evenTpw9ARR+1agP+/+6NKif3T3PZC5vkm9Wt\n0WZ7i2prge3x4+P/DoyIantvY12/nqyk8k1Gpo6NWREhsuTrQmrWI29a5ExiNYk5sznw56ziPvhk\nocNrOHyHPOU74sdON3tJZ/YBngDWD4smJZ05KJp/mNnBOcoUcBDwdWahS5huwAtAzwVuDAvcGLpt\nule3PtufObJdlw3pucX+lU7yzTffMGOGn3Brxx13BGDVkp8C/BPpdPzNUd+MQ67TpTZ7opmxQPbq\nUSuA/+V5PQ0iqu1nZK2V3ZzEtQ1iznyM/6xnkyFjoim9im/ZzPx+DmjmE0a1ygCNf+LL1XqwRwHH\nfgHsn6N8Uk0Hxnw/yapiHk+d9M2Yj1MRnAGG4JO68i0PmW9M8pEuYS7Br8V9qy61T+KDec+KXRQb\nDDm2yoHl5eW8+eabXHjhhQRBwKhRo9h9d/+QXtK+2wO61I4HcAkzEDgKn8X9si61n+aox8X4OcCH\nhj+vAE63V+iWsJpWYzsK/0WY+fm4M17AvOpCVCfpzOHAJfg8lvH4+foLSvCKarsm6UwU/92+Pz6L\n+x/A2KQzo/BN4N8BD0e1/bkBqt8gWl2SWMyZvfBZqLl8GNd2p3C/7YCtgVTm7GHhAhsfUHkFqy+A\nSFzbXGshpwOzpeLJYzxwbFzb2XV5LQ0h6cye+PplWwN0iGpbZSyUS5gjgSezisupWCM57XD8ilaP\npwtK2ndn84P9dNuXX345jzzyCOXl5fz000+Ul5fTp08fzjzzTK688kratVs7RfpGQK3eO3ODawPs\nhW/W/p+9Qs+tzfHF6pPXTPeVHTkW+Cayq833ua5XMWe64rt0+gHJuLbvN8Z1RZ2U4FubDsAnVm6J\nXwJ1MeDwIxsSNFGTcNKZQ4Hns4qnAYOj2q7zkMekM0/gbyrTfgR2i2o7LdzeBhgGLEzPn19MWuMT\n9MnVbLspTPb6L3BYWLYm5swtcW0vB4hr+33MmR3xy0mms7jvSQfncBjKYcAvwKNxbX/EB6/hGdfZ\nC3iQelz0oR4tyFO+iDz9j7rUPuUS5kT83e8mwFfkzq7+A/41/wysV+XCCxasbc5OW7lyJcuWLWPp\n0qX06NEjXTwcvyRowcJs7UZt0m5oqXfMXxZtzBXl7XxyzBufmdntlrPtrhH7U0NeN67tYvznVxQ/\nhc+buZjKXUFpvYHNgf3wT5yP4n9P863h3lByJZVuBpSScUNfG0n/MHZUVnFf4HLgjKQzO+MfnDYN\n9/8fcHRU28Z+7Xm1xifoxwGTY9NzcW0PizlzPnB7ju27x7V9u4ZzZ08duRg4ndwfsABYP65tvoDY\nZJLOvAfsnFX816i2fyrkeJfIOWsWwNe61A50CTMSn/2+FaqkfItD/tWmTdsOaunSpaxcuZLy8nJm\nzpxJMpnktttuY+bMmQwaNIj3338/HaQ/wK/J3Wp9+j+zxZz+TFmT1UbRaRETdx9u8+VJiBqErQPb\nA9PjukouQ3N0DXB1+oc1a9ZQVlbGpEmTWLBgAd26dWPkyJFsttlmmcd8hW8SntVYlUw6Mwn4VY5N\nF0a1/cc6nvNu/FK12T7Ht2ZOw68FkKmohmW1xizu/+YpvzX8O1fCE+TvfwUg5kwfKg+SBz+Zw8V5\nDgnCP8VoFPAYsBy/SMiN+L6dtVzCKJcwl7mE+dElzEqXMAmXMOnhTa+Q+7W9DKBL7dv4PuGvCMpL\nfpkyVoEfctWzZ0969erF0KFDufDCC5kwYQJdunRh8uTJ3H772vumnaiaSd+qLO/EH7ODM8CKTmzX\n+LVpGWLOnAj8gB8dMCXmjG0BwyePBfjqq6847bTT6NOnD9tttx2//e1vicVinHDCCWy++eYMHz6c\n559f28I8EHi4kev5Si3LqxUuAXt2ns1f4UedZAdnqPrE3aRaXRN3XNtnY878BR84O+Kfcv8c1/ad\ncJd8zRs1JRTtQO51pIfjm8G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kfTiGysEZ/BP+WVQsd9rSXYOflvGw8Oc5wClxbevypPoa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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aV4tc0eya4-a",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "eed1f959-7d92-4d5f-e954-5a22276394cc",
        "id": "aVL0JVlRa4-e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "l = np.array(lbl)\n",
        "l[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4j-knhoka4-q",
        "colab": {}
      },
      "source": [
        "preds_trans = TSNE().fit_transform(preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "29631dd8-e791-49c3-840a-da54a121f2a6",
        "id": "LOXSWxAka4-v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "scatter(preds_trans, train_Y[:512], \"Training Data After TNN\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EtoiIVIECWsrzK9Dziy++YMqUKaxduxa/31+kQ3R0NGeccQYPPvggJ554YrD5ceDWOq5V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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JTvvj_6Ja4_B",
        "colab": {}
      },
      "source": [
        "preds_df = pd.DataFrame(np.concatenate((preds_trans, l[:, None]), axis=1), columns=['x','y','labels'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "bc7fc8f4-3cd9-45a9-8eb9-c0b9171d38f0",
        "id": "MSryEW1-a4_W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "preds_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.646187</td>\n",
              "      <td>4.600009</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29.978218</td>\n",
              "      <td>-7.271741</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.266596</td>\n",
              "      <td>32.423031</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-41.041939</td>\n",
              "      <td>-5.710828</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-12.677921</td>\n",
              "      <td>-17.887129</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           x          y  labels\n",
              "0  -1.646187   4.600009     5.0\n",
              "1  29.978218  -7.271741     0.0\n",
              "2   1.266596  32.423031     4.0\n",
              "3 -41.041939  -5.710828     1.0\n",
              "4 -12.677921 -17.887129     9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "bca71801-5e3f-45d9-f577-75b6a4cbe1ac",
        "id": "ulyLGFsca4_y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "sns.lmplot('x', 'y', data=preds_df, hue='labels', fit_reg=False, legend=False, height=10)\n",
        "plt.grid(visible=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MJpHQG0dG9O+NDfL8QKnWVuXzeQVBoEsvvXShfyUAADBPjHnDitWz/S4d2769/HAsyRij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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7hz_VkiFa4__",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}