3_keras_center_loss_with_embedding.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "colab": {
      "name": "3-keras_center_loss_with_embedding.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzGy398kM2xJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.datasets import mnist\n",
        "from keras.layers import Input,Conv2D,Activation,Dense,Lambda,Flatten,Embedding,PReLU,BatchNormalization\n",
        "from keras.models import Model\n",
        "import keras.backend as K\n",
        "from keras.utils import to_categorical\n",
        "import numpy as np\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxZsPyIsM2xh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "71d7fef0-95eb-424e-c20e-c7538bd96623"
      },
      "source": [
        "from keras.datasets import mnist\n",
        "# load data\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pzO4VaOM2yC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "outputId": "3948bc94-378f-4a26-c833-c8a5e77ecce5"
      },
      "source": [
        "inputs=Input((28,28,1))\n",
        "x=Conv2D(32,(3,3))(inputs)\n",
        "x=BatchNormalization()(x)\n",
        "x=PReLU()(x)\n",
        "x=Conv2D(32,(3,3))(x)\n",
        "x=BatchNormalization()(x)\n",
        "x=PReLU()(x)\n",
        "x=Conv2D(64,(5,5))(x)\n",
        "x=BatchNormalization()(x)\n",
        "x=PReLU()(x)\n",
        "x=Conv2D(64,(5,5))(x)\n",
        "x=BatchNormalization()(x)\n",
        "x=PReLU()(x)\n",
        "x=Conv2D(128,(7,7))(x)\n",
        "x=BatchNormalization()(x)\n",
        "x=PReLU()(x)\n",
        "x=Conv2D(128,(7,7))(x)\n",
        "x=BatchNormalization()(x)\n",
        "x=PReLU()(x)\n",
        "x=Flatten()(x)\n",
        "x=Dense(2)(x)\n",
        "out1=PReLU(name=\"out1\")(x)\n",
        "out2=Dense(10,activation=\"softmax\")(out1)\n",
        "\n",
        "model=Model(inputs,out2)\n",
        "model.summary()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 26, 26, 32)        128       \n",
            "_________________________________________________________________\n",
            "p_re_lu (PReLU)              (None, 26, 26, 32)        21632     \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 24, 24, 32)        128       \n",
            "_________________________________________________________________\n",
            "p_re_lu_1 (PReLU)            (None, 24, 24, 32)        18432     \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 20, 20, 64)        51264     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 20, 20, 64)        256       \n",
            "_________________________________________________________________\n",
            "p_re_lu_2 (PReLU)            (None, 20, 20, 64)        25600     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        102464    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "p_re_lu_3 (PReLU)            (None, 16, 16, 64)        16384     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 10, 10, 128)       401536    \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 10, 10, 128)       512       \n",
            "_________________________________________________________________\n",
            "p_re_lu_4 (PReLU)            (None, 10, 10, 128)       12800     \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 4, 4, 128)         802944    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 4, 4, 128)         512       \n",
            "_________________________________________________________________\n",
            "p_re_lu_5 (PReLU)            (None, 4, 4, 128)         2048      \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2)                 4098      \n",
            "_________________________________________________________________\n",
            "out1 (PReLU)                 (None, 2)                 2         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                30        \n",
            "=================================================================\n",
            "Total params: 1,470,594\n",
            "Trainable params: 1,469,698\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJaNJTXBM2yZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ba816f3d-e810-4a91-d5d8-173eb1539386"
      },
      "source": [
        "lambda_c=1\n",
        "input_=Input(shape=(1,))\n",
        "centers=Embedding(10,2)(input_)\n",
        "intra_loss=Lambda(lambda x:K.sum(K.square(x[0]-x[1][:,0]),1,keepdims=True))([out1,centers])\n",
        "model_center_loss=Model([inputs,input_],[out2,intra_loss])\n",
        "model_center_loss.compile(optimizer=\"sgd\",\n",
        "                          loss=[\"categorical_crossentropy\",lambda y_true,y_pred:y_pred],\n",
        "                          loss_weights=[1,lambda_c/2.],\n",
        "                          metrics=[\"acc\"])\n",
        "model_center_loss.summary()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 28, 28, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 26, 26, 32)   320         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 26, 26, 32)   128         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu (PReLU)                 (None, 26, 26, 32)   21632       batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 24, 24, 32)   9248        p_re_lu[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 24, 24, 32)   128         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_1 (PReLU)               (None, 24, 24, 32)   18432       batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 20, 20, 64)   51264       p_re_lu_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 20, 20, 64)   256         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_2 (PReLU)               (None, 20, 20, 64)   25600       batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 16, 16, 64)   102464      p_re_lu_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 16, 16, 64)   256         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_3 (PReLU)               (None, 16, 16, 64)   16384       batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 10, 10, 128)  401536      p_re_lu_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 10, 10, 128)  512         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_4 (PReLU)               (None, 10, 10, 128)  12800       batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 4, 4, 128)    802944      p_re_lu_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 4, 4, 128)    512         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "p_re_lu_5 (PReLU)               (None, 4, 4, 128)    2048        batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 2048)         0           p_re_lu_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 2)            4098        flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "out1 (PReLU)                    (None, 2)            2           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "embedding (Embedding)           (None, 1, 2)         20          input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 10)           30          out1[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "lambda (Lambda)                 (None, 1)            0           out1[0][0]                       \n",
            "                                                                 embedding[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 1,470,614\n",
            "Trainable params: 1,469,718\n",
            "Non-trainable params: 896\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhcD9owzM2ys",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "f1e266fd-6b3f-41ab-9294-518ed973d29d"
      },
      "source": [
        "x_train=x_train.astype(\"float32\")\n",
        "x_test=x_test.astype(\"float32\")\n",
        "x_train/=255.\n",
        "x_test/=255.\n",
        "x_train=np.expand_dims(x_train,axis=-1)\n",
        "x_test=np.expand_dims(x_test,axis=-1)\n",
        "\n",
        "y_train_one_hot=to_categorical(y_train)\n",
        "y_test_one_hot=to_categorical(y_test)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(y_train_one_hot.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28, 1)\n",
            "(60000,)\n",
            "(60000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LED6gcO1M2zC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "98bf8ff2-f197-496c-f086-309822868cf3"
      },
      "source": [
        "dummy_matrix1=np.zeros((x_train.shape[0],1))\n",
        "dummy_matrix2=np.zeros((x_test.shape[0],1))\n",
        "\n",
        "model_center_loss.fit(x=[x_train,y_train],y=[y_train_one_hot,dummy_matrix1],\n",
        "                     batch_size=512,epochs=100,verbose=1,validation_data=([x_test,y_test],[y_test_one_hot,dummy_matrix2]),\n",
        "                     callbacks=[EarlyStopping(monitor=\"val_loss\",patience=20)])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 2.2934 - dense_1_loss: 2.2674 - lambda_loss: 0.0521 - dense_1_acc: 0.1485 - lambda_acc: 0.9737 - val_loss: 2.3036 - val_dense_1_loss: 2.3022 - val_lambda_loss: 0.0028 - val_dense_1_acc: 0.1135 - val_lambda_acc: 1.0000\n",
            "Epoch 2/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 2.2415 - dense_1_loss: 2.1690 - lambda_loss: 0.1451 - dense_1_acc: 0.3169 - lambda_acc: 0.9280 - val_loss: 2.3042 - val_dense_1_loss: 2.3018 - val_lambda_loss: 0.0048 - val_dense_1_acc: 0.1135 - val_lambda_acc: 1.0000\n",
            "Epoch 3/100\n",
            "118/118 [==============================] - 14s 119ms/step - loss: 2.1972 - dense_1_loss: 2.1035 - lambda_loss: 0.1875 - dense_1_acc: 0.3701 - lambda_acc: 0.9043 - val_loss: 2.3682 - val_dense_1_loss: 2.3055 - val_lambda_loss: 0.1255 - val_dense_1_acc: 0.0958 - val_lambda_acc: 1.0000\n",
            "Epoch 4/100\n",
            "118/118 [==============================] - 14s 116ms/step - loss: 2.1522 - dense_1_loss: 2.0463 - lambda_loss: 0.2118 - dense_1_acc: 0.3938 - lambda_acc: 0.8878 - val_loss: 2.4646 - val_dense_1_loss: 2.3009 - val_lambda_loss: 0.3275 - val_dense_1_acc: 0.0958 - val_lambda_acc: 0.8141\n",
            "Epoch 5/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 2.1043 - dense_1_loss: 1.9892 - lambda_loss: 0.2301 - dense_1_acc: 0.4060 - lambda_acc: 0.8754 - val_loss: 2.2773 - val_dense_1_loss: 2.1875 - val_lambda_loss: 0.1796 - val_dense_1_acc: 0.2091 - val_lambda_acc: 0.8995\n",
            "Epoch 6/100\n",
            "118/118 [==============================] - 13s 110ms/step - loss: 2.0537 - dense_1_loss: 1.9302 - lambda_loss: 0.2470 - dense_1_acc: 0.4131 - lambda_acc: 0.8597 - val_loss: 2.1149 - val_dense_1_loss: 2.0179 - val_lambda_loss: 0.1941 - val_dense_1_acc: 0.3673 - val_lambda_acc: 0.8765\n",
            "Epoch 7/100\n",
            "118/118 [==============================] - 13s 111ms/step - loss: 2.0013 - dense_1_loss: 1.8712 - lambda_loss: 0.2601 - dense_1_acc: 0.4162 - lambda_acc: 0.8476 - val_loss: 1.9902 - val_dense_1_loss: 1.9048 - val_lambda_loss: 0.1709 - val_dense_1_acc: 0.4163 - val_lambda_acc: 0.9362\n",
            "Epoch 8/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 1.9457 - dense_1_loss: 1.8107 - lambda_loss: 0.2701 - dense_1_acc: 0.4214 - lambda_acc: 0.8337 - val_loss: 1.9269 - val_dense_1_loss: 1.7702 - val_lambda_loss: 0.3135 - val_dense_1_acc: 0.4157 - val_lambda_acc: 0.7877\n",
            "Epoch 9/100\n",
            "118/118 [==============================] - 13s 114ms/step - loss: 1.8890 - dense_1_loss: 1.7516 - lambda_loss: 0.2748 - dense_1_acc: 0.4247 - lambda_acc: 0.8307 - val_loss: 1.8882 - val_dense_1_loss: 1.6997 - val_lambda_loss: 0.3770 - val_dense_1_acc: 0.4214 - val_lambda_acc: 0.7497\n",
            "Epoch 10/100\n",
            "118/118 [==============================] - 13s 114ms/step - loss: 1.8338 - dense_1_loss: 1.6950 - lambda_loss: 0.2775 - dense_1_acc: 0.4283 - lambda_acc: 0.8240 - val_loss: 1.8149 - val_dense_1_loss: 1.6836 - val_lambda_loss: 0.2626 - val_dense_1_acc: 0.4210 - val_lambda_acc: 0.8188\n",
            "Epoch 11/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 1.7835 - dense_1_loss: 1.6428 - lambda_loss: 0.2814 - dense_1_acc: 0.4307 - lambda_acc: 0.8148 - val_loss: 1.8223 - val_dense_1_loss: 1.6139 - val_lambda_loss: 0.4167 - val_dense_1_acc: 0.4091 - val_lambda_acc: 0.6811\n",
            "Epoch 12/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.7234 - dense_1_loss: 1.5879 - lambda_loss: 0.2710 - dense_1_acc: 0.4372 - lambda_acc: 0.8307 - val_loss: 1.7147 - val_dense_1_loss: 1.5751 - val_lambda_loss: 0.2793 - val_dense_1_acc: 0.4335 - val_lambda_acc: 0.8284\n",
            "Epoch 13/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.6774 - dense_1_loss: 1.5419 - lambda_loss: 0.2710 - dense_1_acc: 0.4432 - lambda_acc: 0.8284 - val_loss: 1.6776 - val_dense_1_loss: 1.5298 - val_lambda_loss: 0.2957 - val_dense_1_acc: 0.4254 - val_lambda_acc: 0.7869\n",
            "Epoch 14/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.6233 - dense_1_loss: 1.4937 - lambda_loss: 0.2591 - dense_1_acc: 0.4563 - lambda_acc: 0.8425 - val_loss: 1.6109 - val_dense_1_loss: 1.4859 - val_lambda_loss: 0.2500 - val_dense_1_acc: 0.4695 - val_lambda_acc: 0.8506\n",
            "Epoch 15/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 1.5806 - dense_1_loss: 1.4530 - lambda_loss: 0.2552 - dense_1_acc: 0.4659 - lambda_acc: 0.8499 - val_loss: 1.5772 - val_dense_1_loss: 1.4411 - val_lambda_loss: 0.2723 - val_dense_1_acc: 0.4621 - val_lambda_acc: 0.8586\n",
            "Epoch 16/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 1.5344 - dense_1_loss: 1.4112 - lambda_loss: 0.2465 - dense_1_acc: 0.4839 - lambda_acc: 0.8596 - val_loss: 1.5639 - val_dense_1_loss: 1.3942 - val_lambda_loss: 0.3394 - val_dense_1_acc: 0.4349 - val_lambda_acc: 0.7306\n",
            "Epoch 17/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 1.4913 - dense_1_loss: 1.3724 - lambda_loss: 0.2377 - dense_1_acc: 0.4987 - lambda_acc: 0.8697 - val_loss: 1.5336 - val_dense_1_loss: 1.3849 - val_lambda_loss: 0.2973 - val_dense_1_acc: 0.4838 - val_lambda_acc: 0.7841\n",
            "Epoch 18/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.4556 - dense_1_loss: 1.3381 - lambda_loss: 0.2350 - dense_1_acc: 0.5185 - lambda_acc: 0.8694 - val_loss: 1.5154 - val_dense_1_loss: 1.3207 - val_lambda_loss: 0.3892 - val_dense_1_acc: 0.5209 - val_lambda_acc: 0.7139\n",
            "Epoch 19/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.4152 - dense_1_loss: 1.3022 - lambda_loss: 0.2261 - dense_1_acc: 0.5465 - lambda_acc: 0.8827 - val_loss: 1.4371 - val_dense_1_loss: 1.3369 - val_lambda_loss: 0.2003 - val_dense_1_acc: 0.5133 - val_lambda_acc: 0.9258\n",
            "Epoch 20/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.3804 - dense_1_loss: 1.2720 - lambda_loss: 0.2168 - dense_1_acc: 0.5507 - lambda_acc: 0.8874 - val_loss: 1.3837 - val_dense_1_loss: 1.2751 - val_lambda_loss: 0.2172 - val_dense_1_acc: 0.5837 - val_lambda_acc: 0.8623\n",
            "Epoch 21/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.3415 - dense_1_loss: 1.2373 - lambda_loss: 0.2085 - dense_1_acc: 0.5885 - lambda_acc: 0.8990 - val_loss: 1.3825 - val_dense_1_loss: 1.2630 - val_lambda_loss: 0.2390 - val_dense_1_acc: 0.5426 - val_lambda_acc: 0.8431\n",
            "Epoch 22/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.3124 - dense_1_loss: 1.2088 - lambda_loss: 0.2072 - dense_1_acc: 0.6027 - lambda_acc: 0.9061 - val_loss: 1.4239 - val_dense_1_loss: 1.2878 - val_lambda_loss: 0.2722 - val_dense_1_acc: 0.4833 - val_lambda_acc: 0.8344\n",
            "Epoch 23/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.2759 - dense_1_loss: 1.1776 - lambda_loss: 0.1966 - dense_1_acc: 0.6325 - lambda_acc: 0.9115 - val_loss: 1.3352 - val_dense_1_loss: 1.1895 - val_lambda_loss: 0.2913 - val_dense_1_acc: 0.6209 - val_lambda_acc: 0.8302\n",
            "Epoch 24/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.2453 - dense_1_loss: 1.1494 - lambda_loss: 0.1918 - dense_1_acc: 0.6504 - lambda_acc: 0.9215 - val_loss: 1.2571 - val_dense_1_loss: 1.1509 - val_lambda_loss: 0.2125 - val_dense_1_acc: 0.6546 - val_lambda_acc: 0.8670\n",
            "Epoch 25/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.2138 - dense_1_loss: 1.1215 - lambda_loss: 0.1845 - dense_1_acc: 0.6648 - lambda_acc: 0.9276 - val_loss: 1.2651 - val_dense_1_loss: 1.1878 - val_lambda_loss: 0.1546 - val_dense_1_acc: 0.7398 - val_lambda_acc: 0.9450\n",
            "Epoch 26/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.1872 - dense_1_loss: 1.0968 - lambda_loss: 0.1808 - dense_1_acc: 0.6781 - lambda_acc: 0.9295 - val_loss: 1.2890 - val_dense_1_loss: 1.1115 - val_lambda_loss: 0.3551 - val_dense_1_acc: 0.5897 - val_lambda_acc: 0.7456\n",
            "Epoch 27/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.1604 - dense_1_loss: 1.0711 - lambda_loss: 0.1786 - dense_1_acc: 0.6931 - lambda_acc: 0.9349 - val_loss: 1.1761 - val_dense_1_loss: 1.0656 - val_lambda_loss: 0.2210 - val_dense_1_acc: 0.6795 - val_lambda_acc: 0.8925\n",
            "Epoch 28/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.1393 - dense_1_loss: 1.0496 - lambda_loss: 0.1793 - dense_1_acc: 0.7090 - lambda_acc: 0.9306 - val_loss: 1.1707 - val_dense_1_loss: 1.0778 - val_lambda_loss: 0.1859 - val_dense_1_acc: 0.7508 - val_lambda_acc: 0.9306\n",
            "Epoch 29/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.1100 - dense_1_loss: 1.0244 - lambda_loss: 0.1713 - dense_1_acc: 0.7281 - lambda_acc: 0.9387 - val_loss: 1.1559 - val_dense_1_loss: 1.0228 - val_lambda_loss: 0.2662 - val_dense_1_acc: 0.6886 - val_lambda_acc: 0.8469\n",
            "Epoch 30/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.0924 - dense_1_loss: 1.0046 - lambda_loss: 0.1755 - dense_1_acc: 0.7459 - lambda_acc: 0.9333 - val_loss: 1.1743 - val_dense_1_loss: 1.0535 - val_lambda_loss: 0.2416 - val_dense_1_acc: 0.7694 - val_lambda_acc: 0.8572\n",
            "Epoch 31/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.0602 - dense_1_loss: 0.9789 - lambda_loss: 0.1626 - dense_1_acc: 0.7711 - lambda_acc: 0.9487 - val_loss: 1.0988 - val_dense_1_loss: 1.0254 - val_lambda_loss: 0.1468 - val_dense_1_acc: 0.7738 - val_lambda_acc: 0.9565\n",
            "Epoch 32/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.0346 - dense_1_loss: 0.9556 - lambda_loss: 0.1579 - dense_1_acc: 0.7953 - lambda_acc: 0.9545 - val_loss: 1.1302 - val_dense_1_loss: 1.0138 - val_lambda_loss: 0.2327 - val_dense_1_acc: 0.7260 - val_lambda_acc: 0.8828\n",
            "Epoch 33/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 1.0274 - dense_1_loss: 0.9414 - lambda_loss: 0.1720 - dense_1_acc: 0.8106 - lambda_acc: 0.9357 - val_loss: 1.0745 - val_dense_1_loss: 0.9641 - val_lambda_loss: 0.2207 - val_dense_1_acc: 0.7624 - val_lambda_acc: 0.8859\n",
            "Epoch 34/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.9975 - dense_1_loss: 0.9176 - lambda_loss: 0.1597 - dense_1_acc: 0.8352 - lambda_acc: 0.9481 - val_loss: 1.0328 - val_dense_1_loss: 0.9549 - val_lambda_loss: 0.1559 - val_dense_1_acc: 0.8147 - val_lambda_acc: 0.9469\n",
            "Epoch 35/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.9789 - dense_1_loss: 0.8981 - lambda_loss: 0.1616 - dense_1_acc: 0.8534 - lambda_acc: 0.9475 - val_loss: 1.0040 - val_dense_1_loss: 0.9299 - val_lambda_loss: 0.1482 - val_dense_1_acc: 0.8786 - val_lambda_acc: 0.9554\n",
            "Epoch 36/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.9521 - dense_1_loss: 0.8756 - lambda_loss: 0.1529 - dense_1_acc: 0.8731 - lambda_acc: 0.9572 - val_loss: 0.9920 - val_dense_1_loss: 0.9099 - val_lambda_loss: 0.1642 - val_dense_1_acc: 0.8572 - val_lambda_acc: 0.9407\n",
            "Epoch 37/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.9426 - dense_1_loss: 0.8611 - lambda_loss: 0.1629 - dense_1_acc: 0.8832 - lambda_acc: 0.9456 - val_loss: 0.9712 - val_dense_1_loss: 0.8845 - val_lambda_loss: 0.1734 - val_dense_1_acc: 0.9212 - val_lambda_acc: 0.9461\n",
            "Epoch 38/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.9076 - dense_1_loss: 0.8349 - lambda_loss: 0.1455 - dense_1_acc: 0.9047 - lambda_acc: 0.9629 - val_loss: 0.9812 - val_dense_1_loss: 0.8790 - val_lambda_loss: 0.2045 - val_dense_1_acc: 0.8936 - val_lambda_acc: 0.9277\n",
            "Epoch 39/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.8924 - dense_1_loss: 0.8173 - lambda_loss: 0.1503 - dense_1_acc: 0.9182 - lambda_acc: 0.9567 - val_loss: 0.9533 - val_dense_1_loss: 0.8740 - val_lambda_loss: 0.1585 - val_dense_1_acc: 0.8804 - val_lambda_acc: 0.9456\n",
            "Epoch 40/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.8788 - dense_1_loss: 0.8018 - lambda_loss: 0.1540 - dense_1_acc: 0.9194 - lambda_acc: 0.9534 - val_loss: 0.9526 - val_dense_1_loss: 0.8193 - val_lambda_loss: 0.2665 - val_dense_1_acc: 0.8864 - val_lambda_acc: 0.8559\n",
            "Epoch 41/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.8582 - dense_1_loss: 0.7824 - lambda_loss: 0.1515 - dense_1_acc: 0.9266 - lambda_acc: 0.9574 - val_loss: 1.0328 - val_dense_1_loss: 0.8434 - val_lambda_loss: 0.3787 - val_dense_1_acc: 0.8315 - val_lambda_acc: 0.7795\n",
            "Epoch 42/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.8385 - dense_1_loss: 0.7641 - lambda_loss: 0.1489 - dense_1_acc: 0.9396 - lambda_acc: 0.9601 - val_loss: 0.9197 - val_dense_1_loss: 0.7715 - val_lambda_loss: 0.2964 - val_dense_1_acc: 0.8720 - val_lambda_acc: 0.8231\n",
            "Epoch 43/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.8261 - dense_1_loss: 0.7480 - lambda_loss: 0.1561 - dense_1_acc: 0.9410 - lambda_acc: 0.9516 - val_loss: 0.8583 - val_dense_1_loss: 0.7535 - val_lambda_loss: 0.2096 - val_dense_1_acc: 0.9436 - val_lambda_acc: 0.9299\n",
            "Epoch 44/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.8011 - dense_1_loss: 0.7277 - lambda_loss: 0.1466 - dense_1_acc: 0.9493 - lambda_acc: 0.9597 - val_loss: 0.8439 - val_dense_1_loss: 0.7537 - val_lambda_loss: 0.1803 - val_dense_1_acc: 0.8942 - val_lambda_acc: 0.9376\n",
            "Epoch 45/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.7847 - dense_1_loss: 0.7114 - lambda_loss: 0.1466 - dense_1_acc: 0.9557 - lambda_acc: 0.9604 - val_loss: 0.8408 - val_dense_1_loss: 0.7505 - val_lambda_loss: 0.1806 - val_dense_1_acc: 0.9115 - val_lambda_acc: 0.9286\n",
            "Epoch 46/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.7632 - dense_1_loss: 0.6929 - lambda_loss: 0.1407 - dense_1_acc: 0.9582 - lambda_acc: 0.9639 - val_loss: 0.8653 - val_dense_1_loss: 0.7387 - val_lambda_loss: 0.2532 - val_dense_1_acc: 0.8823 - val_lambda_acc: 0.8814\n",
            "Epoch 47/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.7493 - dense_1_loss: 0.6768 - lambda_loss: 0.1450 - dense_1_acc: 0.9643 - lambda_acc: 0.9616 - val_loss: 0.8947 - val_dense_1_loss: 0.7595 - val_lambda_loss: 0.2705 - val_dense_1_acc: 0.9340 - val_lambda_acc: 0.8183\n",
            "Epoch 48/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.7407 - dense_1_loss: 0.6651 - lambda_loss: 0.1513 - dense_1_acc: 0.9640 - lambda_acc: 0.9549 - val_loss: 0.8164 - val_dense_1_loss: 0.7011 - val_lambda_loss: 0.2305 - val_dense_1_acc: 0.9065 - val_lambda_acc: 0.9119\n",
            "Epoch 49/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.7214 - dense_1_loss: 0.6481 - lambda_loss: 0.1467 - dense_1_acc: 0.9688 - lambda_acc: 0.9570 - val_loss: 0.7812 - val_dense_1_loss: 0.6978 - val_lambda_loss: 0.1668 - val_dense_1_acc: 0.9476 - val_lambda_acc: 0.9426\n",
            "Epoch 50/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.6952 - dense_1_loss: 0.6292 - lambda_loss: 0.1320 - dense_1_acc: 0.9740 - lambda_acc: 0.9697 - val_loss: 0.7603 - val_dense_1_loss: 0.6690 - val_lambda_loss: 0.1827 - val_dense_1_acc: 0.9494 - val_lambda_acc: 0.9403\n",
            "Epoch 51/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.6780 - dense_1_loss: 0.6131 - lambda_loss: 0.1297 - dense_1_acc: 0.9769 - lambda_acc: 0.9707 - val_loss: 0.8174 - val_dense_1_loss: 0.6566 - val_lambda_loss: 0.3217 - val_dense_1_acc: 0.9442 - val_lambda_acc: 0.8408\n",
            "Epoch 52/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.6732 - dense_1_loss: 0.6030 - lambda_loss: 0.1403 - dense_1_acc: 0.9737 - lambda_acc: 0.9603 - val_loss: 0.7487 - val_dense_1_loss: 0.6652 - val_lambda_loss: 0.1669 - val_dense_1_acc: 0.9606 - val_lambda_acc: 0.9468\n",
            "Epoch 53/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.6580 - dense_1_loss: 0.5897 - lambda_loss: 0.1366 - dense_1_acc: 0.9775 - lambda_acc: 0.9643 - val_loss: 0.7103 - val_dense_1_loss: 0.6164 - val_lambda_loss: 0.1877 - val_dense_1_acc: 0.9556 - val_lambda_acc: 0.9199\n",
            "Epoch 54/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.6458 - dense_1_loss: 0.5771 - lambda_loss: 0.1375 - dense_1_acc: 0.9788 - lambda_acc: 0.9637 - val_loss: 0.7184 - val_dense_1_loss: 0.6166 - val_lambda_loss: 0.2035 - val_dense_1_acc: 0.9597 - val_lambda_acc: 0.9128\n",
            "Epoch 55/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.6229 - dense_1_loss: 0.5601 - lambda_loss: 0.1257 - dense_1_acc: 0.9833 - lambda_acc: 0.9724 - val_loss: 0.7040 - val_dense_1_loss: 0.6160 - val_lambda_loss: 0.1761 - val_dense_1_acc: 0.9618 - val_lambda_acc: 0.9392\n",
            "Epoch 56/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.6196 - dense_1_loss: 0.5519 - lambda_loss: 0.1355 - dense_1_acc: 0.9813 - lambda_acc: 0.9620 - val_loss: 0.8309 - val_dense_1_loss: 0.6468 - val_lambda_loss: 0.3682 - val_dense_1_acc: 0.9269 - val_lambda_acc: 0.8098\n",
            "Epoch 57/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.6108 - dense_1_loss: 0.5423 - lambda_loss: 0.1370 - dense_1_acc: 0.9810 - lambda_acc: 0.9605 - val_loss: 0.6424 - val_dense_1_loss: 0.5610 - val_lambda_loss: 0.1630 - val_dense_1_acc: 0.9680 - val_lambda_acc: 0.9538\n",
            "Epoch 58/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.5849 - dense_1_loss: 0.5246 - lambda_loss: 0.1206 - dense_1_acc: 0.9862 - lambda_acc: 0.9738 - val_loss: 0.6893 - val_dense_1_loss: 0.5706 - val_lambda_loss: 0.2374 - val_dense_1_acc: 0.9557 - val_lambda_acc: 0.8916\n",
            "Epoch 59/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.5841 - dense_1_loss: 0.5180 - lambda_loss: 0.1322 - dense_1_acc: 0.9851 - lambda_acc: 0.9639 - val_loss: 0.6607 - val_dense_1_loss: 0.5740 - val_lambda_loss: 0.1734 - val_dense_1_acc: 0.9563 - val_lambda_acc: 0.9430\n",
            "Epoch 60/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.5596 - dense_1_loss: 0.5022 - lambda_loss: 0.1148 - dense_1_acc: 0.9873 - lambda_acc: 0.9748 - val_loss: 0.6214 - val_dense_1_loss: 0.5288 - val_lambda_loss: 0.1852 - val_dense_1_acc: 0.9711 - val_lambda_acc: 0.9439\n",
            "Epoch 61/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.5581 - dense_1_loss: 0.4949 - lambda_loss: 0.1264 - dense_1_acc: 0.9876 - lambda_acc: 0.9688 - val_loss: 0.6619 - val_dense_1_loss: 0.5541 - val_lambda_loss: 0.2156 - val_dense_1_acc: 0.9665 - val_lambda_acc: 0.9198\n",
            "Epoch 62/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.5385 - dense_1_loss: 0.4814 - lambda_loss: 0.1142 - dense_1_acc: 0.9892 - lambda_acc: 0.9764 - val_loss: 0.6521 - val_dense_1_loss: 0.5514 - val_lambda_loss: 0.2015 - val_dense_1_acc: 0.9647 - val_lambda_acc: 0.9208\n",
            "Epoch 63/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.5386 - dense_1_loss: 0.4765 - lambda_loss: 0.1243 - dense_1_acc: 0.9879 - lambda_acc: 0.9690 - val_loss: 0.7666 - val_dense_1_loss: 0.5365 - val_lambda_loss: 0.4601 - val_dense_1_acc: 0.9331 - val_lambda_acc: 0.7046\n",
            "Epoch 64/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.5239 - dense_1_loss: 0.4645 - lambda_loss: 0.1187 - dense_1_acc: 0.9895 - lambda_acc: 0.9709 - val_loss: 0.6514 - val_dense_1_loss: 0.5211 - val_lambda_loss: 0.2606 - val_dense_1_acc: 0.9570 - val_lambda_acc: 0.8945\n",
            "Epoch 65/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.5203 - dense_1_loss: 0.4575 - lambda_loss: 0.1256 - dense_1_acc: 0.9892 - lambda_acc: 0.9665 - val_loss: 0.6681 - val_dense_1_loss: 0.5317 - val_lambda_loss: 0.2729 - val_dense_1_acc: 0.9465 - val_lambda_acc: 0.8653\n",
            "Epoch 66/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.5002 - dense_1_loss: 0.4447 - lambda_loss: 0.1110 - dense_1_acc: 0.9909 - lambda_acc: 0.9748 - val_loss: 0.5608 - val_dense_1_loss: 0.4703 - val_lambda_loss: 0.1811 - val_dense_1_acc: 0.9734 - val_lambda_acc: 0.9304\n",
            "Epoch 67/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4975 - dense_1_loss: 0.4379 - lambda_loss: 0.1191 - dense_1_acc: 0.9915 - lambda_acc: 0.9679 - val_loss: 0.6042 - val_dense_1_loss: 0.4885 - val_lambda_loss: 0.2315 - val_dense_1_acc: 0.9544 - val_lambda_acc: 0.9147\n",
            "Epoch 68/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4841 - dense_1_loss: 0.4282 - lambda_loss: 0.1118 - dense_1_acc: 0.9922 - lambda_acc: 0.9746 - val_loss: 0.7658 - val_dense_1_loss: 0.5577 - val_lambda_loss: 0.4162 - val_dense_1_acc: 0.9256 - val_lambda_acc: 0.7828\n",
            "Epoch 69/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4768 - dense_1_loss: 0.4204 - lambda_loss: 0.1128 - dense_1_acc: 0.9918 - lambda_acc: 0.9734 - val_loss: 0.5698 - val_dense_1_loss: 0.4722 - val_lambda_loss: 0.1951 - val_dense_1_acc: 0.9736 - val_lambda_acc: 0.9204\n",
            "Epoch 70/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4604 - dense_1_loss: 0.4090 - lambda_loss: 0.1028 - dense_1_acc: 0.9932 - lambda_acc: 0.9784 - val_loss: 0.5503 - val_dense_1_loss: 0.4567 - val_lambda_loss: 0.1870 - val_dense_1_acc: 0.9701 - val_lambda_acc: 0.9297\n",
            "Epoch 71/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4576 - dense_1_loss: 0.4029 - lambda_loss: 0.1094 - dense_1_acc: 0.9936 - lambda_acc: 0.9752 - val_loss: 0.5644 - val_dense_1_loss: 0.4536 - val_lambda_loss: 0.2217 - val_dense_1_acc: 0.9663 - val_lambda_acc: 0.9109\n",
            "Epoch 72/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4503 - dense_1_loss: 0.3957 - lambda_loss: 0.1092 - dense_1_acc: 0.9938 - lambda_acc: 0.9773 - val_loss: 0.6142 - val_dense_1_loss: 0.5049 - val_lambda_loss: 0.2186 - val_dense_1_acc: 0.9595 - val_lambda_acc: 0.9195\n",
            "Epoch 73/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4452 - dense_1_loss: 0.3893 - lambda_loss: 0.1116 - dense_1_acc: 0.9937 - lambda_acc: 0.9725 - val_loss: 0.7176 - val_dense_1_loss: 0.5195 - val_lambda_loss: 0.3962 - val_dense_1_acc: 0.8942 - val_lambda_acc: 0.7784\n",
            "Epoch 74/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4384 - dense_1_loss: 0.3819 - lambda_loss: 0.1130 - dense_1_acc: 0.9932 - lambda_acc: 0.9716 - val_loss: 0.7839 - val_dense_1_loss: 0.5605 - val_lambda_loss: 0.4468 - val_dense_1_acc: 0.9339 - val_lambda_acc: 0.7506\n",
            "Epoch 75/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4272 - dense_1_loss: 0.3738 - lambda_loss: 0.1067 - dense_1_acc: 0.9940 - lambda_acc: 0.9754 - val_loss: 0.5334 - val_dense_1_loss: 0.4249 - val_lambda_loss: 0.2170 - val_dense_1_acc: 0.9741 - val_lambda_acc: 0.9263\n",
            "Epoch 76/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4086 - dense_1_loss: 0.3619 - lambda_loss: 0.0933 - dense_1_acc: 0.9955 - lambda_acc: 0.9834 - val_loss: 0.5074 - val_dense_1_loss: 0.4189 - val_lambda_loss: 0.1771 - val_dense_1_acc: 0.9763 - val_lambda_acc: 0.9399\n",
            "Epoch 77/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4078 - dense_1_loss: 0.3572 - lambda_loss: 0.1013 - dense_1_acc: 0.9955 - lambda_acc: 0.9807 - val_loss: 0.5210 - val_dense_1_loss: 0.4197 - val_lambda_loss: 0.2025 - val_dense_1_acc: 0.9747 - val_lambda_acc: 0.9370\n",
            "Epoch 78/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4063 - dense_1_loss: 0.3526 - lambda_loss: 0.1074 - dense_1_acc: 0.9953 - lambda_acc: 0.9768 - val_loss: 0.6423 - val_dense_1_loss: 0.4555 - val_lambda_loss: 0.3736 - val_dense_1_acc: 0.9414 - val_lambda_acc: 0.7904\n",
            "Epoch 79/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.4044 - dense_1_loss: 0.3483 - lambda_loss: 0.1123 - dense_1_acc: 0.9936 - lambda_acc: 0.9732 - val_loss: 0.5595 - val_dense_1_loss: 0.4238 - val_lambda_loss: 0.2715 - val_dense_1_acc: 0.9687 - val_lambda_acc: 0.8565\n",
            "Epoch 80/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3888 - dense_1_loss: 0.3379 - lambda_loss: 0.1019 - dense_1_acc: 0.9954 - lambda_acc: 0.9786 - val_loss: 0.6018 - val_dense_1_loss: 0.4545 - val_lambda_loss: 0.2947 - val_dense_1_acc: 0.9548 - val_lambda_acc: 0.8748\n",
            "Epoch 81/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3756 - dense_1_loss: 0.3288 - lambda_loss: 0.0935 - dense_1_acc: 0.9958 - lambda_acc: 0.9803 - val_loss: 0.5267 - val_dense_1_loss: 0.3781 - val_lambda_loss: 0.2971 - val_dense_1_acc: 0.9696 - val_lambda_acc: 0.8511\n",
            "Epoch 82/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3723 - dense_1_loss: 0.3234 - lambda_loss: 0.0978 - dense_1_acc: 0.9960 - lambda_acc: 0.9792 - val_loss: 0.5280 - val_dense_1_loss: 0.4034 - val_lambda_loss: 0.2492 - val_dense_1_acc: 0.9686 - val_lambda_acc: 0.9169\n",
            "Epoch 83/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3764 - dense_1_loss: 0.3212 - lambda_loss: 0.1103 - dense_1_acc: 0.9958 - lambda_acc: 0.9728 - val_loss: 0.4900 - val_dense_1_loss: 0.3599 - val_lambda_loss: 0.2603 - val_dense_1_acc: 0.9745 - val_lambda_acc: 0.8555\n",
            "Epoch 84/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3664 - dense_1_loss: 0.3140 - lambda_loss: 0.1048 - dense_1_acc: 0.9960 - lambda_acc: 0.9756 - val_loss: 0.4960 - val_dense_1_loss: 0.3941 - val_lambda_loss: 0.2038 - val_dense_1_acc: 0.9724 - val_lambda_acc: 0.9285\n",
            "Epoch 85/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3572 - dense_1_loss: 0.3074 - lambda_loss: 0.0996 - dense_1_acc: 0.9963 - lambda_acc: 0.9807 - val_loss: 0.5132 - val_dense_1_loss: 0.3726 - val_lambda_loss: 0.2813 - val_dense_1_acc: 0.9690 - val_lambda_acc: 0.8677\n",
            "Epoch 86/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3527 - dense_1_loss: 0.3015 - lambda_loss: 0.1023 - dense_1_acc: 0.9964 - lambda_acc: 0.9751 - val_loss: 0.4379 - val_dense_1_loss: 0.3526 - val_lambda_loss: 0.1706 - val_dense_1_acc: 0.9793 - val_lambda_acc: 0.9458\n",
            "Epoch 87/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.3442 - dense_1_loss: 0.2949 - lambda_loss: 0.0986 - dense_1_acc: 0.9969 - lambda_acc: 0.9809 - val_loss: 0.4820 - val_dense_1_loss: 0.3547 - val_lambda_loss: 0.2546 - val_dense_1_acc: 0.9767 - val_lambda_acc: 0.8525\n",
            "Epoch 88/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3407 - dense_1_loss: 0.2900 - lambda_loss: 0.1015 - dense_1_acc: 0.9969 - lambda_acc: 0.9761 - val_loss: 0.4532 - val_dense_1_loss: 0.3415 - val_lambda_loss: 0.2233 - val_dense_1_acc: 0.9774 - val_lambda_acc: 0.9284\n",
            "Epoch 89/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.3312 - dense_1_loss: 0.2830 - lambda_loss: 0.0966 - dense_1_acc: 0.9972 - lambda_acc: 0.9794 - val_loss: 0.4331 - val_dense_1_loss: 0.3398 - val_lambda_loss: 0.1866 - val_dense_1_acc: 0.9788 - val_lambda_acc: 0.9380\n",
            "Epoch 90/100\n",
            "118/118 [==============================] - 13s 112ms/step - loss: 0.3289 - dense_1_loss: 0.2790 - lambda_loss: 0.0999 - dense_1_acc: 0.9969 - lambda_acc: 0.9775 - val_loss: 0.4614 - val_dense_1_loss: 0.3443 - val_lambda_loss: 0.2341 - val_dense_1_acc: 0.9783 - val_lambda_acc: 0.8905\n",
            "Epoch 91/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.3235 - dense_1_loss: 0.2736 - lambda_loss: 0.0999 - dense_1_acc: 0.9972 - lambda_acc: 0.9781 - val_loss: 0.4615 - val_dense_1_loss: 0.3431 - val_lambda_loss: 0.2369 - val_dense_1_acc: 0.9712 - val_lambda_acc: 0.9160\n",
            "Epoch 92/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.3222 - dense_1_loss: 0.2701 - lambda_loss: 0.1044 - dense_1_acc: 0.9970 - lambda_acc: 0.9732 - val_loss: 0.4457 - val_dense_1_loss: 0.3274 - val_lambda_loss: 0.2364 - val_dense_1_acc: 0.9788 - val_lambda_acc: 0.9151\n",
            "Epoch 93/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.3076 - dense_1_loss: 0.2619 - lambda_loss: 0.0915 - dense_1_acc: 0.9973 - lambda_acc: 0.9830 - val_loss: 0.4657 - val_dense_1_loss: 0.3282 - val_lambda_loss: 0.2751 - val_dense_1_acc: 0.9713 - val_lambda_acc: 0.8831\n",
            "Epoch 94/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.3055 - dense_1_loss: 0.2575 - lambda_loss: 0.0961 - dense_1_acc: 0.9974 - lambda_acc: 0.9779 - val_loss: 0.6459 - val_dense_1_loss: 0.4210 - val_lambda_loss: 0.4498 - val_dense_1_acc: 0.9217 - val_lambda_acc: 0.8185\n",
            "Epoch 95/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.2967 - dense_1_loss: 0.2513 - lambda_loss: 0.0908 - dense_1_acc: 0.9976 - lambda_acc: 0.9821 - val_loss: 0.4391 - val_dense_1_loss: 0.3198 - val_lambda_loss: 0.2386 - val_dense_1_acc: 0.9758 - val_lambda_acc: 0.8978\n",
            "Epoch 96/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.3004 - dense_1_loss: 0.2499 - lambda_loss: 0.1009 - dense_1_acc: 0.9975 - lambda_acc: 0.9778 - val_loss: 0.5211 - val_dense_1_loss: 0.3203 - val_lambda_loss: 0.4016 - val_dense_1_acc: 0.9731 - val_lambda_acc: 0.8180\n",
            "Epoch 97/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.2910 - dense_1_loss: 0.2435 - lambda_loss: 0.0949 - dense_1_acc: 0.9977 - lambda_acc: 0.9785 - val_loss: 0.4850 - val_dense_1_loss: 0.3098 - val_lambda_loss: 0.3503 - val_dense_1_acc: 0.9663 - val_lambda_acc: 0.8063\n",
            "Epoch 98/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.2840 - dense_1_loss: 0.2383 - lambda_loss: 0.0913 - dense_1_acc: 0.9976 - lambda_acc: 0.9809 - val_loss: 0.4247 - val_dense_1_loss: 0.3133 - val_lambda_loss: 0.2227 - val_dense_1_acc: 0.9732 - val_lambda_acc: 0.9224\n",
            "Epoch 99/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.2770 - dense_1_loss: 0.2331 - lambda_loss: 0.0878 - dense_1_acc: 0.9978 - lambda_acc: 0.9828 - val_loss: 0.4760 - val_dense_1_loss: 0.3312 - val_lambda_loss: 0.2895 - val_dense_1_acc: 0.9757 - val_lambda_acc: 0.8993\n",
            "Epoch 100/100\n",
            "118/118 [==============================] - 13s 113ms/step - loss: 0.2732 - dense_1_loss: 0.2290 - lambda_loss: 0.0884 - dense_1_acc: 0.9980 - lambda_acc: 0.9829 - val_loss: 0.4499 - val_dense_1_loss: 0.2853 - val_lambda_loss: 0.3292 - val_dense_1_acc: 0.9753 - val_lambda_acc: 0.8472\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3880105588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQAmb6rZM2zV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "a4c1194a-708b-422a-de0e-38b95790b15e"
      },
      "source": [
        "func=K.function([model.input],[model.get_layer('out1').output])\n",
        "test_features= func([x_test])[0]\n",
        "print(test_features.shape)\n",
        "\n",
        "test_centers=np.dot(np.transpose(y_test_one_hot),test_features)\n",
        "test_centers_count=np.sum(np.transpose(y_test_one_hot),axis=1,keepdims=True)\n",
        "test_centers/=test_centers_count\n",
        "print(test_centers.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 2)\n",
            "(10, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVhgUHudM2zj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8a89247b-dd1a-4e84-94b0-6d4daeb60f2e"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.scatter(test_features[:,0],test_features[:,1],c=y_test,edgecolor=\"none\",s=5)\n",
        "plt.scatter(test_centers[:,0],test_centers[:,1],c=\"black\",marker=\"*\",edgecolor=\"none\",s=50)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tru1pJchM2zx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}