classifier_confidence_boundary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from tensorflow import set_random_seed\n",
    "# for reproducibility \n",
    "np.random.seed(1)\n",
    "set_random_seed(1)\n",
    "\n",
    "from keras.layers import Dense, Input, concatenate\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Lambda, Dropout, Activation, Lambda\n",
    "from keras.layers import Reshape, Conv2DTranspose\n",
    "from keras.models import Model, Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.datasets import mnist\n",
    "from keras.datasets import fashion_mnist\n",
    "from keras.losses import mse, binary_crossentropy\n",
    "from keras.utils import plot_model\n",
    "from keras import backend as K\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multi-input classifier - boundary outlier samples\n",
    "\n",
    "class Classifier:\n",
    "\n",
    "    # 3 Class classifier - class 2 represents outliers and class 0,1 represent inliers.\n",
    "    # Class 0 sampled from a circle centered at (x, y) = (-2, 0) with radius 0.5\n",
    "    # Class 1 sampled from a circle centered at (x, y) = (+2, 0) with radius 0.5\n",
    "    # Class 2 sampled from rectagle with origin (x, y) = (-3, -1) with h=2, w=6 excluding regions covered by inliers.\n",
    "\n",
    "    # Generate n points uniformly within a circle of radius r, centered at (x, y)\n",
    "    # return as a numpy array n x 2 of dtype = float32\n",
    "    def __init__(self):\n",
    "        self.number_of_classes = 2\n",
    "        self.input_dim = 2\n",
    "        self.epochs = 300\n",
    "        self.batch_size = 32\n",
    "        self.data, self.labels, self.data_out, self.labels_out = self.get_data()\n",
    "        # Oultiers from rectagle outside 2 circles\n",
    "        self.data_outlier, self.labels_outlier = self.get_outlier_data()\n",
    "\n",
    "        print(self.data.shape, self.labels.shape)\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.data, self.labels, test_size=0.5, shuffle=True, stratify=self.labels,\n",
    "                                                            random_state=1)\n",
    "        self.X_train_outliers, self.X_test_outliers, self.y_train_outliers, self.y_test_outliers = train_test_split(self.data_out, self.labels_out, test_size=0.5, shuffle=True,\n",
    "                                                            random_state=1)\n",
    "        self.multi_input_classifier, self.model = self.get_model()\n",
    "\n",
    "\n",
    "        self.mu = np.mean(np.concatenate((self.X_train, self.X_train_outliers), axis=0), axis=0)\n",
    "        self.sigma = np.std(np.concatenate((self.X_train, self.X_train_outliers), axis=0), axis=0)\n",
    "\n",
    "        self.data = self.preprocess_data(self.data)\n",
    "        self.data_out = self.preprocess_data(self.data_out)\n",
    "        self.data_outlier = self.preprocess_data(self.data_outlier)\n",
    "        self.X_train = self.preprocess_data(self.X_train)\n",
    "        self.X_test = self.preprocess_data(self.X_test)\n",
    "        self.X_train_outlier = self.preprocess_data(self.X_train_outliers)\n",
    "        self.X_test_outliers = self.preprocess_data(self.X_test_outliers)\n",
    "\n",
    "\n",
    "    def preprocess_data(self, x):\n",
    "        # return (x-self.mu)/self.sigma\n",
    "        return x\n",
    "\n",
    "    def get_circle_data(self, x, y, r, n):\n",
    "        rho = np.sqrt(np.random.uniform(0, r, n))\n",
    "        phi = np.random.uniform(0, 2*np.pi, n)\n",
    "\n",
    "        data = np.zeros((n, 2))\n",
    "        data[:, 0] = rho * np.cos(phi) + x\n",
    "        data[:, 1] = rho * np.sin(phi) + y\n",
    "        data = data.astype(np.float32)\n",
    "        return data\n",
    "        # plt.scatter(x, y, s = 4)\n",
    "    \n",
    "    # return True if point (x1, y1) lies inside a circle defined by (x, y, r+0.1)\n",
    "    def is_in_circle(self, x, y, r, x1, y1):\n",
    "        return (x-x1)**2 + (y-y1)**2 <= (r+0.1)**2\n",
    "\n",
    "\n",
    "    \n",
    "    # generate n outlier points uniformly sampled within rectagle (x1, y1), (h, w) excuding the inlier circles\n",
    "    def get_outlier_data_sub(self, x1, y1, h, w, cx1, cy1, r1, cx2, cy2, r2, n):\n",
    "        count = 0\n",
    "        data = np.zeros((n, 2))\n",
    "        while count < n:\n",
    "            x = np.random.uniform(x1, x1+w, 1)\n",
    "            y = np.random.uniform(y1, y1+h, 1)\n",
    "            if (not self.is_in_circle(cx1, cy1, r1, x, y)) and (not self.is_in_circle(cx2, cy2, r2, x, y)):\n",
    "                data[count,0] = x\n",
    "                data[count,1] = y\n",
    "                count += 1\n",
    "        return data\n",
    "\n",
    "    def get_outlier_data(self):\n",
    "        data = self.get_outlier_data_sub(-50, -50, 100, 100, -2, 0, 1+0.3, +2, 0, 1+0.3, 1000)\n",
    "        #data = self.get_outlier_data_sub(-50, -50, 100, 100, -2, 0, 1+0.3, +2, 0, 1+0.3, 1000)\n",
    "        labels = np.ones((1000,1))*2\n",
    "        return data.astype(np.float32), labels.astype(np.float32)\n",
    "\n",
    "    \n",
    "    # gets a point sampled from region within concentric cercles centered at (x, y) with radii r1 and r2. \n",
    "    def get_random_sample_from_strip(self, x, y, r1, r2):\n",
    "        r = np.random.uniform(r1, r2)\n",
    "        x1 = np.random.normal(0, r)\n",
    "        y1 = np.random.normal(0, r)\n",
    "        d = math.sqrt(x1**2 + y1**2)/r\n",
    "        return (x1/d)+x, (y1/d)+y\n",
    "\n",
    "    \n",
    "    def get_boundary_outlier_data(self, cx1, cy1, r1, cx2, cy2, r2, n):\n",
    "        count = 0\n",
    "        data = np.zeros((n, 2))\n",
    "        # boundary of first circle\n",
    "        while count < n/2:\n",
    "            data[count,0], data[count,1] = self.get_random_sample_from_strip(cx1, cy1, r1+0.1, r1+0.3)\n",
    "            count += 1\n",
    "        # boundary of second circle\n",
    "        while count < n:\n",
    "            data[count,0], data[count,1] = self.get_random_sample_from_strip(cx2, cy2, r2+0.1, r2+0.3)\n",
    "            count += 1\n",
    "        return data\n",
    "    \n",
    "    def get_data(self):\n",
    "        # class 0 data\n",
    "        data = self.get_circle_data(-2, 0, 1, 1000)\n",
    "        labels = np.ones((1000, 1))*0\n",
    "        print(labels.shape)\n",
    "        # class 1 data\n",
    "        data = np.concatenate((data, self.get_circle_data(+2, 0, 1, 1000)), axis=0)\n",
    "        labels = np.concatenate((labels, np.ones((1000, 1))*1), axis=0)\n",
    "        print(labels.shape)\n",
    "        # boundary outlier data\n",
    "        data_out = self.get_boundary_outlier_data(-2, 0, 1, +2, 0, 1, 2000)\n",
    "        labels_out = np.ones((2000,1))*2\n",
    "        print(labels.shape)\n",
    "        return data.astype(np.float32), labels.astype(np.float32), data_out.astype(np.float32), labels_out.astype(np.float32)\n",
    "\n",
    "\n",
    "    def base_model(self):\n",
    "        model = Sequential()\n",
    "        model.add(Dense(500, input_shape=(self.input_dim,)))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(500))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(self.number_of_classes))\n",
    "        # model.add(Convolution2D(10,3,3, border_mode='same'))\n",
    "        # model.add(GlobalAveragePooling2D())\n",
    "        model.add(Activation('softmax'))\n",
    "        #model.compile(loss='binary_crossentropy', optimizer=Adam(0.001), metrics=['accuracy'])\n",
    "        return model\n",
    "\n",
    "    def get_model(self):\n",
    "        # instantiate classifier model\n",
    "        classifier = self.base_model()\n",
    "        classifier.compile(loss='categorical_crossentropy', optimizer=Adam(0.01))\n",
    "\n",
    "        # just the classifier\n",
    "        input_data = Input(shape=(self.input_dim, ))\n",
    "        y_classifier = classifier(input_data)\n",
    "\n",
    "\n",
    "        # 2 input classifier\n",
    "        input_a = Input(shape=(self.input_dim, ))\n",
    "        processed_a = classifier(input_a)\n",
    "        input_b = Input(shape=(self.input_dim, ))\n",
    "        processed_b = classifier(input_b)\n",
    "\n",
    "        out_a = Lambda(lambda x: x, name = 'out_a')(processed_a)\n",
    "        out_b = Lambda(lambda x: x, name = 'out_b')(processed_b)\n",
    "\n",
    "        multi_input_classifier = Model(inputs=[input_a, input_b], outputs=[out_a, out_b])\n",
    "        multi_input_classifier.compile(loss={'out_a':'categorical_crossentropy', 'out_b':'categorical_crossentropy'}, \n",
    "                                       loss_weights={'out_a': 1.0, 'out_b': 1.0}, optimizer=Adam(0.01), \n",
    "                                       metrics={'out_a':['acc'], 'out_b':['categorical_crossentropy']})\n",
    "\n",
    "\n",
    "        mnist_model = Model(input_data, y_classifier)\n",
    "        mnist_model.compile(loss='categorical_crossentropy', optimizer=Adam(0.01), metrics=['acc'])\n",
    "\n",
    "        return multi_input_classifier, mnist_model\n",
    "\n",
    "\n",
    "    def train(self):\n",
    "        # fit the model\n",
    "        Y_train = np_utils.to_categorical(self.y_train, self.number_of_classes)\n",
    "        Y_train_outliers = np.ones((self.y_train_outliers.shape[0], self.number_of_classes))\n",
    "        Y_test = np_utils.to_categorical(self.y_test, self.number_of_classes)\n",
    "        Y_test_outliers = np.ones((self.y_test_outliers.shape[0], self.number_of_classes))\n",
    "\n",
    "        self.multi_input_classifier.fit([self.X_train, self.X_train_outliers], [Y_train, Y_train_outliers], \n",
    "                                        epochs=self.epochs, validation_data=([self.X_test, self.X_test_outliers], [Y_test, Y_test_outliers]), batch_size=self.batch_size)     \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1)\n",
      "(2000, 1)\n",
      "(2000, 1)\n",
      "((2000, 2), (2000, 1))\n",
      "Train on 1000 samples, validate on 1000 samples\n",
      "Epoch 1/300\n",
      "1000/1000 [==============================] - 0s 410us/step - loss: 2.0224 - out_a_loss: 0.4289 - out_b_loss: 1.5934 - out_a_acc: 0.8940 - out_b_categorical_crossentropy: 1.5934 - val_loss: 1.7791 - val_out_a_loss: 0.2053 - val_out_b_loss: 1.5738 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.5738\n",
      "Epoch 2/300\n",
      "1000/1000 [==============================] - 0s 130us/step - loss: 1.7402 - out_a_loss: 0.2353 - out_b_loss: 1.5049 - out_a_acc: 0.9850 - out_b_categorical_crossentropy: 1.5049 - val_loss: 1.6402 - val_out_a_loss: 0.2053 - val_out_b_loss: 1.4349 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4349\n",
      "Epoch 3/300\n",
      "1000/1000 [==============================] - 0s 117us/step - loss: 1.5884 - out_a_loss: 0.1331 - out_b_loss: 1.4553 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.4553 - val_loss: 1.5597 - val_out_a_loss: 0.1396 - val_out_b_loss: 1.4201 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4201\n",
      "Epoch 4/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.5900 - out_a_loss: 0.1388 - out_b_loss: 1.4512 - out_a_acc: 0.9820 - out_b_categorical_crossentropy: 1.4512 - val_loss: 1.5075 - val_out_a_loss: 0.0760 - val_out_b_loss: 1.4316 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4316\n",
      "Epoch 5/300\n",
      "1000/1000 [==============================] - 0s 112us/step - loss: 1.5264 - out_a_loss: 0.0920 - out_b_loss: 1.4344 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.4344 - val_loss: 1.4989 - val_out_a_loss: 0.0876 - val_out_b_loss: 1.4113 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4113\n",
      "Epoch 6/300\n",
      "1000/1000 [==============================] - 0s 117us/step - loss: 1.4866 - out_a_loss: 0.0717 - out_b_loss: 1.4149 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.4149 - val_loss: 1.4423 - val_out_a_loss: 0.0485 - val_out_b_loss: 1.3938 - val_out_a_acc: 0.9990 - val_out_b_categorical_crossentropy: 1.3938\n",
      "Epoch 7/300\n",
      "1000/1000 [==============================] - 0s 114us/step - loss: 1.4538 - out_a_loss: 0.0481 - out_b_loss: 1.4057 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.4057 - val_loss: 1.5130 - val_out_a_loss: 0.1140 - val_out_b_loss: 1.3991 - val_out_a_acc: 0.9580 - val_out_b_categorical_crossentropy: 1.3991\n",
      "Epoch 8/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.6654 - out_a_loss: 0.2190 - out_b_loss: 1.4464 - out_a_acc: 0.9310 - out_b_categorical_crossentropy: 1.4464 - val_loss: 1.5552 - val_out_a_loss: 0.1106 - val_out_b_loss: 1.4446 - val_out_a_acc: 0.9890 - val_out_b_categorical_crossentropy: 1.4446\n",
      "Epoch 9/300\n",
      "1000/1000 [==============================] - 0s 116us/step - loss: 1.5102 - out_a_loss: 0.0873 - out_b_loss: 1.4229 - out_a_acc: 0.9860 - out_b_categorical_crossentropy: 1.4229 - val_loss: 1.6095 - val_out_a_loss: 0.0071 - val_out_b_loss: 1.6024 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.6024\n",
      "Epoch 10/300\n",
      "1000/1000 [==============================] - 0s 120us/step - loss: 1.4636 - out_a_loss: 0.0524 - out_b_loss: 1.4112 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.4112 - val_loss: 1.4179 - val_out_a_loss: 0.0182 - val_out_b_loss: 1.3997 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3997\n",
      "Epoch 11/300\n",
      "1000/1000 [==============================] - 0s 127us/step - loss: 1.4461 - out_a_loss: 0.0417 - out_b_loss: 1.4044 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.4044 - val_loss: 1.4691 - val_out_a_loss: 0.0435 - val_out_b_loss: 1.4257 - val_out_a_acc: 0.9910 - val_out_b_categorical_crossentropy: 1.4257\n",
      "Epoch 12/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4684 - out_a_loss: 0.0601 - out_b_loss: 1.4082 - out_a_acc: 0.9760 - out_b_categorical_crossentropy: 1.4082 - val_loss: 1.6059 - val_out_a_loss: 0.0692 - val_out_b_loss: 1.5367 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.5367\n",
      "Epoch 13/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.4770 - out_a_loss: 0.0628 - out_b_loss: 1.4142 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.4142 - val_loss: 1.4221 - val_out_a_loss: 0.0175 - val_out_b_loss: 1.4046 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4046\n",
      "Epoch 14/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.5290 - out_a_loss: 0.1180 - out_b_loss: 1.4110 - out_a_acc: 0.9680 - out_b_categorical_crossentropy: 1.4110 - val_loss: 1.5132 - val_out_a_loss: 0.0916 - val_out_b_loss: 1.4215 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4215\n",
      "Epoch 15/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.4619 - out_a_loss: 0.0479 - out_b_loss: 1.4140 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.4140 - val_loss: 1.5393 - val_out_a_loss: 0.1466 - val_out_b_loss: 1.3927 - val_out_a_acc: 0.8650 - val_out_b_categorical_crossentropy: 1.3927\n",
      "Epoch 16/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.4786 - out_a_loss: 0.0715 - out_b_loss: 1.4071 - out_a_acc: 0.9790 - out_b_categorical_crossentropy: 1.4071 - val_loss: 1.4240 - val_out_a_loss: 0.0180 - val_out_b_loss: 1.4060 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4060\n",
      "Epoch 17/300\n",
      "1000/1000 [==============================] - 0s 114us/step - loss: 1.4671 - out_a_loss: 0.0625 - out_b_loss: 1.4047 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.4047 - val_loss: 1.4264 - val_out_a_loss: 0.0347 - val_out_b_loss: 1.3917 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3917\n",
      "Epoch 18/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.4365 - out_a_loss: 0.0362 - out_b_loss: 1.4003 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.4003 - val_loss: 1.4319 - val_out_a_loss: 0.0377 - val_out_b_loss: 1.3942 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3942\n",
      "Epoch 19/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.4474 - out_a_loss: 0.0448 - out_b_loss: 1.4025 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.4025 - val_loss: 1.4855 - val_out_a_loss: 0.0889 - val_out_b_loss: 1.3967 - val_out_a_acc: 0.9990 - val_out_b_categorical_crossentropy: 1.3967\n",
      "Epoch 20/300\n",
      "1000/1000 [==============================] - 0s 112us/step - loss: 1.4354 - out_a_loss: 0.0342 - out_b_loss: 1.4012 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.4012 - val_loss: 1.4020 - val_out_a_loss: 0.0154 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 21/300\n",
      "1000/1000 [==============================] - 0s 112us/step - loss: 1.4205 - out_a_loss: 0.0251 - out_b_loss: 1.3954 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3954 - val_loss: 1.4345 - val_out_a_loss: 0.0449 - val_out_b_loss: 1.3897 - val_out_a_acc: 0.9930 - val_out_b_categorical_crossentropy: 1.3897\n",
      "Epoch 22/300\n",
      "1000/1000 [==============================] - 0s 117us/step - loss: 1.4677 - out_a_loss: 0.0622 - out_b_loss: 1.4055 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.4055 - val_loss: 1.4415 - val_out_a_loss: 0.0417 - val_out_b_loss: 1.3998 - val_out_a_acc: 0.9990 - val_out_b_categorical_crossentropy: 1.3998\n",
      "Epoch 23/300\n",
      "1000/1000 [==============================] - 0s 114us/step - loss: 1.4930 - out_a_loss: 0.0862 - out_b_loss: 1.4068 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.4068 - val_loss: 1.4620 - val_out_a_loss: 0.0715 - val_out_b_loss: 1.3904 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3904\n",
      "Epoch 24/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.5150 - out_a_loss: 0.0985 - out_b_loss: 1.4165 - out_a_acc: 0.9810 - out_b_categorical_crossentropy: 1.4165 - val_loss: 1.4604 - val_out_a_loss: 0.0439 - val_out_b_loss: 1.4165 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4165\n",
      "Epoch 25/300\n",
      "1000/1000 [==============================] - 0s 112us/step - loss: 1.4552 - out_a_loss: 0.0495 - out_b_loss: 1.4056 - out_a_acc: 0.9830 - out_b_categorical_crossentropy: 1.4056 - val_loss: 1.4737 - val_out_a_loss: 0.0736 - val_out_b_loss: 1.4000 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/300\n",
      "1000/1000 [==============================] - 0s 115us/step - loss: 1.4166 - out_a_loss: 0.0205 - out_b_loss: 1.3961 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3961 - val_loss: 1.4056 - val_out_a_loss: 0.0180 - val_out_b_loss: 1.3876 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3876\n",
      "Epoch 27/300\n",
      "1000/1000 [==============================] - 0s 112us/step - loss: 1.4225 - out_a_loss: 0.0246 - out_b_loss: 1.3980 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3980 - val_loss: 1.4027 - val_out_a_loss: 0.0129 - val_out_b_loss: 1.3898 - val_out_a_acc: 0.9990 - val_out_b_categorical_crossentropy: 1.3898\n",
      "Epoch 28/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.4378 - out_a_loss: 0.0403 - out_b_loss: 1.3975 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.3975 - val_loss: 1.5504 - val_out_a_loss: 0.1530 - val_out_b_loss: 1.3974 - val_out_a_acc: 0.9010 - val_out_b_categorical_crossentropy: 1.3974\n",
      "Epoch 29/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4765 - out_a_loss: 0.0684 - out_b_loss: 1.4081 - out_a_acc: 0.9790 - out_b_categorical_crossentropy: 1.4081 - val_loss: 1.4982 - val_out_a_loss: 0.0979 - val_out_b_loss: 1.4002 - val_out_a_acc: 0.9200 - val_out_b_categorical_crossentropy: 1.4002\n",
      "Epoch 30/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.4461 - out_a_loss: 0.0475 - out_b_loss: 1.3987 - out_a_acc: 0.9760 - out_b_categorical_crossentropy: 1.3987 - val_loss: 1.4262 - val_out_a_loss: 0.0354 - val_out_b_loss: 1.3908 - val_out_a_acc: 0.9990 - val_out_b_categorical_crossentropy: 1.3908\n",
      "Epoch 31/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4531 - out_a_loss: 0.0515 - out_b_loss: 1.4016 - out_a_acc: 0.9730 - out_b_categorical_crossentropy: 1.4016 - val_loss: 1.4224 - val_out_a_loss: 0.0302 - val_out_b_loss: 1.3921 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3921\n",
      "Epoch 32/300\n",
      "1000/1000 [==============================] - 0s 128us/step - loss: 1.4604 - out_a_loss: 0.0553 - out_b_loss: 1.4051 - out_a_acc: 0.9820 - out_b_categorical_crossentropy: 1.4051 - val_loss: 1.4780 - val_out_a_loss: 0.0904 - val_out_b_loss: 1.3876 - val_out_a_acc: 0.9890 - val_out_b_categorical_crossentropy: 1.3876\n",
      "Epoch 33/300\n",
      "1000/1000 [==============================] - 0s 134us/step - loss: 1.4368 - out_a_loss: 0.0367 - out_b_loss: 1.4001 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.4001 - val_loss: 1.4287 - val_out_a_loss: 0.0327 - val_out_b_loss: 1.3960 - val_out_a_acc: 0.9880 - val_out_b_categorical_crossentropy: 1.3960\n",
      "Epoch 34/300\n",
      "1000/1000 [==============================] - 0s 116us/step - loss: 1.4140 - out_a_loss: 0.0207 - out_b_loss: 1.3933 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3933 - val_loss: 1.3986 - val_out_a_loss: 0.0075 - val_out_b_loss: 1.3911 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3911\n",
      "Epoch 35/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4275 - out_a_loss: 0.0302 - out_b_loss: 1.3973 - out_a_acc: 0.9850 - out_b_categorical_crossentropy: 1.3973 - val_loss: 1.4343 - val_out_a_loss: 0.0446 - val_out_b_loss: 1.3897 - val_out_a_acc: 0.9950 - val_out_b_categorical_crossentropy: 1.3897\n",
      "Epoch 36/300\n",
      "1000/1000 [==============================] - 0s 138us/step - loss: 1.4300 - out_a_loss: 0.0321 - out_b_loss: 1.3979 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3979 - val_loss: 1.4081 - val_out_a_loss: 0.0051 - val_out_b_loss: 1.4030 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4030\n",
      "Epoch 37/300\n",
      "1000/1000 [==============================] - 0s 135us/step - loss: 1.4249 - out_a_loss: 0.0294 - out_b_loss: 1.3955 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3955 - val_loss: 1.3964 - val_out_a_loss: 0.0089 - val_out_b_loss: 1.3876 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3876\n",
      "Epoch 38/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.4292 - out_a_loss: 0.0345 - out_b_loss: 1.3947 - out_a_acc: 0.9860 - out_b_categorical_crossentropy: 1.3947 - val_loss: 1.4971 - val_out_a_loss: 0.0916 - val_out_b_loss: 1.4055 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4055\n",
      "Epoch 39/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.4628 - out_a_loss: 0.0601 - out_b_loss: 1.4028 - out_a_acc: 0.9900 - out_b_categorical_crossentropy: 1.4028 - val_loss: 1.4245 - val_out_a_loss: 0.0269 - val_out_b_loss: 1.3976 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3976\n",
      "Epoch 40/300\n",
      "1000/1000 [==============================] - 0s 128us/step - loss: 1.4344 - out_a_loss: 0.0369 - out_b_loss: 1.3975 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3975 - val_loss: 1.4481 - val_out_a_loss: 0.0588 - val_out_b_loss: 1.3893 - val_out_a_acc: 0.9710 - val_out_b_categorical_crossentropy: 1.3893\n",
      "Epoch 41/300\n",
      "1000/1000 [==============================] - 0s 134us/step - loss: 1.4239 - out_a_loss: 0.0301 - out_b_loss: 1.3938 - out_a_acc: 0.9880 - out_b_categorical_crossentropy: 1.3938 - val_loss: 1.3981 - val_out_a_loss: 0.0111 - val_out_b_loss: 1.3870 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 42/300\n",
      "1000/1000 [==============================] - 0s 115us/step - loss: 1.4327 - out_a_loss: 0.0338 - out_b_loss: 1.3989 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.3989 - val_loss: 1.4140 - val_out_a_loss: 0.0275 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 43/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4005 - out_a_loss: 0.0107 - out_b_loss: 1.3898 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3898 - val_loss: 1.4187 - val_out_a_loss: 0.0027 - val_out_b_loss: 1.4160 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4160\n",
      "Epoch 44/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3983 - out_a_loss: 0.0076 - out_b_loss: 1.3906 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3906 - val_loss: 1.4292 - val_out_a_loss: 0.0422 - val_out_b_loss: 1.3870 - val_out_a_acc: 0.9670 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 45/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.6550 - out_a_loss: 0.2010 - out_b_loss: 1.4541 - out_a_acc: 0.9230 - out_b_categorical_crossentropy: 1.4541 - val_loss: 1.5478 - val_out_a_loss: 0.1141 - val_out_b_loss: 1.4337 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4337\n",
      "Epoch 46/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.5417 - out_a_loss: 0.1070 - out_b_loss: 1.4347 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.4347 - val_loss: 1.4457 - val_out_a_loss: 0.0362 - val_out_b_loss: 1.4095 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4095\n",
      "Epoch 47/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4477 - out_a_loss: 0.0407 - out_b_loss: 1.4071 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4071 - val_loss: 1.4233 - val_out_a_loss: 0.0305 - val_out_b_loss: 1.3928 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3928\n",
      "Epoch 48/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4547 - out_a_loss: 0.0567 - out_b_loss: 1.3980 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.3980 - val_loss: 1.4574 - val_out_a_loss: 0.0611 - val_out_b_loss: 1.3962 - val_out_a_acc: 0.9510 - val_out_b_categorical_crossentropy: 1.3962\n",
      "Epoch 49/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4453 - out_a_loss: 0.0439 - out_b_loss: 1.4013 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.4013 - val_loss: 1.4051 - val_out_a_loss: 0.0115 - val_out_b_loss: 1.3936 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3936\n",
      "Epoch 50/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4046 - out_a_loss: 0.0135 - out_b_loss: 1.3911 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3911 - val_loss: 1.4101 - val_out_a_loss: 0.0236 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 51/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4273 - out_a_loss: 0.0301 - out_b_loss: 1.3973 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.3973 - val_loss: 1.4452 - val_out_a_loss: 0.0577 - val_out_b_loss: 1.3874 - val_out_a_acc: 0.9730 - val_out_b_categorical_crossentropy: 1.3874\n",
      "Epoch 52/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4110 - out_a_loss: 0.0177 - out_b_loss: 1.3933 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3933 - val_loss: 1.4146 - val_out_a_loss: 0.0279 - val_out_b_loss: 1.3868 - val_out_a_acc: 0.9950 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 53/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4081 - out_a_loss: 0.0172 - out_b_loss: 1.3909 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3909 - val_loss: 1.3970 - val_out_a_loss: 0.0038 - val_out_b_loss: 1.3932 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3932\n",
      "Epoch 54/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4285 - out_a_loss: 0.0301 - out_b_loss: 1.3984 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.3984 - val_loss: 1.4777 - val_out_a_loss: 0.0823 - val_out_b_loss: 1.3953 - val_out_a_acc: 0.9490 - val_out_b_categorical_crossentropy: 1.3953\n",
      "Epoch 55/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.6716 - out_a_loss: 0.2211 - out_b_loss: 1.4504 - out_a_acc: 0.9450 - out_b_categorical_crossentropy: 1.4504 - val_loss: 1.6340 - val_out_a_loss: 0.1595 - val_out_b_loss: 1.4744 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4744\n",
      "Epoch 56/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.5677 - out_a_loss: 0.1305 - out_b_loss: 1.4372 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4372 - val_loss: 1.4852 - val_out_a_loss: 0.0673 - val_out_b_loss: 1.4179 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4179\n",
      "Epoch 57/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4656 - out_a_loss: 0.0498 - out_b_loss: 1.4158 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4158 - val_loss: 1.4428 - val_out_a_loss: 0.0192 - val_out_b_loss: 1.4237 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4237\n",
      "Epoch 58/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4775 - out_a_loss: 0.0386 - out_b_loss: 1.4389 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.4389 - val_loss: 1.5201 - val_out_a_loss: 0.0802 - val_out_b_loss: 1.4400 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4400\n",
      "Epoch 59/300\n",
      "1000/1000 [==============================] - 0s 112us/step - loss: 1.4751 - out_a_loss: 0.0548 - out_b_loss: 1.4204 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.4204 - val_loss: 1.4681 - val_out_a_loss: 0.0625 - val_out_b_loss: 1.4056 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4056\n",
      "Epoch 60/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4512 - out_a_loss: 0.0444 - out_b_loss: 1.4068 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4068 - val_loss: 1.4694 - val_out_a_loss: 0.0279 - val_out_b_loss: 1.4415 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.4415\n",
      "Epoch 61/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4431 - out_a_loss: 0.0406 - out_b_loss: 1.4025 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.4025 - val_loss: 1.4300 - val_out_a_loss: 0.0112 - val_out_b_loss: 1.4189 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4189\n",
      "Epoch 62/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.4092 - out_a_loss: 0.0130 - out_b_loss: 1.3962 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3962 - val_loss: 1.4623 - val_out_a_loss: 0.0086 - val_out_b_loss: 1.4537 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4537\n",
      "Epoch 63/300\n",
      "1000/1000 [==============================] - 0s 147us/step - loss: 1.4299 - out_a_loss: 0.0316 - out_b_loss: 1.3983 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3983 - val_loss: 1.5909 - val_out_a_loss: 0.0610 - val_out_b_loss: 1.5299 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.5299\n",
      "Epoch 64/300\n",
      "1000/1000 [==============================] - 0s 118us/step - loss: 1.4657 - out_a_loss: 0.0568 - out_b_loss: 1.4089 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4089 - val_loss: 1.5055 - val_out_a_loss: 0.0274 - val_out_b_loss: 1.4780 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4780\n",
      "Epoch 65/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4806 - out_a_loss: 0.0696 - out_b_loss: 1.4110 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.4110 - val_loss: 1.4249 - val_out_a_loss: 0.0223 - val_out_b_loss: 1.4025 - val_out_a_acc: 0.9920 - val_out_b_categorical_crossentropy: 1.4025\n",
      "Epoch 66/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4487 - out_a_loss: 0.0441 - out_b_loss: 1.4046 - out_a_acc: 0.9800 - out_b_categorical_crossentropy: 1.4046 - val_loss: 1.4189 - val_out_a_loss: 0.0177 - val_out_b_loss: 1.4013 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4013\n",
      "Epoch 67/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4106 - out_a_loss: 0.0175 - out_b_loss: 1.3930 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3930 - val_loss: 1.4058 - val_out_a_loss: 0.0110 - val_out_b_loss: 1.3948 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3948\n",
      "Epoch 68/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3946 - out_a_loss: 0.0059 - out_b_loss: 1.3887 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3887 - val_loss: 1.3946 - val_out_a_loss: 0.0081 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 69/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3973 - out_a_loss: 0.0085 - out_b_loss: 1.3888 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3888 - val_loss: 1.4146 - val_out_a_loss: 0.0082 - val_out_b_loss: 1.4064 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4064\n",
      "Epoch 70/300\n",
      "1000/1000 [==============================] - 0s 134us/step - loss: 1.4106 - out_a_loss: 0.0186 - out_b_loss: 1.3921 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3921 - val_loss: 1.4161 - val_out_a_loss: 0.0258 - val_out_b_loss: 1.3903 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3903\n",
      "Epoch 71/300\n",
      "1000/1000 [==============================] - 0s 136us/step - loss: 1.4446 - out_a_loss: 0.0509 - out_b_loss: 1.3937 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.3937 - val_loss: 1.4785 - val_out_a_loss: 0.0444 - val_out_b_loss: 1.4341 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4341\n",
      "Epoch 72/300\n",
      "1000/1000 [==============================] - 0s 122us/step - loss: 1.5239 - out_a_loss: 0.1077 - out_b_loss: 1.4162 - out_a_acc: 0.9570 - out_b_categorical_crossentropy: 1.4162 - val_loss: 1.4282 - val_out_a_loss: 0.0200 - val_out_b_loss: 1.4082 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4082\n",
      "Epoch 73/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4348 - out_a_loss: 0.0342 - out_b_loss: 1.4006 - out_a_acc: 0.9900 - out_b_categorical_crossentropy: 1.4006 - val_loss: 1.4521 - val_out_a_loss: 0.0537 - val_out_b_loss: 1.3984 - val_out_a_acc: 0.9850 - val_out_b_categorical_crossentropy: 1.3984\n",
      "Epoch 74/300\n",
      "1000/1000 [==============================] - 0s 117us/step - loss: 1.4906 - out_a_loss: 0.0876 - out_b_loss: 1.4030 - out_a_acc: 0.9910 - out_b_categorical_crossentropy: 1.4030 - val_loss: 1.4722 - val_out_a_loss: 0.0567 - val_out_b_loss: 1.4155 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4155\n",
      "Epoch 75/300\n",
      "1000/1000 [==============================] - 0s 140us/step - loss: 1.4531 - out_a_loss: 0.0498 - out_b_loss: 1.4033 - out_a_acc: 0.9860 - out_b_categorical_crossentropy: 1.4033 - val_loss: 1.4859 - val_out_a_loss: 0.0173 - val_out_b_loss: 1.4685 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4685\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 76/300\n",
      "1000/1000 [==============================] - 0s 119us/step - loss: 1.4281 - out_a_loss: 0.0279 - out_b_loss: 1.4001 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.4001 - val_loss: 1.4301 - val_out_a_loss: 0.0435 - val_out_b_loss: 1.3866 - val_out_a_acc: 0.9900 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 77/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4375 - out_a_loss: 0.0415 - out_b_loss: 1.3959 - out_a_acc: 0.9860 - out_b_categorical_crossentropy: 1.3959 - val_loss: 1.4450 - val_out_a_loss: 0.0583 - val_out_b_loss: 1.3867 - val_out_a_acc: 0.9910 - val_out_b_categorical_crossentropy: 1.3867\n",
      "Epoch 78/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.4480 - out_a_loss: 0.0491 - out_b_loss: 1.3989 - out_a_acc: 0.9780 - out_b_categorical_crossentropy: 1.3989 - val_loss: 1.4246 - val_out_a_loss: 0.0310 - val_out_b_loss: 1.3937 - val_out_a_acc: 0.9840 - val_out_b_categorical_crossentropy: 1.3937\n",
      "Epoch 79/300\n",
      "1000/1000 [==============================] - 0s 130us/step - loss: 1.4258 - out_a_loss: 0.0311 - out_b_loss: 1.3947 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3947 - val_loss: 1.4466 - val_out_a_loss: 0.0602 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9850 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 80/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.4373 - out_a_loss: 0.0447 - out_b_loss: 1.3925 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3925 - val_loss: 1.4064 - val_out_a_loss: 0.0163 - val_out_b_loss: 1.3900 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3900\n",
      "Epoch 81/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4285 - out_a_loss: 0.0349 - out_b_loss: 1.3936 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3936 - val_loss: 1.4266 - val_out_a_loss: 0.0389 - val_out_b_loss: 1.3877 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3877\n",
      "Epoch 82/300\n",
      "1000/1000 [==============================] - 0s 118us/step - loss: 1.4222 - out_a_loss: 0.0266 - out_b_loss: 1.3956 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3956 - val_loss: 1.3924 - val_out_a_loss: 0.0052 - val_out_b_loss: 1.3872 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3872\n",
      "Epoch 83/300\n",
      "1000/1000 [==============================] - 0s 128us/step - loss: 1.3917 - out_a_loss: 0.0042 - out_b_loss: 1.3875 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3875 - val_loss: 1.3889 - val_out_a_loss: 0.0020 - val_out_b_loss: 1.3869 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3869\n",
      "Epoch 84/300\n",
      "1000/1000 [==============================] - 0s 133us/step - loss: 1.3900 - out_a_loss: 0.0028 - out_b_loss: 1.3873 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3873 - val_loss: 1.3973 - val_out_a_loss: 0.0108 - val_out_b_loss: 1.3865 - val_out_a_acc: 0.9960 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 85/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.3942 - out_a_loss: 0.0055 - out_b_loss: 1.3887 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3887 - val_loss: 1.3933 - val_out_a_loss: 0.0070 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 86/300\n",
      "1000/1000 [==============================] - 0s 143us/step - loss: 1.3978 - out_a_loss: 0.0083 - out_b_loss: 1.3895 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3895 - val_loss: 1.3940 - val_out_a_loss: 0.0075 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 87/300\n",
      "1000/1000 [==============================] - 0s 129us/step - loss: 1.4455 - out_a_loss: 0.0529 - out_b_loss: 1.3926 - out_a_acc: 0.9890 - out_b_categorical_crossentropy: 1.3926 - val_loss: 1.4125 - val_out_a_loss: 0.0153 - val_out_b_loss: 1.3972 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3972\n",
      "Epoch 88/300\n",
      "1000/1000 [==============================] - 0s 140us/step - loss: 1.4329 - out_a_loss: 0.0314 - out_b_loss: 1.4015 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.4015 - val_loss: 1.4615 - val_out_a_loss: 0.0745 - val_out_b_loss: 1.3871 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3871\n",
      "Epoch 89/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4373 - out_a_loss: 0.0362 - out_b_loss: 1.4011 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.4011 - val_loss: 1.4290 - val_out_a_loss: 0.0421 - val_out_b_loss: 1.3869 - val_out_a_acc: 0.9960 - val_out_b_categorical_crossentropy: 1.3869\n",
      "Epoch 90/300\n",
      "1000/1000 [==============================] - 0s 127us/step - loss: 1.4349 - out_a_loss: 0.0386 - out_b_loss: 1.3963 - out_a_acc: 0.9890 - out_b_categorical_crossentropy: 1.3963 - val_loss: 1.4102 - val_out_a_loss: 0.0165 - val_out_b_loss: 1.3937 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3937\n",
      "Epoch 91/300\n",
      "1000/1000 [==============================] - 0s 133us/step - loss: 1.4377 - out_a_loss: 0.0440 - out_b_loss: 1.3937 - out_a_acc: 0.9740 - out_b_categorical_crossentropy: 1.3937 - val_loss: 1.4301 - val_out_a_loss: 0.0398 - val_out_b_loss: 1.3903 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3903\n",
      "Epoch 92/300\n",
      "1000/1000 [==============================] - 0s 122us/step - loss: 1.4439 - out_a_loss: 0.0464 - out_b_loss: 1.3975 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.3975 - val_loss: 1.4095 - val_out_a_loss: 0.0143 - val_out_b_loss: 1.3952 - val_out_a_acc: 0.9930 - val_out_b_categorical_crossentropy: 1.3952\n",
      "Epoch 93/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4157 - out_a_loss: 0.0195 - out_b_loss: 1.3962 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3962 - val_loss: 1.4393 - val_out_a_loss: 7.7199e-04 - val_out_b_loss: 1.4385 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4385\n",
      "Epoch 94/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4709 - out_a_loss: 0.0689 - out_b_loss: 1.4020 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.4020 - val_loss: 1.4433 - val_out_a_loss: 0.0466 - val_out_b_loss: 1.3967 - val_out_a_acc: 0.9860 - val_out_b_categorical_crossentropy: 1.3967\n",
      "Epoch 95/300\n",
      "1000/1000 [==============================] - 0s 128us/step - loss: 1.4309 - out_a_loss: 0.0319 - out_b_loss: 1.3991 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3991 - val_loss: 1.4178 - val_out_a_loss: 0.0313 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 96/300\n",
      "1000/1000 [==============================] - 0s 149us/step - loss: 1.4082 - out_a_loss: 0.0187 - out_b_loss: 1.3895 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3895 - val_loss: 1.3971 - val_out_a_loss: 0.0056 - val_out_b_loss: 1.3915 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3915\n",
      "Epoch 97/300\n",
      "1000/1000 [==============================] - 0s 127us/step - loss: 1.4003 - out_a_loss: 0.0105 - out_b_loss: 1.3898 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3898 - val_loss: 1.3916 - val_out_a_loss: 0.0017 - val_out_b_loss: 1.3900 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3900\n",
      "Epoch 98/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3979 - out_a_loss: 0.0094 - out_b_loss: 1.3886 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3886 - val_loss: 1.4117 - val_out_a_loss: 0.0252 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9820 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 99/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3952 - out_a_loss: 0.0072 - out_b_loss: 1.3880 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3880 - val_loss: 1.3886 - val_out_a_loss: 0.0020 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 100/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3964 - out_a_loss: 0.0087 - out_b_loss: 1.3877 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3877 - val_loss: 1.3897 - val_out_a_loss: 0.0017 - val_out_b_loss: 1.3880 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3880\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 101/300\n",
      "1000/1000 [==============================] - 0s 129us/step - loss: 1.3944 - out_a_loss: 0.0058 - out_b_loss: 1.3886 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3886 - val_loss: 1.3897 - val_out_a_loss: 9.7643e-04 - val_out_b_loss: 1.3888 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3888\n",
      "Epoch 102/300\n",
      "1000/1000 [==============================] - 0s 138us/step - loss: 1.4014 - out_a_loss: 0.0135 - out_b_loss: 1.3879 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3879 - val_loss: 1.4286 - val_out_a_loss: 0.0418 - val_out_b_loss: 1.3868 - val_out_a_acc: 0.9790 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 103/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4205 - out_a_loss: 0.0232 - out_b_loss: 1.3974 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.3974 - val_loss: 1.4947 - val_out_a_loss: 0.0771 - val_out_b_loss: 1.4176 - val_out_a_acc: 0.9830 - val_out_b_categorical_crossentropy: 1.4176\n",
      "Epoch 104/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.5073 - out_a_loss: 0.1028 - out_b_loss: 1.4045 - out_a_acc: 0.9510 - out_b_categorical_crossentropy: 1.4045 - val_loss: 1.4655 - val_out_a_loss: 0.0502 - val_out_b_loss: 1.4153 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4153\n",
      "Epoch 105/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4259 - out_a_loss: 0.0294 - out_b_loss: 1.3965 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3965 - val_loss: 1.4082 - val_out_a_loss: 0.0207 - val_out_b_loss: 1.3876 - val_out_a_acc: 0.9930 - val_out_b_categorical_crossentropy: 1.3876\n",
      "Epoch 106/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4410 - out_a_loss: 0.0420 - out_b_loss: 1.3990 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3990 - val_loss: 1.4660 - val_out_a_loss: 0.0765 - val_out_b_loss: 1.3894 - val_out_a_acc: 0.9890 - val_out_b_categorical_crossentropy: 1.3894\n",
      "Epoch 107/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4819 - out_a_loss: 0.0734 - out_b_loss: 1.4085 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.4085 - val_loss: 1.4885 - val_out_a_loss: 0.0810 - val_out_b_loss: 1.4075 - val_out_a_acc: 0.9580 - val_out_b_categorical_crossentropy: 1.4075\n",
      "Epoch 108/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4479 - out_a_loss: 0.0491 - out_b_loss: 1.3988 - out_a_acc: 0.9820 - out_b_categorical_crossentropy: 1.3988 - val_loss: 1.4042 - val_out_a_loss: 0.0116 - val_out_b_loss: 1.3926 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3926\n",
      "Epoch 109/300\n",
      "1000/1000 [==============================] - 0s 116us/step - loss: 1.4233 - out_a_loss: 0.0257 - out_b_loss: 1.3976 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3976 - val_loss: 1.4183 - val_out_a_loss: 0.0318 - val_out_b_loss: 1.3865 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 110/300\n",
      "1000/1000 [==============================] - 0s 139us/step - loss: 1.4191 - out_a_loss: 0.0286 - out_b_loss: 1.3905 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3905 - val_loss: 1.4408 - val_out_a_loss: 0.0542 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 111/300\n",
      "1000/1000 [==============================] - 0s 140us/step - loss: 1.4085 - out_a_loss: 0.0178 - out_b_loss: 1.3907 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3907 - val_loss: 1.4174 - val_out_a_loss: 0.0016 - val_out_b_loss: 1.4158 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4158\n",
      "Epoch 112/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3939 - out_a_loss: 0.0060 - out_b_loss: 1.3880 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3880 - val_loss: 1.3907 - val_out_a_loss: 0.0043 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 113/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3970 - out_a_loss: 0.0070 - out_b_loss: 1.3900 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3900 - val_loss: 1.4021 - val_out_a_loss: 0.0157 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 114/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4491 - out_a_loss: 0.0580 - out_b_loss: 1.3910 - out_a_acc: 0.9910 - out_b_categorical_crossentropy: 1.3910 - val_loss: 1.4550 - val_out_a_loss: 0.0548 - val_out_b_loss: 1.4001 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4001\n",
      "Epoch 115/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4095 - out_a_loss: 0.0148 - out_b_loss: 1.3947 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3947 - val_loss: 1.3911 - val_out_a_loss: 0.0012 - val_out_b_loss: 1.3898 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3898\n",
      "Epoch 116/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3959 - out_a_loss: 0.0060 - out_b_loss: 1.3900 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3900 - val_loss: 1.3879 - val_out_a_loss: 0.0014 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 117/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4030 - out_a_loss: 0.0112 - out_b_loss: 1.3918 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3918 - val_loss: 1.4003 - val_out_a_loss: 0.0083 - val_out_b_loss: 1.3920 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3920\n",
      "Epoch 118/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4046 - out_a_loss: 0.0145 - out_b_loss: 1.3901 - out_a_acc: 0.9910 - out_b_categorical_crossentropy: 1.3901 - val_loss: 1.4185 - val_out_a_loss: 0.0319 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 119/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4024 - out_a_loss: 0.0117 - out_b_loss: 1.3908 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3908 - val_loss: 1.4191 - val_out_a_loss: 0.0328 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9590 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 120/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4042 - out_a_loss: 0.0159 - out_b_loss: 1.3884 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3884 - val_loss: 1.3881 - val_out_a_loss: 0.0011 - val_out_b_loss: 1.3870 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 121/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4007 - out_a_loss: 0.0092 - out_b_loss: 1.3914 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3914 - val_loss: 1.4282 - val_out_a_loss: 0.0419 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 122/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.4158 - out_a_loss: 0.0284 - out_b_loss: 1.3874 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3874 - val_loss: 1.3871 - val_out_a_loss: 6.6558e-04 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 123/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3876 - out_a_loss: 7.4831e-04 - out_b_loss: 1.3868 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3868 - val_loss: 1.3887 - val_out_a_loss: 0.0022 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 124/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4513 - out_a_loss: 0.0560 - out_b_loss: 1.3953 - out_a_acc: 0.9900 - out_b_categorical_crossentropy: 1.3953 - val_loss: 1.4049 - val_out_a_loss: 0.0180 - val_out_b_loss: 1.3869 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3869\n",
      "Epoch 125/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4213 - out_a_loss: 0.0255 - out_b_loss: 1.3958 - out_a_acc: 0.9850 - out_b_categorical_crossentropy: 1.3958 - val_loss: 1.4071 - val_out_a_loss: 0.0208 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9930 - val_out_b_categorical_crossentropy: 1.3864\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 126/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4124 - out_a_loss: 0.0203 - out_b_loss: 1.3921 - out_a_acc: 0.9900 - out_b_categorical_crossentropy: 1.3921 - val_loss: 1.5176 - val_out_a_loss: 0.0873 - val_out_b_loss: 1.4303 - val_out_a_acc: 0.8840 - val_out_b_categorical_crossentropy: 1.4303\n",
      "Epoch 127/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4612 - out_a_loss: 0.0596 - out_b_loss: 1.4017 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.4017 - val_loss: 1.5248 - val_out_a_loss: 0.1282 - val_out_b_loss: 1.3966 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3966\n",
      "Epoch 128/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4419 - out_a_loss: 0.0458 - out_b_loss: 1.3962 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3962 - val_loss: 1.4374 - val_out_a_loss: 0.0011 - val_out_b_loss: 1.4363 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4363\n",
      "Epoch 129/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4502 - out_a_loss: 0.0582 - out_b_loss: 1.3920 - out_a_acc: 0.9780 - out_b_categorical_crossentropy: 1.3920 - val_loss: 1.5701 - val_out_a_loss: 0.1754 - val_out_b_loss: 1.3947 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3947\n",
      "Epoch 130/300\n",
      "1000/1000 [==============================] - 0s 102us/step - loss: 1.5137 - out_a_loss: 0.1087 - out_b_loss: 1.4050 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4050 - val_loss: 1.4250 - val_out_a_loss: 0.0342 - val_out_b_loss: 1.3908 - val_out_a_acc: 0.9920 - val_out_b_categorical_crossentropy: 1.3908\n",
      "Epoch 131/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4402 - out_a_loss: 0.0355 - out_b_loss: 1.4047 - out_a_acc: 0.9800 - out_b_categorical_crossentropy: 1.4047 - val_loss: 1.4370 - val_out_a_loss: 0.0502 - val_out_b_loss: 1.3868 - val_out_a_acc: 0.9590 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 132/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.5023 - out_a_loss: 0.1057 - out_b_loss: 1.3965 - out_a_acc: 0.9160 - out_b_categorical_crossentropy: 1.3965 - val_loss: 1.4921 - val_out_a_loss: 0.0289 - val_out_b_loss: 1.4632 - val_out_a_acc: 0.9680 - val_out_b_categorical_crossentropy: 1.4632\n",
      "Epoch 133/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4208 - out_a_loss: 0.0270 - out_b_loss: 1.3938 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3938 - val_loss: 1.4803 - val_out_a_loss: 0.0219 - val_out_b_loss: 1.4584 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4584\n",
      "Epoch 134/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4238 - out_a_loss: 0.0291 - out_b_loss: 1.3947 - out_a_acc: 0.9850 - out_b_categorical_crossentropy: 1.3947 - val_loss: 1.4303 - val_out_a_loss: 0.0423 - val_out_b_loss: 1.3880 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3880\n",
      "Epoch 135/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4323 - out_a_loss: 0.0355 - out_b_loss: 1.3968 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.3968 - val_loss: 1.4181 - val_out_a_loss: 0.0137 - val_out_b_loss: 1.4044 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4044\n",
      "Epoch 136/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4519 - out_a_loss: 0.0504 - out_b_loss: 1.4014 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4014 - val_loss: 1.4342 - val_out_a_loss: 0.0414 - val_out_b_loss: 1.3928 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3928\n",
      "Epoch 137/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4172 - out_a_loss: 0.0235 - out_b_loss: 1.3937 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3937 - val_loss: 1.4036 - val_out_a_loss: 0.0108 - val_out_b_loss: 1.3928 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3928\n",
      "Epoch 138/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4256 - out_a_loss: 0.0299 - out_b_loss: 1.3957 - out_a_acc: 0.9860 - out_b_categorical_crossentropy: 1.3957 - val_loss: 1.4148 - val_out_a_loss: 0.0278 - val_out_b_loss: 1.3870 - val_out_a_acc: 0.9830 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 139/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4034 - out_a_loss: 0.0132 - out_b_loss: 1.3902 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3902 - val_loss: 1.3951 - val_out_a_loss: 0.0067 - val_out_b_loss: 1.3884 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3884\n",
      "Epoch 140/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4184 - out_a_loss: 0.0277 - out_b_loss: 1.3907 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3907 - val_loss: 1.4560 - val_out_a_loss: 0.0614 - val_out_b_loss: 1.3946 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3946\n",
      "Epoch 141/300\n",
      "1000/1000 [==============================] - 0s 133us/step - loss: 1.4169 - out_a_loss: 0.0248 - out_b_loss: 1.3921 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3921 - val_loss: 1.4029 - val_out_a_loss: 0.0056 - val_out_b_loss: 1.3973 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3973\n",
      "Epoch 142/300\n",
      "1000/1000 [==============================] - 0s 136us/step - loss: 1.4380 - out_a_loss: 0.0342 - out_b_loss: 1.4038 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.4038 - val_loss: 1.5323 - val_out_a_loss: 0.1460 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.8830 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 143/300\n",
      "1000/1000 [==============================] - 0s 114us/step - loss: 1.4807 - out_a_loss: 0.0873 - out_b_loss: 1.3934 - out_a_acc: 0.9590 - out_b_categorical_crossentropy: 1.3934 - val_loss: 1.4958 - val_out_a_loss: 0.0122 - val_out_b_loss: 1.4836 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4836\n",
      "Epoch 144/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4146 - out_a_loss: 0.0208 - out_b_loss: 1.3937 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3937 - val_loss: 1.4176 - val_out_a_loss: 0.0310 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 145/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3994 - out_a_loss: 0.0097 - out_b_loss: 1.3897 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3897 - val_loss: 1.4028 - val_out_a_loss: 0.0164 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9870 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 146/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3890 - out_a_loss: 0.0017 - out_b_loss: 1.3873 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3873 - val_loss: 1.3874 - val_out_a_loss: 0.0011 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 147/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3875 - out_a_loss: 0.0011 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3890 - val_out_a_loss: 3.6058e-04 - val_out_b_loss: 1.3886 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3886\n",
      "Epoch 148/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3874 - out_a_loss: 9.3515e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3877 - val_out_a_loss: 9.4332e-04 - val_out_b_loss: 1.3868 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 149/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.3871 - out_a_loss: 6.1555e-04 - out_b_loss: 1.3865 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3865 - val_loss: 1.3870 - val_out_a_loss: 5.0916e-04 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 150/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3870 - out_a_loss: 5.9746e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3872 - val_out_a_loss: 8.9961e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 151/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.3867 - out_a_loss: 3.7930e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3879 - val_out_a_loss: 2.4411e-04 - val_out_b_loss: 1.3877 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3877\n",
      "Epoch 152/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3867 - out_a_loss: 3.6541e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3873 - val_out_a_loss: 5.2327e-04 - val_out_b_loss: 1.3868 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 153/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3865 - out_a_loss: 1.8956e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3871 - val_out_a_loss: 3.6783e-04 - val_out_b_loss: 1.3868 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 154/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3887 - out_a_loss: 0.0019 - out_b_loss: 1.3868 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3868 - val_loss: 1.4191 - val_out_a_loss: 0.0325 - val_out_b_loss: 1.3866 - val_out_a_acc: 0.9600 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 155/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4153 - out_a_loss: 0.0194 - out_b_loss: 1.3959 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3959 - val_loss: 1.4106 - val_out_a_loss: 0.0218 - val_out_b_loss: 1.3888 - val_out_a_acc: 0.9820 - val_out_b_categorical_crossentropy: 1.3888\n",
      "Epoch 156/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.4137 - out_a_loss: 0.0158 - out_b_loss: 1.3979 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3979 - val_loss: 1.4644 - val_out_a_loss: 0.0678 - val_out_b_loss: 1.3966 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3966\n",
      "Epoch 157/300\n",
      "1000/1000 [==============================] - 0s 102us/step - loss: 1.4340 - out_a_loss: 0.0436 - out_b_loss: 1.3904 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3904 - val_loss: 1.3969 - val_out_a_loss: 0.0068 - val_out_b_loss: 1.3901 - val_out_a_acc: 0.9940 - val_out_b_categorical_crossentropy: 1.3901\n",
      "Epoch 158/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4597 - out_a_loss: 0.0677 - out_b_loss: 1.3920 - out_a_acc: 0.9750 - out_b_categorical_crossentropy: 1.3920 - val_loss: 1.5662 - val_out_a_loss: 0.1141 - val_out_b_loss: 1.4521 - val_out_a_acc: 0.9810 - val_out_b_categorical_crossentropy: 1.4521\n",
      "Epoch 159/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.4323 - out_a_loss: 0.0367 - out_b_loss: 1.3956 - out_a_acc: 0.9880 - out_b_categorical_crossentropy: 1.3956 - val_loss: 1.4014 - val_out_a_loss: 0.0150 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9890 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 160/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4153 - out_a_loss: 0.0227 - out_b_loss: 1.3926 - out_a_acc: 0.9890 - out_b_categorical_crossentropy: 1.3926 - val_loss: 1.4126 - val_out_a_loss: 0.0235 - val_out_b_loss: 1.3890 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3890\n",
      "Epoch 161/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4085 - out_a_loss: 0.0197 - out_b_loss: 1.3888 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3888 - val_loss: 1.4469 - val_out_a_loss: 0.0188 - val_out_b_loss: 1.4280 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4280\n",
      "Epoch 162/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4378 - out_a_loss: 0.0451 - out_b_loss: 1.3927 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.3927 - val_loss: 1.3974 - val_out_a_loss: 0.0108 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 163/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.4155 - out_a_loss: 0.0231 - out_b_loss: 1.3924 - out_a_acc: 0.9850 - out_b_categorical_crossentropy: 1.3924 - val_loss: 1.4501 - val_out_a_loss: 0.0632 - val_out_b_loss: 1.3869 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3869\n",
      "Epoch 164/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4309 - out_a_loss: 0.0393 - out_b_loss: 1.3915 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3915 - val_loss: 1.3983 - val_out_a_loss: 0.0120 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9990 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 165/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3959 - out_a_loss: 0.0074 - out_b_loss: 1.3885 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3885 - val_loss: 1.3926 - val_out_a_loss: 0.0049 - val_out_b_loss: 1.3876 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3876\n",
      "Epoch 166/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3956 - out_a_loss: 0.0078 - out_b_loss: 1.3877 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3877 - val_loss: 1.3938 - val_out_a_loss: 0.0060 - val_out_b_loss: 1.3878 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3878\n",
      "Epoch 167/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4021 - out_a_loss: 0.0108 - out_b_loss: 1.3913 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3913 - val_loss: 1.3907 - val_out_a_loss: 7.6368e-04 - val_out_b_loss: 1.3900 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3900\n",
      "Epoch 168/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4042 - out_a_loss: 0.0126 - out_b_loss: 1.3916 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.3916 - val_loss: 1.4200 - val_out_a_loss: 0.0337 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9610 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 169/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4135 - out_a_loss: 0.0222 - out_b_loss: 1.3913 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3913 - val_loss: 1.4276 - val_out_a_loss: 0.0128 - val_out_b_loss: 1.4148 - val_out_a_acc: 0.9910 - val_out_b_categorical_crossentropy: 1.4148\n",
      "Epoch 170/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4539 - out_a_loss: 0.0616 - out_b_loss: 1.3922 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3922 - val_loss: 1.4098 - val_out_a_loss: 0.0218 - val_out_b_loss: 1.3880 - val_out_a_acc: 0.9960 - val_out_b_categorical_crossentropy: 1.3880\n",
      "Epoch 171/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.5494 - out_a_loss: 0.1250 - out_b_loss: 1.4244 - out_a_acc: 0.9650 - out_b_categorical_crossentropy: 1.4244 - val_loss: 1.5401 - val_out_a_loss: 0.1229 - val_out_b_loss: 1.4172 - val_out_a_acc: 0.9900 - val_out_b_categorical_crossentropy: 1.4172\n",
      "Epoch 172/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4677 - out_a_loss: 0.0621 - out_b_loss: 1.4056 - out_a_acc: 0.9900 - out_b_categorical_crossentropy: 1.4056 - val_loss: 1.4110 - val_out_a_loss: 0.0240 - val_out_b_loss: 1.3870 - val_out_a_acc: 0.9790 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 173/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3925 - out_a_loss: 0.0042 - out_b_loss: 1.3882 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3882 - val_loss: 1.3935 - val_out_a_loss: 0.0070 - val_out_b_loss: 1.3865 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 174/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3903 - out_a_loss: 0.0035 - out_b_loss: 1.3868 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3868 - val_loss: 1.3874 - val_out_a_loss: 0.0011 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 175/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.4321 - out_a_loss: 0.0413 - out_b_loss: 1.3908 - out_a_acc: 0.9880 - out_b_categorical_crossentropy: 1.3908 - val_loss: 1.4069 - val_out_a_loss: 0.0113 - val_out_b_loss: 1.3957 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3957\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 176/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.5029 - out_a_loss: 0.0973 - out_b_loss: 1.4056 - out_a_acc: 0.9680 - out_b_categorical_crossentropy: 1.4056 - val_loss: 1.4528 - val_out_a_loss: 0.0500 - val_out_b_loss: 1.4029 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4029\n",
      "Epoch 177/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4312 - out_a_loss: 0.0272 - out_b_loss: 1.4040 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.4040 - val_loss: 1.3948 - val_out_a_loss: 0.0084 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 178/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4433 - out_a_loss: 0.0507 - out_b_loss: 1.3926 - out_a_acc: 0.9790 - out_b_categorical_crossentropy: 1.3926 - val_loss: 1.4264 - val_out_a_loss: 0.0250 - val_out_b_loss: 1.4014 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.4014\n",
      "Epoch 179/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3966 - out_a_loss: 0.0069 - out_b_loss: 1.3897 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3897 - val_loss: 1.3888 - val_out_a_loss: 0.0018 - val_out_b_loss: 1.3870 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 180/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3922 - out_a_loss: 0.0035 - out_b_loss: 1.3887 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3887 - val_loss: 1.4055 - val_out_a_loss: 0.0184 - val_out_b_loss: 1.3870 - val_out_a_acc: 0.9810 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 181/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4073 - out_a_loss: 0.0181 - out_b_loss: 1.3892 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3892 - val_loss: 1.4048 - val_out_a_loss: 0.0184 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 182/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4049 - out_a_loss: 0.0181 - out_b_loss: 1.3868 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3868 - val_loss: 1.3963 - val_out_a_loss: 0.0098 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 183/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4274 - out_a_loss: 0.0361 - out_b_loss: 1.3913 - out_a_acc: 0.9890 - out_b_categorical_crossentropy: 1.3913 - val_loss: 1.4374 - val_out_a_loss: 0.0463 - val_out_b_loss: 1.3911 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3911\n",
      "Epoch 184/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4079 - out_a_loss: 0.0174 - out_b_loss: 1.3905 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3905 - val_loss: 1.4203 - val_out_a_loss: 0.0210 - val_out_b_loss: 1.3993 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3993\n",
      "Epoch 185/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4145 - out_a_loss: 0.0189 - out_b_loss: 1.3956 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3956 - val_loss: 1.4077 - val_out_a_loss: 0.0192 - val_out_b_loss: 1.3885 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3885\n",
      "Epoch 186/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4068 - out_a_loss: 0.0153 - out_b_loss: 1.3914 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.3914 - val_loss: 1.4351 - val_out_a_loss: 0.0487 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 187/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.4268 - out_a_loss: 0.0349 - out_b_loss: 1.3919 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3919 - val_loss: 1.4362 - val_out_a_loss: 0.0414 - val_out_b_loss: 1.3948 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3948\n",
      "Epoch 188/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.4045 - out_a_loss: 0.0152 - out_b_loss: 1.3893 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3893 - val_loss: 1.3937 - val_out_a_loss: 0.0065 - val_out_b_loss: 1.3872 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3872\n",
      "Epoch 189/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3914 - out_a_loss: 0.0035 - out_b_loss: 1.3880 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3880 - val_loss: 1.3985 - val_out_a_loss: 2.5899e-04 - val_out_b_loss: 1.3983 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3983\n",
      "Epoch 190/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3959 - out_a_loss: 0.0078 - out_b_loss: 1.3881 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3881 - val_loss: 1.3936 - val_out_a_loss: 0.0073 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 191/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3963 - out_a_loss: 0.0090 - out_b_loss: 1.3873 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3873 - val_loss: 1.3986 - val_out_a_loss: 0.0119 - val_out_b_loss: 1.3866 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 192/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3977 - out_a_loss: 0.0068 - out_b_loss: 1.3909 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3909 - val_loss: 1.3978 - val_out_a_loss: 0.0115 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9910 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 193/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3955 - out_a_loss: 0.0086 - out_b_loss: 1.3869 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3869 - val_loss: 1.4555 - val_out_a_loss: 2.0193e-04 - val_out_b_loss: 1.4553 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4553\n",
      "Epoch 194/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3993 - out_a_loss: 0.0073 - out_b_loss: 1.3920 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3920 - val_loss: 1.3876 - val_out_a_loss: 0.0013 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 195/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3871 - out_a_loss: 6.5159e-04 - out_b_loss: 1.3865 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3865 - val_loss: 1.3931 - val_out_a_loss: 2.8365e-04 - val_out_b_loss: 1.3928 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3928\n",
      "Epoch 196/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3887 - out_a_loss: 0.0021 - out_b_loss: 1.3866 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3866 - val_loss: 1.3887 - val_out_a_loss: 1.0393e-04 - val_out_b_loss: 1.3886 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3886\n",
      "Epoch 197/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3932 - out_a_loss: 0.0055 - out_b_loss: 1.3877 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3877 - val_loss: 1.4305 - val_out_a_loss: 0.0019 - val_out_b_loss: 1.4286 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4286\n",
      "Epoch 198/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3958 - out_a_loss: 0.0066 - out_b_loss: 1.3892 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3892 - val_loss: 1.3904 - val_out_a_loss: 0.0041 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 199/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3895 - out_a_loss: 0.0028 - out_b_loss: 1.3868 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3868 - val_loss: 1.3923 - val_out_a_loss: 0.0035 - val_out_b_loss: 1.3887 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3887\n",
      "Epoch 200/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3935 - out_a_loss: 0.0057 - out_b_loss: 1.3878 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.3878 - val_loss: 1.3970 - val_out_a_loss: 0.0107 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 201/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3913 - out_a_loss: 0.0042 - out_b_loss: 1.3871 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3871 - val_loss: 1.3878 - val_out_a_loss: 3.7962e-04 - val_out_b_loss: 1.3875 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3875\n",
      "Epoch 202/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3882 - out_a_loss: 0.0012 - out_b_loss: 1.3871 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3871 - val_loss: 1.3913 - val_out_a_loss: 0.0050 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 203/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4969 - out_a_loss: 0.0880 - out_b_loss: 1.4090 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.4090 - val_loss: 1.4895 - val_out_a_loss: 0.0934 - val_out_b_loss: 1.3961 - val_out_a_acc: 0.9910 - val_out_b_categorical_crossentropy: 1.3961\n",
      "Epoch 204/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4402 - out_a_loss: 0.0467 - out_b_loss: 1.3935 - out_a_acc: 0.9830 - out_b_categorical_crossentropy: 1.3935 - val_loss: 1.4462 - val_out_a_loss: 0.0590 - val_out_b_loss: 1.3872 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3872\n",
      "Epoch 205/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4371 - out_a_loss: 0.0392 - out_b_loss: 1.3980 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.3980 - val_loss: 1.4230 - val_out_a_loss: 0.0336 - val_out_b_loss: 1.3895 - val_out_a_acc: 0.9650 - val_out_b_categorical_crossentropy: 1.3895\n",
      "Epoch 206/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4069 - out_a_loss: 0.0170 - out_b_loss: 1.3898 - out_a_acc: 0.9910 - out_b_categorical_crossentropy: 1.3898 - val_loss: 1.3903 - val_out_a_loss: 0.0017 - val_out_b_loss: 1.3886 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3886\n",
      "Epoch 207/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3880 - out_a_loss: 0.0015 - out_b_loss: 1.3866 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3866 - val_loss: 1.3912 - val_out_a_loss: 2.2132e-04 - val_out_b_loss: 1.3909 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3909\n",
      "Epoch 208/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3960 - out_a_loss: 0.0057 - out_b_loss: 1.3903 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3903 - val_loss: 1.4446 - val_out_a_loss: 0.0578 - val_out_b_loss: 1.3868 - val_out_a_acc: 0.9240 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 209/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4410 - out_a_loss: 0.0519 - out_b_loss: 1.3891 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3891 - val_loss: 1.4226 - val_out_a_loss: 0.0331 - val_out_b_loss: 1.3895 - val_out_a_acc: 0.9770 - val_out_b_categorical_crossentropy: 1.3895\n",
      "Epoch 210/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4189 - out_a_loss: 0.0229 - out_b_loss: 1.3960 - out_a_acc: 0.9840 - out_b_categorical_crossentropy: 1.3960 - val_loss: 1.4074 - val_out_a_loss: 0.0188 - val_out_b_loss: 1.3885 - val_out_a_acc: 0.9840 - val_out_b_categorical_crossentropy: 1.3885\n",
      "Epoch 211/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4193 - out_a_loss: 0.0263 - out_b_loss: 1.3930 - out_a_acc: 0.9890 - out_b_categorical_crossentropy: 1.3930 - val_loss: 1.4193 - val_out_a_loss: 0.0329 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 212/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4273 - out_a_loss: 0.0229 - out_b_loss: 1.4044 - out_a_acc: 0.9970 - out_b_categorical_crossentropy: 1.4044 - val_loss: 1.4238 - val_out_a_loss: 0.0365 - val_out_b_loss: 1.3873 - val_out_a_acc: 0.9870 - val_out_b_categorical_crossentropy: 1.3873\n",
      "Epoch 213/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.4046 - out_a_loss: 0.0159 - out_b_loss: 1.3887 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3887 - val_loss: 1.3940 - val_out_a_loss: 0.0057 - val_out_b_loss: 1.3883 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3883\n",
      "Epoch 214/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3891 - out_a_loss: 0.0020 - out_b_loss: 1.3871 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3871 - val_loss: 1.3892 - val_out_a_loss: 0.0023 - val_out_b_loss: 1.3869 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3869\n",
      "Epoch 215/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3956 - out_a_loss: 0.0071 - out_b_loss: 1.3885 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3885 - val_loss: 1.4150 - val_out_a_loss: 0.0058 - val_out_b_loss: 1.4092 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4092\n",
      "Epoch 216/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4062 - out_a_loss: 0.0151 - out_b_loss: 1.3911 - out_a_acc: 0.9890 - out_b_categorical_crossentropy: 1.3911 - val_loss: 1.4144 - val_out_a_loss: 0.0275 - val_out_b_loss: 1.3870 - val_out_a_acc: 0.9910 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 217/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4035 - out_a_loss: 0.0145 - out_b_loss: 1.3890 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.3890 - val_loss: 1.4245 - val_out_a_loss: 0.0311 - val_out_b_loss: 1.3935 - val_out_a_acc: 0.9680 - val_out_b_categorical_crossentropy: 1.3935\n",
      "Epoch 218/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4059 - out_a_loss: 0.0159 - out_b_loss: 1.3900 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3900 - val_loss: 1.3898 - val_out_a_loss: 0.0035 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 219/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4043 - out_a_loss: 0.0119 - out_b_loss: 1.3925 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3925 - val_loss: 1.3941 - val_out_a_loss: 0.0077 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 220/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4120 - out_a_loss: 0.0183 - out_b_loss: 1.3937 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3937 - val_loss: 1.4086 - val_out_a_loss: 0.0188 - val_out_b_loss: 1.3898 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3898\n",
      "Epoch 221/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.5470 - out_a_loss: 0.1475 - out_b_loss: 1.3995 - out_a_acc: 0.9840 - out_b_categorical_crossentropy: 1.3995 - val_loss: 1.5273 - val_out_a_loss: 0.1169 - val_out_b_loss: 1.4104 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4104\n",
      "Epoch 222/300\n",
      "1000/1000 [==============================] - 0s 114us/step - loss: 1.5375 - out_a_loss: 0.1290 - out_b_loss: 1.4085 - out_a_acc: 0.9800 - out_b_categorical_crossentropy: 1.4085 - val_loss: 1.4923 - val_out_a_loss: 0.0981 - val_out_b_loss: 1.3942 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3942\n",
      "Epoch 223/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4835 - out_a_loss: 0.0781 - out_b_loss: 1.4054 - out_a_acc: 0.9890 - out_b_categorical_crossentropy: 1.4054 - val_loss: 1.4233 - val_out_a_loss: 0.0326 - val_out_b_loss: 1.3907 - val_out_a_acc: 0.9610 - val_out_b_categorical_crossentropy: 1.3907\n",
      "Epoch 224/300\n",
      "1000/1000 [==============================] - 0s 114us/step - loss: 1.4972 - out_a_loss: 0.0945 - out_b_loss: 1.4028 - out_a_acc: 0.9770 - out_b_categorical_crossentropy: 1.4028 - val_loss: 1.4975 - val_out_a_loss: 0.1009 - val_out_b_loss: 1.3966 - val_out_a_acc: 0.9720 - val_out_b_categorical_crossentropy: 1.3966\n",
      "Epoch 225/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4418 - out_a_loss: 0.0473 - out_b_loss: 1.3945 - out_a_acc: 0.9790 - out_b_categorical_crossentropy: 1.3945 - val_loss: 1.3994 - val_out_a_loss: 0.0131 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 226/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3967 - out_a_loss: 0.0091 - out_b_loss: 1.3876 - out_a_acc: 0.9950 - out_b_categorical_crossentropy: 1.3876 - val_loss: 1.3891 - val_out_a_loss: 0.0026 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 227/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3897 - out_a_loss: 0.0030 - out_b_loss: 1.3867 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3867 - val_loss: 1.3880 - val_out_a_loss: 0.0017 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 228/300\n",
      "1000/1000 [==============================] - 0s 113us/step - loss: 1.3902 - out_a_loss: 0.0023 - out_b_loss: 1.3879 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3879 - val_loss: 1.3917 - val_out_a_loss: 0.0054 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 229/300\n",
      "1000/1000 [==============================] - 0s 137us/step - loss: 1.3962 - out_a_loss: 0.0047 - out_b_loss: 1.3915 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3915 - val_loss: 1.3893 - val_out_a_loss: 0.0030 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 230/300\n",
      "1000/1000 [==============================] - 0s 136us/step - loss: 1.4002 - out_a_loss: 0.0114 - out_b_loss: 1.3889 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3889 - val_loss: 1.3986 - val_out_a_loss: 0.0123 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9890 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 231/300\n",
      "1000/1000 [==============================] - 0s 119us/step - loss: 1.3957 - out_a_loss: 0.0084 - out_b_loss: 1.3873 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3873 - val_loss: 1.3988 - val_out_a_loss: 0.0042 - val_out_b_loss: 1.3946 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3946\n",
      "Epoch 232/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3946 - out_a_loss: 0.0060 - out_b_loss: 1.3886 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3886 - val_loss: 1.3885 - val_out_a_loss: 0.0022 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 233/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4058 - out_a_loss: 0.0164 - out_b_loss: 1.3893 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3893 - val_loss: 1.4218 - val_out_a_loss: 0.0327 - val_out_b_loss: 1.3891 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3891\n",
      "Epoch 234/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4112 - out_a_loss: 0.0216 - out_b_loss: 1.3896 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3896 - val_loss: 1.3922 - val_out_a_loss: 0.0048 - val_out_b_loss: 1.3874 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3874\n",
      "Epoch 235/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4323 - out_a_loss: 0.0399 - out_b_loss: 1.3924 - out_a_acc: 0.9910 - out_b_categorical_crossentropy: 1.3924 - val_loss: 1.4759 - val_out_a_loss: 0.0826 - val_out_b_loss: 1.3933 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3933\n",
      "Epoch 236/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4160 - out_a_loss: 0.0241 - out_b_loss: 1.3919 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3919 - val_loss: 1.3903 - val_out_a_loss: 0.0018 - val_out_b_loss: 1.3884 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3884\n",
      "Epoch 237/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3883 - out_a_loss: 0.0016 - out_b_loss: 1.3867 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3867 - val_loss: 1.3870 - val_out_a_loss: 4.5692e-04 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 238/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3871 - out_a_loss: 6.0843e-04 - out_b_loss: 1.3865 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3865 - val_loss: 1.3871 - val_out_a_loss: 8.1196e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 239/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3872 - out_a_loss: 3.3143e-04 - out_b_loss: 1.3869 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3869 - val_loss: 1.3992 - val_out_a_loss: 0.0129 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9910 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 240/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4028 - out_a_loss: 0.0140 - out_b_loss: 1.3889 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3889 - val_loss: 1.3932 - val_out_a_loss: 0.0057 - val_out_b_loss: 1.3874 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3874\n",
      "Epoch 241/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4068 - out_a_loss: 0.0109 - out_b_loss: 1.3959 - out_a_acc: 0.9960 - out_b_categorical_crossentropy: 1.3959 - val_loss: 1.4628 - val_out_a_loss: 0.0762 - val_out_b_loss: 1.3866 - val_out_a_acc: 0.9940 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 242/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.4847 - out_a_loss: 0.0894 - out_b_loss: 1.3953 - out_a_acc: 0.9620 - out_b_categorical_crossentropy: 1.3953 - val_loss: 1.4189 - val_out_a_loss: 0.0219 - val_out_b_loss: 1.3970 - val_out_a_acc: 0.9960 - val_out_b_categorical_crossentropy: 1.3970\n",
      "Epoch 243/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.4481 - out_a_loss: 0.0470 - out_b_loss: 1.4012 - out_a_acc: 0.9670 - out_b_categorical_crossentropy: 1.4012 - val_loss: 1.4697 - val_out_a_loss: 0.0817 - val_out_b_loss: 1.3880 - val_out_a_acc: 0.9220 - val_out_b_categorical_crossentropy: 1.3880\n",
      "Epoch 244/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4472 - out_a_loss: 0.0539 - out_b_loss: 1.3933 - out_a_acc: 0.9640 - out_b_categorical_crossentropy: 1.3933 - val_loss: 1.4154 - val_out_a_loss: 0.0285 - val_out_b_loss: 1.3869 - val_out_a_acc: 0.9880 - val_out_b_categorical_crossentropy: 1.3869\n",
      "Epoch 245/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4053 - out_a_loss: 0.0153 - out_b_loss: 1.3900 - out_a_acc: 0.9930 - out_b_categorical_crossentropy: 1.3900 - val_loss: 1.3971 - val_out_a_loss: 0.0056 - val_out_b_loss: 1.3915 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3915\n",
      "Epoch 246/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4354 - out_a_loss: 0.0408 - out_b_loss: 1.3945 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3945 - val_loss: 1.3878 - val_out_a_loss: 0.0010 - val_out_b_loss: 1.3868 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3868\n",
      "Epoch 247/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.3898 - out_a_loss: 0.0022 - out_b_loss: 1.3876 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3876 - val_loss: 1.3912 - val_out_a_loss: 0.0049 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9990 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 248/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3966 - out_a_loss: 0.0041 - out_b_loss: 1.3925 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.3925 - val_loss: 1.4105 - val_out_a_loss: 0.0242 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 249/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4274 - out_a_loss: 0.0356 - out_b_loss: 1.3918 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3918 - val_loss: 1.4405 - val_out_a_loss: 0.0542 - val_out_b_loss: 1.3863 - val_out_a_acc: 0.9340 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 250/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.4382 - out_a_loss: 0.0365 - out_b_loss: 1.4017 - out_a_acc: 0.9990 - out_b_categorical_crossentropy: 1.4017 - val_loss: 1.3932 - val_out_a_loss: 0.0015 - val_out_b_loss: 1.3917 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3917\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 251/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3891 - out_a_loss: 0.0015 - out_b_loss: 1.3876 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3876 - val_loss: 1.3898 - val_out_a_loss: 0.0033 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 252/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3875 - out_a_loss: 0.0010 - out_b_loss: 1.3865 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3865 - val_loss: 1.3883 - val_out_a_loss: 1.4976e-04 - val_out_b_loss: 1.3881 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3881\n",
      "Epoch 253/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3868 - out_a_loss: 2.3820e-04 - out_b_loss: 1.3865 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3865 - val_loss: 1.3869 - val_out_a_loss: 6.4085e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 254/300\n",
      "1000/1000 [==============================] - 0s 110us/step - loss: 1.3872 - out_a_loss: 7.6599e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3869 - val_out_a_loss: 5.6794e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 255/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3865 - out_a_loss: 1.8244e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 1.5359e-04 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 256/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3867 - out_a_loss: 2.9991e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3870 - val_out_a_loss: 6.6097e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 257/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3865 - out_a_loss: 1.9625e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3867 - val_out_a_loss: 1.1958e-04 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 258/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3871 - out_a_loss: 6.6272e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3889 - val_out_a_loss: 1.3360e-04 - val_out_b_loss: 1.3888 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3888\n",
      "Epoch 259/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.3869 - out_a_loss: 4.7986e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3876 - val_out_a_loss: 2.5045e-04 - val_out_b_loss: 1.3873 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3873\n",
      "Epoch 260/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3871 - out_a_loss: 5.8773e-04 - out_b_loss: 1.3865 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3865 - val_loss: 1.3872 - val_out_a_loss: 8.5817e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 261/300\n",
      "1000/1000 [==============================] - 0s 114us/step - loss: 1.3864 - out_a_loss: 1.1881e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3871 - val_out_a_loss: 6.1672e-05 - val_out_b_loss: 1.3870 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3870\n",
      "Epoch 262/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3869 - out_a_loss: 5.2767e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3865 - val_out_a_loss: 1.9291e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 263/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3864 - out_a_loss: 1.2891e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3866 - val_out_a_loss: 9.3149e-05 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 264/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3864 - out_a_loss: 9.0573e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 1.9447e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 265/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3864 - out_a_loss: 1.2574e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 1.4016e-04 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 266/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3864 - out_a_loss: 9.5653e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3867 - val_out_a_loss: 8.4363e-05 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 267/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3864 - out_a_loss: 7.6134e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3864 - val_out_a_loss: 9.5002e-05 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 268/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3864 - out_a_loss: 9.5662e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 1.8151e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 269/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.3864 - out_a_loss: 1.0861e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3866 - val_out_a_loss: 7.5431e-05 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 270/300\n",
      "1000/1000 [==============================] - 0s 111us/step - loss: 1.3864 - out_a_loss: 6.6976e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3866 - val_out_a_loss: 8.3114e-05 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 271/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3864 - out_a_loss: 7.7528e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3868 - val_out_a_loss: 8.1756e-05 - val_out_b_loss: 1.3867 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3867\n",
      "Epoch 272/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.3864 - out_a_loss: 5.6225e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 8.0770e-05 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 273/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3865 - out_a_loss: 1.7462e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 7.7740e-05 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 274/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3864 - out_a_loss: 1.1932e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3868 - val_out_a_loss: 1.4415e-04 - val_out_b_loss: 1.3866 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3866\n",
      "Epoch 275/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3864 - out_a_loss: 6.1046e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 6.3886e-05 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 276/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3864 - out_a_loss: 7.1589e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3864 - val_out_a_loss: 8.7423e-05 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 277/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3864 - out_a_loss: 7.6747e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3866 - val_out_a_loss: 1.0899e-04 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 278/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3864 - out_a_loss: 7.0260e-05 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3874 - val_out_a_loss: 6.0382e-05 - val_out_b_loss: 1.3873 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3873\n",
      "Epoch 279/300\n",
      "1000/1000 [==============================] - 0s 123us/step - loss: 1.3868 - out_a_loss: 4.3978e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3887 - val_out_a_loss: 6.3677e-05 - val_out_b_loss: 1.3886 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3886\n",
      "Epoch 280/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4153 - out_a_loss: 0.0220 - out_b_loss: 1.3932 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3932 - val_loss: 1.4919 - val_out_a_loss: 0.1027 - val_out_b_loss: 1.3892 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3892\n",
      "Epoch 281/300\n",
      "1000/1000 [==============================] - 0s 103us/step - loss: 1.4564 - out_a_loss: 0.0602 - out_b_loss: 1.3962 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3962 - val_loss: 1.4214 - val_out_a_loss: 0.0194 - val_out_b_loss: 1.4020 - val_out_a_acc: 0.9830 - val_out_b_categorical_crossentropy: 1.4020\n",
      "Epoch 282/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4217 - out_a_loss: 0.0266 - out_b_loss: 1.3952 - out_a_acc: 0.9830 - out_b_categorical_crossentropy: 1.3952 - val_loss: 1.4752 - val_out_a_loss: 0.0146 - val_out_b_loss: 1.4607 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.4607\n",
      "Epoch 283/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.4884 - out_a_loss: 0.0856 - out_b_loss: 1.4028 - out_a_acc: 0.9570 - out_b_categorical_crossentropy: 1.4028 - val_loss: 1.4345 - val_out_a_loss: 0.0458 - val_out_b_loss: 1.3887 - val_out_a_acc: 0.9940 - val_out_b_categorical_crossentropy: 1.3887\n",
      "Epoch 284/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.4247 - out_a_loss: 0.0299 - out_b_loss: 1.3948 - out_a_acc: 0.9910 - out_b_categorical_crossentropy: 1.3948 - val_loss: 1.3955 - val_out_a_loss: 0.0082 - val_out_b_loss: 1.3874 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3874\n",
      "Epoch 285/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4180 - out_a_loss: 0.0279 - out_b_loss: 1.3900 - out_a_acc: 0.9920 - out_b_categorical_crossentropy: 1.3900 - val_loss: 1.4049 - val_out_a_loss: 0.0120 - val_out_b_loss: 1.3929 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3929\n",
      "Epoch 286/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3963 - out_a_loss: 0.0059 - out_b_loss: 1.3903 - out_a_acc: 0.9980 - out_b_categorical_crossentropy: 1.3903 - val_loss: 1.3880 - val_out_a_loss: 0.0015 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 287/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.4057 - out_a_loss: 0.0139 - out_b_loss: 1.3918 - out_a_acc: 0.9870 - out_b_categorical_crossentropy: 1.3918 - val_loss: 1.4782 - val_out_a_loss: 0.0918 - val_out_b_loss: 1.3864 - val_out_a_acc: 0.9970 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 288/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.5111 - out_a_loss: 0.1100 - out_b_loss: 1.4012 - out_a_acc: 0.9620 - out_b_categorical_crossentropy: 1.4012 - val_loss: 1.4993 - val_out_a_loss: 0.0584 - val_out_b_loss: 1.4408 - val_out_a_acc: 0.9980 - val_out_b_categorical_crossentropy: 1.4408\n",
      "Epoch 289/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.5354 - out_a_loss: 0.1268 - out_b_loss: 1.4085 - out_a_acc: 0.9490 - out_b_categorical_crossentropy: 1.4085 - val_loss: 1.4626 - val_out_a_loss: 0.0711 - val_out_b_loss: 1.3915 - val_out_a_acc: 0.9830 - val_out_b_categorical_crossentropy: 1.3915\n",
      "Epoch 290/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.4263 - out_a_loss: 0.0315 - out_b_loss: 1.3947 - out_a_acc: 0.9850 - out_b_categorical_crossentropy: 1.3947 - val_loss: 1.4002 - val_out_a_loss: 0.0130 - val_out_b_loss: 1.3873 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3873\n",
      "Epoch 291/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.4793 - out_a_loss: 0.0768 - out_b_loss: 1.4024 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.4024 - val_loss: 1.4309 - val_out_a_loss: 0.0383 - val_out_b_loss: 1.3926 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3926\n",
      "Epoch 292/300\n",
      "1000/1000 [==============================] - 0s 112us/step - loss: 1.4104 - out_a_loss: 0.0173 - out_b_loss: 1.3931 - out_a_acc: 0.9940 - out_b_categorical_crossentropy: 1.3931 - val_loss: 1.4078 - val_out_a_loss: 0.0204 - val_out_b_loss: 1.3874 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3874\n",
      "Epoch 293/300\n",
      "1000/1000 [==============================] - 0s 108us/step - loss: 1.3919 - out_a_loss: 0.0044 - out_b_loss: 1.3875 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3875 - val_loss: 1.3870 - val_out_a_loss: 6.6049e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 294/300\n",
      "1000/1000 [==============================] - 0s 106us/step - loss: 1.3869 - out_a_loss: 5.9357e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3871 - val_out_a_loss: 6.3481e-04 - val_out_b_loss: 1.3864 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3864\n",
      "Epoch 295/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3869 - out_a_loss: 5.1137e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3869 - val_out_a_loss: 2.6203e-04 - val_out_b_loss: 1.3867 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3867\n",
      "Epoch 296/300\n",
      "1000/1000 [==============================] - 0s 107us/step - loss: 1.3866 - out_a_loss: 2.5202e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3866 - val_out_a_loss: 3.3192e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n",
      "Epoch 297/300\n",
      "1000/1000 [==============================] - 0s 104us/step - loss: 1.3869 - out_a_loss: 4.5351e-04 - out_b_loss: 1.3864 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3864 - val_loss: 1.3886 - val_out_a_loss: 0.0021 - val_out_b_loss: 1.3865 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3865\n",
      "Epoch 298/300\n",
      "1000/1000 [==============================] - 0s 109us/step - loss: 1.3868 - out_a_loss: 3.2297e-04 - out_b_loss: 1.3865 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3865 - val_loss: 1.3871 - val_out_a_loss: 2.1369e-04 - val_out_b_loss: 1.3869 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3869\n",
      "Epoch 299/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3867 - out_a_loss: 3.9085e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3882 - val_out_a_loss: 2.4989e-04 - val_out_b_loss: 1.3879 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 300/300\n",
      "1000/1000 [==============================] - 0s 105us/step - loss: 1.3865 - out_a_loss: 1.8560e-04 - out_b_loss: 1.3863 - out_a_acc: 1.0000 - out_b_categorical_crossentropy: 1.3863 - val_loss: 1.3865 - val_out_a_loss: 1.6570e-04 - val_out_b_loss: 1.3863 - val_out_a_acc: 1.0000 - val_out_b_categorical_crossentropy: 1.3863\n"
     ]
    }
   ],
   "source": [
    "classifier = Classifier()\n",
    "\n",
    "classifier.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot of input data\n"
     ]
    }
   ],
   "source": [
    "# Plot input data\n",
    "\n",
    "data = np.concatenate((classifier.data, classifier.data_out), axis=0)\n",
    "labels = np.concatenate((classifier.labels, classifier.labels_out), axis=0)\n",
    "fig=plt.figure()\n",
    "plt.scatter(data[:, 0], data[:, 1], s=4, c=labels.squeeze())\n",
    "print(\"Plot of input data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"505.5555689482045\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0c15698f50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot classified data - for test data\n",
    "\n",
    "X_test = np.concatenate((classifier.X_test, classifier.X_test_outliers, classifier.data_outlier), axis=0)\n",
    "y_test = np.concatenate((classifier.y_test, classifier.y_test_outliers, classifier.labels_outlier), axis=0)\n",
    "\n",
    "#X_test = np.concatenate((classifier.X_test, classifier.data_outlier), axis=0)\n",
    "#y_test = np.concatenate((classifier.y_test, classifier.labels_outlier), axis=0)\n",
    "\n",
    "\n",
    "#X_test = np.concatenate((classifier.X_test, classifier.X_test_outliers), axis=0)\n",
    "#y_test = np.concatenate((classifier.y_test, classifier.y_test_outliers), axis=0)\n",
    "\n",
    "y_pred = classifier.model.predict(X_test)\n",
    "y_max = y_pred.max(axis = -1)\n",
    "\n",
    "# Plot classified data - for test data\n",
    "\n",
    "#y_labels = y_pred.argmax(axis = -1)\n",
    "\n",
    "fig = plt.figure()\n",
    "#ax = fig.add_subplot(111, projection='3d')\n",
    "#ax = Axes3D(fig)\n",
    "\n",
    "ax.scatter(X_test[:, 0], X_test[:, 1], y_max, c=y_test.squeeze())\n",
    "plt.scatter(X_test[:, 0], X_test[:, 1], s=4, c=y_max.squeeze())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000,)\n",
      "(1616,)\n",
      "(1055,)\n"
     ]
    }
   ],
   "source": [
    "print(y_max.shape)\n",
    "print(np.where(y_max>0.95)[0].shape)\n",
    "print(np.where(y_max<0.55)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 2)\n"
     ]
    }
   ],
   "source": [
    "print(classifier.data_outlier.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000, 2)\n",
      "(3000,)\n",
      "(3000, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(y_max.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}