triplet_loss_in_defense.ipynb

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        "<a href=\"https://colab.research.google.com/github/ak-org/notebooks/blob/master/triplet_loss_in_defense.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "height": 545
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        "outputId": "dfd294d8-c713-4a4b-fc68-2e7f62177803"
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-rc1"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0.0-rc1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/73/cf/2fc69ba3e59edc8333e2676fa71b40197718dea7dc1282c79955cf6b2acb/tensorflow_gpu-2.0.0rc1-cp36-cp36m-manylinux2010_x86_64.whl (380.5MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bc/88/24b5fb7280e74c7cf65bde47c171547fd02afb3840cff41bcbe9270650f5/tb_nightly-1.15.0a20190806-py3-none-any.whl (4.3MB)\n",
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            "Installing collected packages: tf-estimator-nightly, tb-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.15.0a20190806 tensorflow-gpu-2.0.0rc1 tf-estimator-nightly-1.14.0.dev2019080601\n"
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          "name": "stdout"
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      "metadata": {
        "id": "vTlq__uhMUNR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6ded4818-a75e-4f35-926b-25df6e2646ac"
      },
      "source": [
        "import tensorflow as tf \n",
        "from tensorflow import keras\n",
        "import math\n",
        "import sklearn\n",
        "import random\n",
        "\n",
        "print(tf.__version__)\n",
        "print(keras.__version__)"
      ],
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-rc1\n",
            "2.2.4-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KXmuYT-wNPNX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## dataset\n",
        "from tensorflow.keras.datasets import mnist, fashion_mnist, cifar10\n",
        "\n",
        "## for Model definition/training\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, concatenate,  Dropout\n",
        "from tensorflow.keras.layers import Input, Flatten, Dense, concatenate, BatchNormalization\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "## required for semi-hard triplet loss:\n",
        "from tensorflow.python.ops import array_ops\n",
        "from tensorflow.python.ops import math_ops\n",
        "from tensorflow.python.framework import dtypes\n",
        "\n",
        "\n",
        "## for visualizing \n",
        "import matplotlib.pyplot as plt, numpy as np\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6y9Fs6qeNFhf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pairwise_distance(feature, squared=False):\n",
        "    \"\"\"Computes the pairwise distance matrix with numerical stability.\n",
        "\n",
        "    output[i, j] = || feature[i, :] - feature[j, :] ||_2\n",
        "\n",
        "    Args:\n",
        "      feature: 2-D Tensor of size [number of data, feature dimension].\n",
        "      squared: Boolean, whether or not to square the pairwise distances.\n",
        "\n",
        "    Returns:\n",
        "      pairwise_distances: 2-D Tensor of size [number of data, number of data].\n",
        "    \"\"\"\n",
        "    pairwise_distances_squared = math_ops.add(\n",
        "        math_ops.reduce_sum(math_ops.square(feature), axis=[1], keepdims=True),\n",
        "        math_ops.reduce_sum(\n",
        "            math_ops.square(array_ops.transpose(feature)),\n",
        "            axis=[0],\n",
        "            keepdims=True)) - 2.0 * math_ops.matmul(feature,\n",
        "                                                    array_ops.transpose(feature))\n",
        "\n",
        "    # Deal with numerical inaccuracies. Set small negatives to zero.\n",
        "    pairwise_distances_squared = math_ops.maximum(pairwise_distances_squared, 0.0)\n",
        "    # Get the mask where the zero distances are at.\n",
        "    error_mask = math_ops.less_equal(pairwise_distances_squared, 0.0)\n",
        "\n",
        "    # Optionally take the sqrt.\n",
        "    if squared:\n",
        "        pairwise_distances = pairwise_distances_squared\n",
        "    else:\n",
        "        pairwise_distances = math_ops.sqrt(\n",
        "            pairwise_distances_squared + math_ops.to_float(error_mask) * 1e-16)\n",
        "\n",
        "    # Undo conditionally adding 1e-16.\n",
        "    pairwise_distances = math_ops.multiply(\n",
        "        pairwise_distances, math_ops.to_float(math_ops.logical_not(error_mask)))\n",
        "\n",
        "    num_data = array_ops.shape(feature)[0]\n",
        "    # Explicitly set diagonals to zero.\n",
        "    mask_offdiagonals = array_ops.ones_like(pairwise_distances) - array_ops.diag(\n",
        "        array_ops.ones([num_data]))\n",
        "    pairwise_distances = math_ops.multiply(pairwise_distances, mask_offdiagonals)\n",
        "    return pairwise_distances"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AI13HibhM2Zs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def masked_maximum(data, mask, dim=1):\n",
        "    \"\"\"Computes the axis wise maximum over chosen elements.\n",
        "\n",
        "    Args:\n",
        "      data: 2-D float `Tensor` of size [n, m].\n",
        "      mask: 2-D Boolean `Tensor` of size [n, m].\n",
        "      dim: The dimension over which to compute the maximum.\n",
        "\n",
        "    Returns:\n",
        "      masked_maximums: N-D `Tensor`.\n",
        "        The maximized dimension is of size 1 after the operation.\n",
        "    \"\"\"\n",
        "    axis_minimums = math_ops.reduce_min(data, dim, keepdims=True)\n",
        "    masked_maximums = math_ops.reduce_max(\n",
        "        math_ops.multiply(data - axis_minimums, mask), dim,\n",
        "        keepdims=True) + axis_minimums\n",
        "    return masked_maximums"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eszgoDh7NonO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def masked_minimum(data, mask, dim=1):\n",
        "    \"\"\"Computes the axis wise minimum over chosen elements.\n",
        "\n",
        "    Args:\n",
        "      data: 2-D float `Tensor` of size [n, m].\n",
        "      mask: 2-D Boolean `Tensor` of size [n, m].\n",
        "      dim: The dimension over which to compute the minimum.\n",
        "\n",
        "    Returns:\n",
        "      masked_minimums: N-D `Tensor`.\n",
        "        The minimized dimension is of size 1 after the operation.\n",
        "    \"\"\"\n",
        "    axis_maximums = math_ops.reduce_max(data, dim, keepdims=True)\n",
        "    masked_minimums = math_ops.reduce_min(\n",
        "        math_ops.multiply(data - axis_maximums, mask), dim,\n",
        "        keepdims=True) + axis_maximums\n",
        "    return masked_minimums"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-aWEQGGNsNH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def triplet_loss_adapted_from_tf(y_true, y_pred):\n",
        "    del y_true\n",
        "    margin = 1.\n",
        "    labels = y_pred[:, :1]\n",
        "\n",
        " \n",
        "    labels = tf.cast(labels, dtype='int32')\n",
        "\n",
        "    embeddings = y_pred[:, 1:]\n",
        "\n",
        "    ### Code from Tensorflow function [tf.contrib.losses.metric_learning.triplet_semihard_loss] starts here:\n",
        "    \n",
        "    # Reshape [batch_size] label tensor to a [batch_size, 1] label tensor.\n",
        "    # lshape=array_ops.shape(labels)\n",
        "    # assert lshape.shape == 1\n",
        "    # labels = array_ops.reshape(labels, [lshape[0], 1])\n",
        "\n",
        "    # Build pairwise squared distance matrix.\n",
        "    pdist_matrix = pairwise_distance(embeddings, squared=True)\n",
        "    # Build pairwise binary adjacency matrix.\n",
        "    adjacency = math_ops.equal(labels, array_ops.transpose(labels))\n",
        "    # Invert so we can select negatives only.\n",
        "    adjacency_not = math_ops.logical_not(adjacency)\n",
        "\n",
        "    # global batch_size  \n",
        "    batch_size = array_ops.size(labels) # was 'array_ops.size(labels)'\n",
        "\n",
        "    # Compute the mask.\n",
        "    pdist_matrix_tile = array_ops.tile(pdist_matrix, [batch_size, 1])\n",
        "    mask = math_ops.logical_and(\n",
        "        array_ops.tile(adjacency_not, [batch_size, 1]),\n",
        "        math_ops.greater(\n",
        "            pdist_matrix_tile, array_ops.reshape(\n",
        "                array_ops.transpose(pdist_matrix), [-1, 1])))\n",
        "    mask_final = array_ops.reshape(\n",
        "        math_ops.greater(\n",
        "            math_ops.reduce_sum(\n",
        "                math_ops.cast(mask, dtype=dtypes.float32), 1, keepdims=True),\n",
        "            0.0), [batch_size, batch_size])\n",
        "    mask_final = array_ops.transpose(mask_final)\n",
        "\n",
        "    adjacency_not = math_ops.cast(adjacency_not, dtype=dtypes.float32)\n",
        "    mask = math_ops.cast(mask, dtype=dtypes.float32)\n",
        "\n",
        "    # negatives_outside: smallest D_an where D_an > D_ap.\n",
        "    negatives_outside = array_ops.reshape(\n",
        "        masked_minimum(pdist_matrix_tile, mask), [batch_size, batch_size])\n",
        "    negatives_outside = array_ops.transpose(negatives_outside)\n",
        "\n",
        "    # negatives_inside: largest D_an.\n",
        "    negatives_inside = array_ops.tile(\n",
        "        masked_maximum(pdist_matrix, adjacency_not), [1, batch_size])\n",
        "    semi_hard_negatives = array_ops.where(\n",
        "        mask_final, negatives_outside, negatives_inside)\n",
        "\n",
        "    loss_mat = math_ops.add(margin, pdist_matrix - semi_hard_negatives)\n",
        "\n",
        "    mask_positives = math_ops.cast(\n",
        "        adjacency, dtype=dtypes.float32) - array_ops.diag(\n",
        "        array_ops.ones([batch_size]))\n",
        "\n",
        "    # In lifted-struct, the authors multiply 0.5 for upper triangular\n",
        "    #   in semihard, they take all positive pairs except the diagonal.\n",
        "    num_positives = math_ops.reduce_sum(mask_positives)\n",
        "\n",
        "    semi_hard_triplet_loss_distance = math_ops.truediv(\n",
        "        math_ops.reduce_sum(\n",
        "            math_ops.maximum(\n",
        "                math_ops.multiply(loss_mat, mask_positives), 0.0)),\n",
        "        num_positives,\n",
        "        name='triplet_semihard_loss')\n",
        "    \n",
        "    ### Code from Tensorflow function semi-hard triplet loss ENDS here.\n",
        "    return semi_hard_triplet_loss_distance"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWk2LacUN4Zg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cifar10_labels = ['Airplanes', 'Automobiles', 'Birds', 'Cats', 'Deers', 'Dogs', 'Frogs', 'Horse', 'Ship', 'Truck']\n",
        "\n",
        "# The data, split between train and test sets\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "#z-score\n",
        "mean = np.mean(x_train,axis=(0,1,2,3))\n",
        "std = np.std(x_train,axis=(0,1,2,3))\n",
        "x_train = (x_train-mean)/(std+1e-7)\n",
        "x_test = (x_test-mean)/(std+1e-7)\n",
        "input_image_shape = (32, 32, 3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmSwPac1NC42",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_base_network(image_input_shape = (32, 32, 3), embedding_size = 64):\n",
        "    \"\"\"\n",
        "    Base network to be shared (eq. to feature extraction).\n",
        "    \"\"\"\n",
        "    input_image = Input(shape=image_input_shape)\n",
        "    weight_decay = 1e-4\n",
        "    actv = 'elu'\n",
        "    x = Conv2D(32, kernel_size=3, kernel_regularizer=regularizers.l2(weight_decay), activation=actv)(input_image)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Conv2D(32, kernel_size=3, kernel_regularizer=regularizers.l2(weight_decay), activation=actv)(x)   \n",
        "    x = BatchNormalization()(x) \n",
        "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = Dropout(0.2)(x)\n",
        "\n",
        "    x = Conv2D(64, kernel_size=3, kernel_regularizer=regularizers.l2(weight_decay), activation=actv)(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Conv2D(64, kernel_size=3, kernel_regularizer=regularizers.l2(weight_decay), activation=actv)(x)   \n",
        "    x = BatchNormalization()(x) \n",
        "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = Dropout(0.3)(x)\n",
        "\n",
        "    x = Conv2D(128, kernel_size=3, kernel_regularizer=regularizers.l2(weight_decay), activation=actv)(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Conv2D(128, kernel_size=3, kernel_regularizer=regularizers.l2(weight_decay), activation=actv)(x)   \n",
        "    x = BatchNormalization()(x) \n",
        "    #x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = Dropout(0.4)(x)\n",
        "\n",
        "    x = Flatten()(x)\n",
        "    x = Dense(128, activation='relu')(x)\n",
        "    x = Dense(128, activation='relu')(x)\n",
        "    x = Dense(embedding_size)(x)\n",
        "    base_network = Model(inputs=input_image, outputs=x)\n",
        "    return base_network"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvOmq3NVcDQI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "714eac2a-6a07-42f1-e99e-5c8f3a2fa494"
      },
      "source": [
        "create_base_network()"
      ],
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.engine.training.Model at 0x7f59b9871668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aZ3CyQ1Wd2K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def step_decay(epoch):\n",
        "   initial_lrate = 0.0001\n",
        "   drop = 0.5\n",
        "   epochs_drop = 10.0\n",
        "   lrate = initial_lrate * math.pow(drop,  \n",
        "           math.floor((1+epoch)/epochs_drop))\n",
        "   return lrate\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvjFKyJCORMz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "54a51605-aa67-4c01-e7a9-8aa03b71f3fe"
      },
      "source": [
        " if train_flag == True:\n",
        "        batch_size = 128\n",
        "        epochs = 25\n",
        "        train_flag = True  # either     True or False\n",
        "        embedding_size = 64\n",
        "        no_of_components = 2  # for visualization -> PCA.fit_transform()\n",
        "        step = 10\n",
        "        lr = 0.0001\n",
        "        base_network = create_base_network()\n",
        "\n",
        "        input_images = Input(shape=input_image_shape, name='input_image') # input layer for images\n",
        "        input_labels = Input(shape=(1,), name='input_label')    # input layer for labels\n",
        "        embeddings = base_network([input_images])               # output of network -> embeddings\n",
        "        labels_plus_embeddings = concatenate([input_labels, embeddings])  # concatenating the labels + embeddings\n",
        "\n",
        "        # Defining a model with inputs (images, labels) and outputs (labels_plus_embeddings)\n",
        "        model = Model(inputs=[input_images, input_labels],\n",
        "                      outputs=labels_plus_embeddings)\n",
        "\n",
        "        model.summary()\n",
        "\n",
        "        # train session\n",
        "        opt = Adam(lr=lr, epsilon = 1e-7)  # choose optimiser. RMS is good too!\n",
        "        #opt  = RMSprop(lr=lr,decay=1e-6)\n",
        "\n",
        "        model.compile(loss=triplet_loss_adapted_from_tf,\n",
        "                      optimizer=opt)\n",
        "\n",
        "        filepath = \"semiH_trip_CIFAR_v13_ep{epoch:02d}_BS%d.hdf5\" % batch_size\n",
        "        checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=False, period=25)\n",
        "        lrate = LearningRateScheduler(step_decay)\n",
        "        callbacks_list = [checkpoint, lrate]\n",
        "\n",
        "        # Uses 'dummy' embeddings + dummy gt labels. Will be removed as soon as loaded, to free memory\n",
        "        dummy_gt_train = np.zeros((len(x_train), embedding_size + 1))\n",
        "        dummy_gt_val = np.zeros((len(x_val), embedding_size + 1))\n",
        "\n",
        "        x_train = np.reshape(x_train, (len(x_train), x_train.shape[1], x_train.shape[2],  x_train.shape[3]))\n",
        "        x_val = np.reshape(x_val, (len(x_val), x_train.shape[1], x_train.shape[2], x_train.shape[3]))\n",
        "\n",
        "        H = model.fit(\n",
        "            x=[x_train,y_train],\n",
        "            y=dummy_gt_train,\n",
        "            batch_size=batch_size,\n",
        "            epochs=epochs,\n",
        "            validation_data=([x_val, y_val], dummy_gt_val),\n",
        "            callbacks=callbacks_list)\n",
        "        plt.figure(figsize=(8,8))\n",
        "        plt.plot(H.history['loss'], label='training loss')\n",
        "        plt.plot(H.history['val_loss'], label='validation loss')\n",
        "        plt.legend()\n",
        "        plt.title('Train/validation loss')\n",
        "        plt.show()\n",
        "else:\n",
        "        model = load_model('semiH_trip_CIFAR_v13_ep25_BS256.hdf5',\n",
        "                            custom_objects={'triplet_loss_adapted_from_tf':triplet_loss_adapted_from_tf})"
      ],
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_93\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_image (InputLayer)        [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_label (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_92 (Model)                (None, 64)           330080      input_image[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_35 (Concatenate)    (None, 65)           0           input_label[0][0]                \n",
            "                                                                 model_92[1][0]                   \n",
            "==================================================================================================\n",
            "Total params: 330,080\n",
            "Trainable params: 329,184\n",
            "Non-trainable params: 896\n",
            "__________________________________________________________________________________________________\n",
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of samples seen.\n",
            "Train on 50000 samples, validate on 5000 samples\n",
            "Epoch 1/25\n",
            "50000/50000 [==============================] - 9s 174us/sample - loss: 0.4761 - val_loss: 0.2878\n",
            "Epoch 2/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.2668 - val_loss: 0.1952\n",
            "Epoch 3/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.2230 - val_loss: 0.1756\n",
            "Epoch 4/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1859 - val_loss: 0.1634\n",
            "Epoch 5/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1706 - val_loss: 0.1672\n",
            "Epoch 6/25\n",
            "50000/50000 [==============================] - 6s 119us/sample - loss: 0.1588 - val_loss: 0.1669\n",
            "Epoch 7/25\n",
            "50000/50000 [==============================] - 6s 119us/sample - loss: 0.1542 - val_loss: 0.1287\n",
            "Epoch 8/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1398 - val_loss: 0.1304\n",
            "Epoch 9/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1327 - val_loss: 0.1149\n",
            "Epoch 10/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1287 - val_loss: 0.1349\n",
            "Epoch 11/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1216 - val_loss: 0.1286\n",
            "Epoch 12/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1199 - val_loss: 0.1250\n",
            "Epoch 13/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1218 - val_loss: 0.1351\n",
            "Epoch 14/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1254 - val_loss: 0.1283\n",
            "Epoch 15/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1170 - val_loss: 0.1198\n",
            "Epoch 16/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1205 - val_loss: 0.1068\n",
            "Epoch 17/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1132 - val_loss: 0.1218\n",
            "Epoch 18/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1108 - val_loss: 0.1076\n",
            "Epoch 19/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1075 - val_loss: 0.1039\n",
            "Epoch 20/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1127 - val_loss: 0.1074\n",
            "Epoch 21/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1091 - val_loss: 0.1020\n",
            "Epoch 22/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1076 - val_loss: 0.1034\n",
            "Epoch 23/25\n",
            "50000/50000 [==============================] - 6s 117us/sample - loss: 0.1094 - val_loss: 0.1040\n",
            "Epoch 24/25\n",
            "50000/50000 [==============================] - 6s 118us/sample - loss: 0.1089 - val_loss: 0.1046\n",
            "Epoch 25/25\n",
            "49920/50000 [============================>.] - ETA: 0s - loss: 0.1041\n",
            "Epoch 00025: saving model to semiH_trip_CIFAR_v13_ep25_BS128.hdf5\n",
            "50000/50000 [==============================] - 6s 119us/sample - loss: 0.1041 - val_loss: 0.0996\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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fH0/Xrl2rLclZk/Xr1/PAAw8wb9480tLSuOaaa+p0nkqBlgCtyTvvvMOnn37KW2+9xX33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\nlFJKRQgNbaWUUipCaGgrpZRSEeL/AYKv24wwI0RfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFMEnFALmogZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ira76cxpR9Ru",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# creating an empty network\n",
        "testing_embeddings = create_base_network(input_image_shape,\n",
        "                                          embedding_size=embedding_size)\n",
        "x_embeddings_before_train = testing_embeddings.predict(np.reshape(x_test, (len(x_test), 32, 32, 3)))\n",
        "# Grabbing the weights from the trained network\n",
        "for layer_target, layer_source in zip(testing_embeddings.layers, model.layers[2].layers):\n",
        "    weights = layer_source.get_weights()\n",
        "    layer_target.set_weights(weights)\n",
        "    del weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFlNJ-ajSELg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1b517c32-deb8-41d9-852b-687668b0d3ad"
      },
      "source": [
        "\n",
        "# Visualizing the effect of embeddings -> using PCA!\n",
        "\n",
        "x_embeddings = testing_embeddings.predict(np.reshape(x_test, (len(x_test), 32, 32, 3)))\n",
        "dict_embeddings = {}\n",
        "dict_gray = {}\n",
        "test_class_labels = np.unique(np.array(y_test))\n",
        "print(x_embeddings.shape, test_class_labels.shape)\n",
        "\n"
      ],
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 64) (10,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDciXenWjgOk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "outputId": "cdde7083-a7a7-4418-ba25-c6128fd1490f"
      },
      "source": [
        "\n",
        "dist = sklearn.metrics.pairwise_distances(x_embeddings)\n",
        "print(\"Random Test\")\n",
        "r = random.randint(0, 10001) \n",
        "plt.figure(figsize = (2,2))\n",
        "plt.imshow(x_test[r])\n",
        "print(\"Refernce image is : \" + cifar10_labels[y_test[r][0]])\n",
        "idx = np.argpartition(dist[r], 5)\n",
        "print(dist[r][idx[:5]])\n",
        "\n",
        "print(\"Nearest images are  : \" + cifar10_labels[y_test[idx[i]][0]]) \n",
        "for i in range(5):\n",
        "  print(cifar10_labels[y_test[idx[i]][0]]) \n",
        "\n",
        "\n",
        "plt.figure(figsize = (10, 2))\n",
        "for i in range(5):\n",
        "  plt.subplot(1,5,i+1)\n",
        "  plt.imshow(x_test[idx[i]])\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.tight_layout()\n",
        "plt.suptitle('Closest 5 images', fontsize = 12)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "farthest_idx = np.argmax(dist[r])\n",
        "print(\"Farthest image distance is {}\".format(dist[r][farthest_idx]))\n",
        "print(\"Labels are {}, {}\". format(cifar10_labels[y_test[r][0]], cifar10_labels[y_test[farthest_idx][0]]))\n",
        "far_idx = [r, farthest_idx]\n",
        "plt.figure(figsize = (8, 2))\n",
        "for i in range(2):\n",
        "  plt.subplot(1,5,i+1)\n",
        "  plt.imshow(x_test[far_idx[i]])\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.tight_layout()\n",
        "plt.suptitle('Farthest image', fontsize = 12)\n",
        "plt.show()"
      ],
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Random Test\n",
            "Refernce image is : Ship\n",
            "[1.6384755 0.        1.5449396 1.606284  1.4066887]\n",
            "Nearest images are  : Ship\n",
            "Ship\n",
            "Ship\n",
            "Ship\n",
            "Ship\n",
            "Ship\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m/nRCKlklNrNtJME620YSTJGMJJgiGUkwRTKSYIpkJMEUyUiCKZKRBFMkIwn/BwdnTs5O\nUkDrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xLr1Q7aC7gHkYBCyn1BBV7r5Wg5suHWEN3+MqMloqhX23ZcfHo/MsTpdUuVaHwCRXI/xi\nSwghhBBCYgEXtoQQQgghJBZwYUsIIYQQQmLBpQlcGj0adG8FcEmkZTZF0DeiXkh7QGkPaBbRWw+6\n3tL1oIavSu6YWDgtEr4El+L0JlQvugpbTKMtd/e1RkRuUXZIt6q1j82Eya0Xhlbn90BeukY5EZEz\nYGu9Kepxc2Dr/JBeGDW/oXOpF363VhsIegtCHW1GpTcH6mkBiTmRtJZ+qjki5IGonubZRBSuCjWK\nFUcTQRdc84SaiJJw8zBMajER5aPLpiQMUqvlrH3z0KVUEcomlZ9E1C0WYILrMIJh1FSa2MG2zSr/\naDWMttbOLqtWrZJNmeiBTgVcCYZcI6a6QMOq7ltPD4TQhWenWIwe9OyJrMnT9xTvBboR0xpX1Lei\nHjeXw8mmRj0J2/cihCnXbs50qFvME7H9z8Hzoi9tT8JOsCBJlnxehQ6ejK6P963+sQe5GuEXW0II\nIYQQEgu4sCWEEEIIIbGAC1tCCCGEEBILmtLYegHZlZLjoPbUhLYEndN41mp1kkoklS9YjU9vn9UZ\n6aqK2KYqirq8DaCNHFDHZkGjNWZNmdDnCXnaY+Q05KE3x1mVzoUjZMpm3X+Qtk0E/JFW+fGsoQ92\nrRbZrhar2dTng/3P10iLhP27on9TtPUFx3p1PRkJo7WovZB3B9h6LD0NeTiwNFmw9XljiF8MzavP\nO3S9kJAm+WjguBZQuiCSO6G6EjgnE9YbxhJIUSWvHsrkGAwCeO5zG6NJAv3NJvVYAl1ieo2dXEpK\nBF/C0LdVPnmVdrcqFK4+OfCrCrpe7ecW9bcwpcqkvkhV0XajvAIcmCjWDkFrH7bW+rGdmZmVU+PR\n9ZgsRDcSW55QLwL0TVuoGlcqLC0Id/F9klIvGx3SF9spTBZq5olYHW21xha1slFduTO2Hv1MdK1F\ngTiEfD6j+gTjHP3a6mMLWFaNFQwVXCrZZ0Jri7VenCF1SSvgF1tCCCGEEBILuLAlhBBCCCGxoCkp\nghMIYanTsMtXCkgRsseGjN2zKVNJj52w+6x9vQPGNuF4YXtorUrfBtu3oWiihdGDxp5IWT9I67qj\nbRXcvdX1ohThJFyTGwPhVnF3WV9nDBs6oWzcequK4qp2D/U2k59tsZuVObFb5DodctHUjGsrBI/V\nO3lrIU+570HXNGfAnkIZQ602RKRfZ73T5h3QBm5JoyefrErjef0YbP04NXO9UBqh+7DMkS5n5kTG\n1TUp5GvrnEyI2Ly9kOPowks9PIPFcIzj0VPRBchCSN0udUGSCbvNmkB3XyYccO0QuiILbP2aPPPw\nmrwkuN6yEi2Ub8EgVfnYuu4fyiY8jlnTqMqcbe328vT0BRk9Fg3ehD6fqq30KFkC3UppurZMAF1t\nFWFcaZlA11o7HrS7rRI+6OCKS/c2NwFb/TCuCuoFU+XuTU8S6GIOXWaWoj+Mn7LnhVKeVFLJcxJw\nDQJzBLpA0+Oqe2Omkm5vX+aJhlwV8IstIYQQQgiJBVzYEkIIIYSQWMCFLSGEEEIIiQVNCVzavHXj\nFQpXqGVwo2NWrzYydMiWLUYxVQsQNjBVtBrbdlXVdnSDFBLSBigdecnY626yl6UnFfVhZMxqpvqV\nO7IXc6BXGrbnMpBS4s1u28ZBQVc0SreFLnm09E7CaG2T0di2OpShl7CuVqPLYXhMPC5UZwbskBsx\n5bYLI8tmwVYyMxmGPKx3SJUdhKIPqfQojlXQ9e7EEMCBsqYPqNVt9B5cZlZfI5K5ObIL+doPs5F2\nQrkk6GhzpyItZnHczkO9MGHo+zXenbFtKrddpRLqL8FWPUyk7HyR7EL3SgunRaxWMwE3L5HH8KY6\npC6EyYWHymiUodGCineOLhyL4LqspASZM/o021v7vaStLSGpVCSS1266QhpmPJ+xHIa3VXMlaGEL\n4KYrX4oetGLBamzTG6K+FfL2gezqAjdY6n0Ht1Ry6G5NnQBqgA8ORcL73AvPm7xt228xdkn5yawO\nVw1jR10TDA+cV+dWgIpQ66z7m1euwWZnLyXwPCELwy+2hBBCCCEkFnBhSwghhBBCYgEXtoQQQggh\nJBY0pbpr917WlhbWbCITSm96cO9uk/fSC88ZOzcaKRd7uq0G6bbMvcZepIw2SP89H2m47NFDzxp7\nqHBjJZ0DxWsCHdmOR3q//Kh1RvpsYcTYfb/9vkp6U98WW28JHUpG5Au2zZQOy6judlurQ+oiXTXS\nInYU4pBKgx2SZGG92gYh7UO1s2Qf2PsDTVa1qTgYsK+FPIzMGwS15VpCdwzyQn5tQ/55W/GgBUis\nEkkr7bCWo+Mkpacd9LXaBf3e82Sk53vmGTsP3Z22F3Lwvtsr6dxNNnayVkpWPX4wJu20iKJxO6CL\nqjDOp91pdTLQRm4U/fXqkLr2ImDo2NykykORpdHngl41j35Wta9hFVK3xXPLq6++Il9+7AsVO6n6\nnOnNmLKZTHQfe3o3mbzuDXAvlF9bfLWl0/ZB19cts9GOo7wKo5tRPtpFbGjZ+b5H983cb6me6lLK\ncWxyjS3b3RUFdz9y0L5LZOtNxtR67XQKfTKnwI7aKVT58tW6bgwdbceODlGsx/xcq3/rQa5K+MWW\nEEIIIYTEAi5sCSGEEEJILGjOAVBpSiQXbXOAAxpj5Y4draSH91vpwczofmMPn1xfST/wpc+ZPNzI\n0+0//OBvmayuVNSHFMT7wy3KlNpGwa26dUm7tbTtI/+7kr7j/nuwRxV2f/0pYx/Yac97/Gi093vg\n7Asmz1oi/cr3y5e+8SWbqVzNjIMLo+yJE8becuPmSjqv6py5MCktZVbsNrjemQqFvg25qxKxW/+4\nlf4k2OPRjb374+DCTaXxjqL9G09HnejZAe6k4Fx0d0f3QkUqrO958AW2Ry4BPVwnIE9fI5RNjAbq\nXGYpApKokRaxbpuSVROEZfuOKMhxcdjKf5778oPG3rYto9qw8p9a0aEX6qANqWvzutMQYle5dMKt\n3lw2KpuGMKkpGPy5U5ENUYalAB3M6zbBddmkcuGFYWQLcDK+qGK8an2Gn5VWMn3utIy+8NiCeVXu\n+AzrjXXtoBUA7Xjb2yrp3t43mLwtm61gaUM6uh8YkvvoSG29FD5WOeX6KgEuvHLg/6uoZGZdIEV4\n773bK+lMj33QuzdYOz9RW8pWLbOp7S4N3cpp0B2Zltnod2+bW25NHLka4BdbQgghhBASC7iwJYQQ\nQgghsYALW0IIIYQQEgua0tie+/lp2fvUVyu21mze9Y63mbK3bt5YSe9MhsPm3Xlr5I7kfe97X6Ck\niCi3K4P91iVPUQmEkqBJa4dqZrWbHQiFm0BhXIOCw8e+BkrJoWeMeVKur6RP16lr6MXDlfShvU/b\n3qRVKEPoax5COI4ez1bS42NR3tRrIV9PSwCG1NXpkFuuUChZERHtyeZJuE+PfNraWyJ3PtnbP26y\n9jwbXd8XQHuZ6Lca65GdB6K8z9u81BY7Bo1LJBhX8i7ldmfQajiD1BuO+ineHMhD1184BPRjuszh\nd9vESq0bDrRZ2+uViIj0qtvV987bTd6uL79i7Ocef6SS3v5Oq7/ULqVKOdDJFsHV0WR0w4p5K2Qe\nOXTc2NlsNqoXfExpuxvcTQ30Z4yd7lpTSeeLtp4ChMIdLZyJ2i+tEYsK45sA94UJq50tqXnd15Zt\nriDsrHv+4E5j71L21t95yORlR+wcMTqaraRzEJp39MeR0vfOd95t8jCkbiajfdytNXnFYu2noKcH\nff5F4DgaOWb7PjKSraRzOXvjtm3dYWwTuhjmHd0Kuo1D91+18hw1tqQF8IstIYQQQgiJBVzYEkII\nIYSQWNCcFCGfl33PRZG3CirCSqJk9zX7+yM3O3kVcUtEpONa63Yljb64gkRbMHd84FtNHNcann46\n2qaWoSHItS5iTneqyz1lt0Glo9Paheia7HvGShoKqSOVdB62gJKwBZRQbs8Kapv83Llz0lKc2C1z\nveOFO2zN3P6MSt9hD+xL2Qhy3bdF22ifG7DV9A5EfyiJzUSPY/nPRNtzvZCHygntbug45OnTXgd5\np8B+lzbqXZ+QfCMkRRgHG12FLSN+VkRPIXo3NeTuK4FSBBhb4+qqF358yORdb4tK9ni0ZbsOKr41\nE92EZ8EN04EfHzb2yGjkci+ftVKE7PftsyzntXs+3M/XUZns/HBkywZjf/uv/qKS3t63VULo3j68\n2/b90Gh0Ewp56xJwBiVaJgScSs/NBdtfsVwfzQN/9Y3PmCzc+B9WY/WLj37V5I2+8Hgl/cJjGH/Q\n0nfneyvpAlzesf1P1DzusarRq8fk+WCbIYb3HTD2u97/QCXd1WMjt0kTkcesPCIq66/yyGM5JZ8c\nH7PrpPGxrLH1OyuTDIS8JPxiSwghhBBC4gEXtoQQQgghJBZwYUsIIYQQQmJBUxrbyZmSHBzTCsTo\n8OE9Vr8me7VfJoh7WbT23tFWuJ5CQWGd2JsNY/v67t97t7JA4NcJ4siE0h2tz9i8HAgcfRSSeN+z\noNbsUn04Cxol9J5Sy4dSq93ztEPbeqShxlYPqSOQdwDsu1T6wzZmbddWqwEbHIiUcY9CNc98U+mh\nd560meMbrZ1S13urVdv1bbd2byl6DtLgSWn/0egP2a9BvN0JGCt//Mkofa/NCrr/wmGvH0O87hhV\nWdfTsL+tpWFuVkTLxXVoz1zWPnOjh16spIvjVhF9dMwKh18pKN3aEat3dNJh7LyPTvq//pffMHk3\n33lnJT0ObYw8DwGxz47I0jNlrNPDWWO/592/W0m//KOfBGvSivI/vMfqy9/98P6ojSyozat0fWrA\n6Js3e2W6cDr78o8q6XoKRq3lHjqw32Z2qN+QzIQdO54YjuahqbGTgZIIPuiL19UaxuzY3fXon6gm\noc2borEzuH27ycKQxNodmJZjo2uyK5Fs1v62JpPpr1FS5NlRe32Hle6/mLPPW7Fg7WOnonXAGOhx\nDx+2Wvkt/ZE7yf6bbH+6VBzy3FH7G4ASTPw3qWMHB28Vy1KtqZYefrElhBBCCCGxgAtbQgghhBAS\nC7iwJYQQQgghsaDJwJltIqKFg1qUZ8MByvmQ41LL6aFjlbT7xWtM3vrTU1i8Zq2hqK1rrJzOhAfM\nbLSZGL6ypzvyGfnC0AmTN3U6oG2aGqudV1cSpXRqm2woUOnJRGnUKCWTYfsi//BEvQ5cGujHNuSL\nVUu3QD/aCQ4kp7Tf4FO28MG7rN/gg/ryf2G/reiA8j3ZZ7VCndusnZKoopt77NhIHs4au1eFGe3r\nzZi8dHfkBbf0KZt3eNSeS3Y88hc9nb3L5J1Oi0U7zw2F28Xj8AHS9jLLp6YviKgopaYrQy9aPfLQ\n542X30WDHjTNTDO03+TtB3ul8crhaBB89flnTd4HdtyFxStgYOf7BqJnaOgr1j+rlHBQqGdB+y0t\nof5zZdIJWsiXDkb6x819di5JwQulpETgOi0i0pGOvFTPFEFoX5w2pnX/Wi/QugZeIB2ZKF01B9g2\nZZ3ygQzhoNf32PMuFqIn8fzwLluP0nUePGzDvo/fZ0OY9w/cXEmXitH48P4K9XmsKKXtSwq92e9+\nPpq/9j+3x+SlU9HN+tzHrR/2/nSmZptP77bPZiFln80tKhRy14R9Hh//ymOV9HPPhf0sm9/swIT5\njz/5gbH7t4T9Zy8n/GJLCCGEEEJiARe2hBBCCCEkFrhmQto5t8qLbKhfUETCe6CI3t5vZjsmxmSi\n7dbBj36kZrECuGApwlbXSeUWZEq7Gjn8LfHnx1vml8f9yoCXv1MuSBp19wUxa3F0vl15fspZTyVy\nEPdVFT+E7bnBgtowSoDrrSRuuerxi3n2+o+rE83DFmWfdj2VB81F5jaod5vUAgUuH1Zp3IXUteyD\nvF374Q/a/Rc+5m9yR7z3EJh46XDX9HjZ/EH1F3WjC7C5NwrutYihd/ugsf/lez9cVD2/+rsPG/vg\n4w9BCR3m10rGvPetm1ucW1wc1g4rOeu80T5zUyfUk3X2mMmTDusCcOu90fy8dcBeb+3Cagxcw41l\ns8Y+sGe3siDM+nLgIDRvlw323bExitc9M9ya566VY2VgYMCjK6zFMF7KGruUj8bK7r3fN3kjBSvv\nGB+P5vrnHvq8ydv5w+9V0vcOWpdpIXY/9UVj4znec080PgcG39lwvfhyzo7XeIeLSBIEn91pK2Np\nBc419h7iF1tCCCGEEBILuLCyOqjzAAAM6ElEQVQlhBBCCCGxgAtbQgghhBASC5p09+WktlgSXIoY\nQo65EPDLJTMN9GsZ6QQdydRShc+8zpqnomt28ACEK9ah9kDDJUdBjzij83XY3nNN97Ap2qV2XEoc\nDrqLWcjKWLtb1ZkCpc2xcRt6dd3uKCxq6eYek5fvisbxeNF2qGfgbmOnZIfUAkey8VwGT1de973L\nhktMBP1r2XtcODBs7MyhKBTnnsceNHk7swHd3vZ/sfZHlcB5uSNdTk2JDOnzUveyx17lwQ/dV0mX\nIETl8ZesO75CQbllgpPyM7VdCV4WAlNf/1ar40yAW6ZUMhpsg/0Zk3dg+Hljb91ys7LAn57iB9/4\njLF/a8C2ufORJyIjewXonmfsu2RqCFXnoWOtsv3A089U0oW8HVfbtm+rpDPgNmx4FH4Y0LFatYGN\ndlqzQynoZ0LvUBjXnRlrb4rsvgE7iabAbdRx9XydtdNOzLHv0Sc/9uvGvnnLjZV0Gu7/Nz7y58Ye\nLUT3DTW2Iz8+WkkXB7eZvO/vsy7UvvZk5KYrX7Tvj21brautVJ/VSjeOfWllugdrlFvZ8IstIYQQ\nQgiJBVzYEkIIIYSQWMCFLSGEEEIIiQWLCKmrPWU2GoOznmBP6YyqfN5eBt9+IZZMU4uchXZU6L1d\nNgxfc2jtrr7dLXMdWB8cdVpCCnLih21kRnlKpdHV6oZDTxl75MPZSvotHvR0uyKt0557PmmymvHG\nh35jtRve0YO2zSf3fjPq23NW93j4sA2ReemeFxugH5wGv0Glm5wZLh0vVrGs5oxuqwNNpaM5IgV3\nqztlz0n7FC3krQYbfUAnVHzTfM7qmovFyMnv5KQ9DuuZKSo3q814XEWNpXpEMz1WI54Cu7c3svs3\n2+s1CecyNBT5be7rs2UTySg0ekms/nLLjZts/7JHJF5cq9IYFhh+MKC020Mv7jdZeaXZHwAN67vu\n+21bz333V5LFotXNFkH7r8cy+ivXeagll6R9mJM6zHqi3fY9d8bYhw8uy0x0WfgmhJ5OqHt+/x0Q\norbL3psJFWJ7BGTTh/tsuNvnR9T1vs76DR46EIW0/Tr0b8fttxr7T//wjytpnA9E0A4RnWcxa++v\nGRsiMPdmmmijcYrFLPTh0tvhF1tCCCGEEBILuLAlhBBCCCGx4BI3HPXhuCkbaiYkTZgI5F2toGxA\nb5PgdcdrW8s92+KiUjaMh+Z0Og9lT6k07Pg9B0V1NdmD1iXLOzbeY+yRD3wgMsDNznX3RFvW9Ryj\nfP0Lj1bS7//UR+uUXsm835r3Q7YeKiFvQi1hQkR0aGMlzThsx/++k9GWeOdGO2C6EtZe2xXZqS7Y\ndoexllLbcN3gpimZqD1VJqrySgsmF8JuG9t6dL1JCPOcyditx8GbIvdDb+ixc0IqscbY3d0ZZaFP\nvmib8tNP2S3VR+77kMSb84E8kIp5ZZ+1rsCyLwyptD1sD27n9ih7FuZyDLs9pSdO6+7JuttkWPpG\n6NlgxWztyehN8OzTVtb22OMwNtTl7tlss14asjKW7p7+SvqF737X5N2qXOilEqE1lIiUonFWOPCi\nyXryqZ3G1nNHd4+d97S8ZP9e+w49Bp7i1qo0vhJCDl5D4OjE1c3cK5ErPem+a1Ft8IstIYQQQgiJ\nBVzYEkIIIYSQWMCFLSGEEEIIiQVL6NQnJMpD7Uig2U7rfgSjA16doA5uzYKl5gnpmZdRYzsr1Vra\ni+DfdYRP8ECVgJCpG49G+qXJHdaVzvN46seUm6MD1pnKWRdpqH5tpbmUaxXbv2TtB+GZHVM3plQ7\n1GprQFeC+maCvvGVbCU5BbcOp4vlubMQ+rRT6WFRf5tcbe01UdnOLvuc6/Cmm8C9D7rlyR6P0pvS\nVvTXncH5Q+t1rXvF50cjF17x19ReDrLWHMsuVIgsEfga0iN/I74vlAu3Ysk+bw+8a7ux1ySims5A\nSN1kyT5vGwpRDyae/abJe/ab0TtqDH4HMnTE6l+Hlcx3uaIbL4daeyPYQ9+PQgen3/CMLAZ+sSWE\nEEIIIbGAC1tCCCGEEBILmpQioA+nELW2wBdqVuWnYNvsDdbulCgySqJk3WqUVAQg3AHcmLZbbnon\nr1CALVk4eFJFhHnlOGx2XhapxOwij1vGyGNzYtUpXTXSImZ3ri9p70UqkTF2+sboxo3vtRGVTj/y\noK33wBMNdPQqYt9b4A8rKapQm9SW2Jyt8feVAs4JgUkBPUqpvT48amrhYiKywJ1Tj3PfLRmT1dNt\n57PB/sjFUE//oMl75AnwT0XIFczJsZ/Jpz/24Yo92Bdp3cYO26eooGQhxTErCxg/bl26jWhvhFWt\nopNKEmJgvbV/5b59CxdsAn6xJYQQQgghsYALW0IIIYQQEgu4sCWEEEIIIbHAed+42yfnVnl0DxMB\nbrqaCrimXOB0ggBznQ0lKbNaVwt+o/IqNiu4w+mEerpSUX+L4I4sX7K2LygN8Bmr65XzOh7sjDQO\nalxDcme4Bqa/eN1Rf1tLE31WvJ9pmdDWbRnwskepj/RthUsoD++O0qOgT7rzPmt/5YkofXbXJfSQ\nNMkR7/1A/WKLw7kOL3Kd+ksoBnOLXdXFnQ6Vxul8ifyjee9bN7c4xwEQI1o5VlYlkz59ww0VO3c8\n0s7OcBRdiTT0HuIXW0IIIYQQEgu4sCWEEEIIIbGAC1tCCCGEEBILFhFSFzWdFwlpO+s1o/S4RRBg\nTkBR5atWktim0qKCtHRqsrb/3akS+LGdhrJaY3t+Eo4OnVujPn8XKothiDWh8MWNtjG3yDoaZEZE\ntJtZfVvPQNmdn1cGBAs8uHNJu3X1EZKvoUNhPeZQCI0OWJcadHy8jOGfrzb0TwGukmjS5OpkzosU\n1VRCXe3VAb/YEkIIIYSQWMCFLSGEEEIIiQWLkCLo7X8tS8CtdL2ViT5lsKyyPWz1n0ctgupyEepJ\nBvJAbjCVU/0rwf5E6jprmy1a7Hsopi5uAzezDxIKSRzKa7TOFu/JFETkJbAvksXCw/gHsmiuBXuz\nSuPjHrJR7nLwUjrVAF5qSxEIIaR52hIdsibdXbHPZkcDpUlc4BdbQgghhBASC7iwJYQQQgghsYAL\nW0IIIYQQEgua1NiWRCTXYFVaI4d6vYC2z60J1CMiXmt8oR4jPYU2E1C2S+l+S3X0fIXFute6FB3r\nTI00cik63hbyclHkUyPqD0dVOrvMnbma2AS2Hvf1dKuL1W4vBV6aC0lNCCFh2toTklTv+o7evkp6\nZnRkoUNIDOAXW0IIIYQQEgu4sCWEEEIIIbGAC1tCCCGEEBILmtTYog5O6zub8YkZ8mOLGt6AdteD\nf9wZXS+EAJ3qrN2FGewf6lT1eWKo2w5dUeA4kfDlXqy+cIVoaqs4KyJPK1vfV/oobR04xrQDYRzn\nGFI3VA8hhFxZlEolOZPLR7b+rc168FdfOBulQ+7pyYqHX2wJIYQQQkgs4MKWEEIIIYTEgkXsN+L2\n+kXqufTSNLPtjtvWut7TkAdyAwPsLczo86i3na/zQ/2pd176WLw+K9Rt16J5TUQOK3tMpWu5jCOL\nQ8thmnG718wzSwghVxazMxfk7Mms+su0SoOsMKmkCVNnhVy58IstIYQQQgiJBVzYEkIIIYSQWMCF\nLSGEEEIIiQWLENXV0n52gL3Y8JxYT6qJeptpR9WL0twpcBVmQB1tSFcb0sliX690TS0yISLPK1u7\nlkJtNLk09Bgchzz9iE9A3izYa5esR4QQctmZLYnkzyycl4Dlz1R+4XLkioNfbAkhhBBCSCzgwpYQ\nQgghhMSCJfTvs9jIWfXqacbtRqgP6E5LRWSauhxujuImPUAwSt0alaYUoXXgthtGytO0gx2KFkgI\nIVcazkoOptR7f2ap1ixkpcEvtoQQQgghJBZwYUsIIYQQQmIBF7aEEEIIISQWXEUxNEOaVmptlp52\nEfmFy92Jq5BNYGvNbQHy1oEdCr9LCCFXGH7O6mr5rr8q4BdbQgghhBASC7iwJYQQQgghsYALW0II\nIYQQEguuIo0tWV5mxfogZrjC5eEY2CFtebaF/SCEkMsN+lMnK52OLZlK+paBQZN34IldDdXBL7aE\nEEIIISQWcGFLCCGEEEJiAaUIpEW8TkT+k7K1y5UhKKvdTE1AXsjtVAnsuIcprkWfSqchT0tAMNzu\narBTgTaGm+0UIYQQ0hQzw9lK+kgp9E6qDb/YEkIIIYSQWMCFLSGEEEIIiQVc2BJCCCGEkFjgvG9c\nl+icOy0iJ1rXHbKMbPLer29V5RwrsYPjhTQKxwppFI4V0gwNjZemFraEEEIIIYSsVChFIIQQQggh\nsYALW0IIIYQQEgu4sCWEEEIIIbGAC1tCCCGEEBILuLAlhBBCCCGxgAtbQgghhBASC7iwJYQQQggh\nsYALW0IIIYQQEgu4sCWEEEIIIbHg/wNbPBFJbnbLfwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x144 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Farthest image distance is 33.887325286865234\n",
            "Labels are Ship, Horse\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m2835WeTBX3vzjYnm188meZ70pshxJhasEMskyPXwzAin1+O1+Zxor2rscXldo0+PzGny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\n",
            "text/plain": [
              "<Figure size 576x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpJzAlrhjaqD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cbf5d286-b93d-4575-ede3-99d33d39e745"
      },
      "source": [
        "'''\n",
        "SIZE = 2000\n",
        "tsne = TSNE(n_components=no_of_components, perplexity = 50)\n",
        "decomposed_embeddings = tsne.fit_transform(x_embeddings[:SIZE])\n",
        "decomposed_gray = tsne.fit_transform(x_embeddings_before_train[:SIZE])\n",
        "'''\n",
        "print('')"
      ],
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "547NSaTTuwYr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "db24ab80-8e9f-466f-b612-b27134e5d2ca"
      },
      "source": [
        "'''\n",
        "\n",
        "fig = plt.figure(figsize=(20, 8))\n",
        "for label in test_class_labels:\n",
        "    decomposed_embeddings_class = decomposed_embeddings[y_test[:SIZE,0] == label]\n",
        "    decomposed_gray_class = decomposed_gray[y_test[:SIZE,0] == label]\n",
        "\n",
        "    plt.subplot(1,2,1)\n",
        "    plt.scatter(decomposed_gray_class[::step,1], decomposed_gray_class[::step,0], label=cifar10_labels[label])\n",
        "    plt.title('before training')\n",
        "    # Turn off tick labels\n",
        "    plt.legend()\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.tight_layout()\n",
        "\n",
        "    plt.subplot(1,2,2)\n",
        "    plt.scatter(decomposed_embeddings_class[::step, 1], decomposed_embeddings_class[::step, 0], label=cifar10_labels[label])\n",
        "    plt.title('@%d epochs' % epochs)\n",
        "    plt.legend()\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.tight_layout()\n",
        "plt.show()\n",
        "'''\n",
        "print('')"
      ],
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3R5-aEn7c3I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}