3. Machine Learning & Deep Learning/190920_(3) _ mnist 실습.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"190920_(3) ; mnist 실습.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"zBwUu0N5iSn6","colab_type":"code","outputId":"a9a3129e-80c4-44b7-a29d-cd4917d247f0","executionInfo":{"status":"ok","timestamp":1569160076857,"user_tz":-540,"elapsed":2261,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["import tensorflow as tf\n","from tensorflow import keras\n","\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","from keras.utils import np_utils\n","from keras.datasets import mnist\n","from keras.models import Sequential\n","from keras.layers import Dense, Activation"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"yL3hSF7DiaW_","colab_type":"code","colab":{}},"source":["(x_train, y_train), (x_test, y_test)= mnist.load_data()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wCcv4Fnuizn8","colab_type":"code","outputId":"b753e588-9636-40e6-ce0b-f3009e299360","executionInfo":{"status":"ok","timestamp":1569160371010,"user_tz":-540,"elapsed":2444,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":592}},"source":["plt.figure(figsize=(10,10))\n","for i in range(25):\n","  plt.subplot(5,5,i+1)\n","  plt.xticks([])\n","  plt.yticks([])\n","  plt.grid(False)\n","  plt.imshow(x_train[i])\n","  plt.xlabel(y_train[i])\n","plt.show()"],"execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkAAAAI/CAYAAACf7mYiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n","text/plain":["<Figure size 720x720 with 25 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Z4z2Qy0DjaRc","colab_type":"code","outputId":"a2066a18-4368-49f9-b8b0-5c925fd78781","executionInfo":{"status":"ok","timestamp":1569160372298,"user_tz":-540,"elapsed":884,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["y_train"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"XGNrXclVkFts","colab_type":"code","colab":{}},"source":["x_train = x_train.reshape(60000,784).astype('float32') /255.0\n","x_test = x_test.reshape(10000,784).astype('float32') /255.0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-pIvW-AsknIN","colab_type":"code","colab":{}},"source":["y_train = np_utils.to_categorical(y_train)\n","y_test = np_utils.to_categorical(y_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XDXCXrhPkpXN","colab_type":"code","colab":{}},"source":["val_x = x_train[50000:]\n","val_y = y_train[50000:]\n","tr_x = x_train[:50000]\n","tr_y = y_train[:50000]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZZKjsfytop4M","colab_type":"code","outputId":"28142549-3196-49af-b1fa-3657b81423ac","executionInfo":{"status":"ok","timestamp":1569160378923,"user_tz":-540,"elapsed":1088,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["tr_x.shape"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(50000, 784)"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"sg30FxVRlL--","colab_type":"code","colab":{}},"source":["tr_rand = np.random.choice(50000, 700)\n","val_rand = np.random.choice(10000, 300)\n","\n","tr_x = tr_x[tr_rand]\n","tr_y = tr_y[tr_rand]\n","val_x = val_x[val_rand]\n","val_y = val_y[val_rand]"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jzY0S-WPpQXd","colab_type":"text"},"source":["# 2. 모델 구성하기"]},{"cell_type":"code","metadata":{"id":"5JTsRLAxpRRe","colab_type":"code","outputId":"ffa9a37b-a887-494d-9e97-13e820e05ed3","executionInfo":{"status":"ok","timestamp":1569160404571,"user_tz":-540,"elapsed":912,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":145}},"source":["model = Sequential()\n","model.add(Dense(units=64, input_dim = 28*28, activation='relu'))\n","model.add(Dense(64, activation='relu'))\n","model.add(Dense(units=10, activation = 'softmax'))"],"execution_count":20,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"o7fFEGsVpWBQ","colab_type":"text"},"source":["# 3. 모델 학습과정 설정하기"]},{"cell_type":"code","metadata":{"id":"EcNCa4s9pUbP","colab_type":"code","outputId":"199a670c-b732-4131-8c2e-62a7159db904","executionInfo":{"status":"ok","timestamp":1569160409397,"user_tz":-540,"elapsed":1038,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"],"execution_count":21,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"m1lmM6YTpZmu","colab_type":"text"},"source":["# 4. 모델 학습시키기"]},{"cell_type":"code","metadata":{"id":"Bg17a95GpYHP","colab_type":"code","outputId":"cdbaeea3-f609-44cc-9264-6793852b282f","executionInfo":{"status":"ok","timestamp":1569160466387,"user_tz":-540,"elapsed":53814,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["hist = model.fit(tr_x, tr_y, epochs=1000, batch_size=32, validation_data=(val_x, val_y))"],"execution_count":22,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.where in 2.0, which has the same broadcast rule as np.where\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","Train on 700 samples, validate on 300 samples\n","Epoch 1/1000\n","700/700 [==============================] - 1s 1ms/step - loss: 2.2954 - acc: 0.1071 - val_loss: 2.2919 - val_acc: 0.1133\n","Epoch 2/1000\n","700/700 [==============================] - 0s 72us/step - loss: 2.2080 - acc: 0.2400 - val_loss: 2.2121 - val_acc: 0.2567\n","Epoch 3/1000\n","700/700 [==============================] - 0s 72us/step - loss: 2.1233 - acc: 0.3357 - val_loss: 2.1311 - val_acc: 0.3400\n","Epoch 4/1000\n","700/700 [==============================] - 0s 69us/step - loss: 2.0342 - acc: 0.4086 - val_loss: 2.0406 - val_acc: 0.4033\n","Epoch 5/1000\n","700/700 [==============================] - 0s 68us/step - loss: 1.9381 - acc: 0.4771 - val_loss: 1.9446 - val_acc: 0.4733\n","Epoch 6/1000\n","700/700 [==============================] - 0s 75us/step - loss: 1.8324 - acc: 0.5286 - val_loss: 1.8433 - val_acc: 0.5100\n","Epoch 7/1000\n","700/700 [==============================] - 0s 73us/step - loss: 1.7236 - acc: 0.6014 - val_loss: 1.7344 - val_acc: 0.5700\n","Epoch 8/1000\n","700/700 [==============================] - 0s 74us/step - loss: 1.6108 - acc: 0.6400 - val_loss: 1.6240 - val_acc: 0.6167\n","Epoch 9/1000\n","700/700 [==============================] - 0s 70us/step - loss: 1.4985 - acc: 0.6614 - val_loss: 1.5185 - val_acc: 0.6567\n","Epoch 10/1000\n","700/700 [==============================] - 0s 69us/step - loss: 1.3925 - acc: 0.7071 - val_loss: 1.4177 - val_acc: 0.6767\n","Epoch 11/1000\n","700/700 [==============================] - 0s 68us/step - loss: 1.2929 - acc: 0.7371 - val_loss: 1.3232 - val_acc: 0.7100\n","Epoch 12/1000\n","700/700 [==============================] - 0s 74us/step - loss: 1.2008 - acc: 0.7700 - val_loss: 1.2327 - val_acc: 0.7300\n","Epoch 13/1000\n","700/700 [==============================] - 0s 70us/step - loss: 1.1164 - acc: 0.7743 - val_loss: 1.1483 - val_acc: 0.7600\n","Epoch 14/1000\n","700/700 [==============================] - 0s 76us/step - loss: 1.0413 - acc: 0.7986 - val_loss: 1.0773 - val_acc: 0.7600\n","Epoch 15/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.9728 - acc: 0.8057 - val_loss: 1.0165 - val_acc: 0.7767\n","Epoch 16/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.9124 - acc: 0.8200 - val_loss: 0.9580 - val_acc: 0.7800\n","Epoch 17/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.8578 - acc: 0.8243 - val_loss: 0.8983 - val_acc: 0.7967\n","Epoch 18/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.8091 - acc: 0.8314 - val_loss: 0.8586 - val_acc: 0.8033\n","Epoch 19/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.7693 - acc: 0.8443 - val_loss: 0.8148 - val_acc: 0.8167\n","Epoch 20/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.7298 - acc: 0.8500 - val_loss: 0.7765 - val_acc: 0.8133\n","Epoch 21/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.6970 - acc: 0.8514 - val_loss: 0.7499 - val_acc: 0.8167\n","Epoch 22/1000\n","700/700 [==============================] - 0s 85us/step - loss: 0.6663 - acc: 0.8586 - val_loss: 0.7235 - val_acc: 0.8200\n","Epoch 23/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.6389 - acc: 0.8671 - val_loss: 0.6978 - val_acc: 0.8133\n","Epoch 24/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.6148 - acc: 0.8686 - val_loss: 0.6720 - val_acc: 0.8100\n","Epoch 25/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.5897 - acc: 0.8786 - val_loss: 0.6546 - val_acc: 0.8200\n","Epoch 26/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.5700 - acc: 0.8800 - val_loss: 0.6372 - val_acc: 0.8200\n","Epoch 27/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.5504 - acc: 0.8743 - val_loss: 0.6315 - val_acc: 0.8267\n","Epoch 28/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.5331 - acc: 0.8871 - val_loss: 0.6119 - val_acc: 0.8233\n","Epoch 29/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.5172 - acc: 0.8886 - val_loss: 0.5907 - val_acc: 0.8433\n","Epoch 30/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.5002 - acc: 0.8886 - val_loss: 0.5805 - val_acc: 0.8333\n","Epoch 31/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.4870 - acc: 0.8886 - val_loss: 0.5694 - val_acc: 0.8467\n","Epoch 32/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.4720 - acc: 0.8929 - val_loss: 0.5546 - val_acc: 0.8433\n","Epoch 33/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.4594 - acc: 0.9014 - val_loss: 0.5428 - val_acc: 0.8467\n","Epoch 34/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.4464 - acc: 0.8986 - val_loss: 0.5290 - val_acc: 0.8567\n","Epoch 35/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.4348 - acc: 0.8986 - val_loss: 0.5341 - val_acc: 0.8467\n","Epoch 36/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.4252 - acc: 0.9057 - val_loss: 0.5123 - val_acc: 0.8500\n","Epoch 37/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.4142 - acc: 0.9057 - val_loss: 0.5116 - val_acc: 0.8467\n","Epoch 38/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.4035 - acc: 0.9086 - val_loss: 0.5074 - val_acc: 0.8467\n","Epoch 39/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.3945 - acc: 0.9129 - val_loss: 0.4989 - val_acc: 0.8467\n","Epoch 40/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.3838 - acc: 0.9157 - val_loss: 0.4929 - val_acc: 0.8467\n","Epoch 41/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.3759 - acc: 0.9186 - val_loss: 0.4875 - val_acc: 0.8533\n","Epoch 42/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.3663 - acc: 0.9186 - val_loss: 0.4847 - val_acc: 0.8533\n","Epoch 43/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.3596 - acc: 0.9157 - val_loss: 0.4848 - val_acc: 0.8533\n","Epoch 44/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.3501 - acc: 0.9229 - val_loss: 0.4786 - val_acc: 0.8533\n","Epoch 45/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.3436 - acc: 0.9200 - val_loss: 0.4751 - val_acc: 0.8433\n","Epoch 46/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.3359 - acc: 0.9186 - val_loss: 0.4643 - val_acc: 0.8567\n","Epoch 47/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.3289 - acc: 0.9243 - val_loss: 0.4635 - val_acc: 0.8567\n","Epoch 48/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.3215 - acc: 0.9243 - val_loss: 0.4663 - val_acc: 0.8600\n","Epoch 49/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.3153 - acc: 0.9257 - val_loss: 0.4681 - val_acc: 0.8600\n","Epoch 50/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.3089 - acc: 0.9357 - val_loss: 0.4555 - val_acc: 0.8600\n","Epoch 51/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.3022 - acc: 0.9343 - val_loss: 0.4466 - val_acc: 0.8567\n","Epoch 52/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.2959 - acc: 0.9357 - val_loss: 0.4483 - val_acc: 0.8600\n","Epoch 53/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.2899 - acc: 0.9386 - val_loss: 0.4483 - val_acc: 0.8667\n","Epoch 54/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.2840 - acc: 0.9400 - val_loss: 0.4404 - val_acc: 0.8633\n","Epoch 55/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.2786 - acc: 0.9343 - val_loss: 0.4429 - val_acc: 0.8567\n","Epoch 56/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.2727 - acc: 0.9400 - val_loss: 0.4355 - val_acc: 0.8600\n","Epoch 57/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.2668 - acc: 0.9400 - val_loss: 0.4309 - val_acc: 0.8600\n","Epoch 58/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.2627 - acc: 0.9371 - val_loss: 0.4330 - val_acc: 0.8567\n","Epoch 59/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.2568 - acc: 0.9429 - val_loss: 0.4325 - val_acc: 0.8667\n","Epoch 60/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.2518 - acc: 0.9457 - val_loss: 0.4264 - val_acc: 0.8567\n","Epoch 61/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.2482 - acc: 0.9386 - val_loss: 0.4267 - val_acc: 0.8700\n","Epoch 62/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.2419 - acc: 0.9486 - val_loss: 0.4257 - val_acc: 0.8700\n","Epoch 63/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.2379 - acc: 0.9514 - val_loss: 0.4363 - val_acc: 0.8567\n","Epoch 64/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.2338 - acc: 0.9529 - val_loss: 0.4170 - val_acc: 0.8633\n","Epoch 65/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.2298 - acc: 0.9514 - val_loss: 0.4172 - val_acc: 0.8700\n","Epoch 66/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.2249 - acc: 0.9529 - val_loss: 0.4206 - val_acc: 0.8633\n","Epoch 67/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.2213 - acc: 0.9557 - val_loss: 0.4160 - val_acc: 0.8633\n","Epoch 68/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.2166 - acc: 0.9586 - val_loss: 0.4235 - val_acc: 0.8633\n","Epoch 69/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.2122 - acc: 0.9600 - val_loss: 0.4216 - val_acc: 0.8567\n","Epoch 70/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.2082 - acc: 0.9600 - val_loss: 0.4065 - val_acc: 0.8800\n","Epoch 71/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.2049 - acc: 0.9629 - val_loss: 0.4135 - val_acc: 0.8700\n","Epoch 72/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.2011 - acc: 0.9614 - val_loss: 0.4128 - val_acc: 0.8633\n","Epoch 73/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.1968 - acc: 0.9643 - val_loss: 0.4190 - val_acc: 0.8633\n","Epoch 74/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.1937 - acc: 0.9671 - val_loss: 0.4202 - val_acc: 0.8567\n","Epoch 75/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.1900 - acc: 0.9643 - val_loss: 0.4167 - val_acc: 0.8667\n","Epoch 76/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.1862 - acc: 0.9643 - val_loss: 0.4117 - val_acc: 0.8667\n","Epoch 77/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.1837 - acc: 0.9686 - val_loss: 0.4184 - val_acc: 0.8667\n","Epoch 78/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.1796 - acc: 0.9714 - val_loss: 0.4063 - val_acc: 0.8667\n","Epoch 79/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.1772 - acc: 0.9686 - val_loss: 0.4117 - val_acc: 0.8667\n","Epoch 80/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.1731 - acc: 0.9714 - val_loss: 0.4131 - val_acc: 0.8667\n","Epoch 81/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.1705 - acc: 0.9729 - val_loss: 0.4075 - val_acc: 0.8667\n","Epoch 82/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.1668 - acc: 0.9729 - val_loss: 0.4031 - val_acc: 0.8767\n","Epoch 83/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.1640 - acc: 0.9729 - val_loss: 0.4080 - val_acc: 0.8700\n","Epoch 84/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.1613 - acc: 0.9786 - val_loss: 0.4001 - val_acc: 0.8700\n","Epoch 85/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.1583 - acc: 0.9771 - val_loss: 0.4011 - val_acc: 0.8767\n","Epoch 86/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.1553 - acc: 0.9743 - val_loss: 0.4117 - val_acc: 0.8667\n","Epoch 87/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.1526 - acc: 0.9814 - val_loss: 0.4043 - val_acc: 0.8700\n","Epoch 88/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.1494 - acc: 0.9786 - val_loss: 0.4079 - val_acc: 0.8767\n","Epoch 89/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.1471 - acc: 0.9786 - val_loss: 0.4051 - val_acc: 0.8700\n","Epoch 90/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.1446 - acc: 0.9786 - val_loss: 0.4100 - val_acc: 0.8667\n","Epoch 91/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.1418 - acc: 0.9829 - val_loss: 0.4045 - val_acc: 0.8700\n","Epoch 92/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.1391 - acc: 0.9814 - val_loss: 0.4128 - val_acc: 0.8667\n","Epoch 93/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.1366 - acc: 0.9857 - val_loss: 0.4067 - val_acc: 0.8700\n","Epoch 94/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.1342 - acc: 0.9900 - val_loss: 0.4064 - val_acc: 0.8667\n","Epoch 95/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.1320 - acc: 0.9871 - val_loss: 0.4050 - val_acc: 0.8800\n","Epoch 96/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.1292 - acc: 0.9871 - val_loss: 0.4013 - val_acc: 0.8700\n","Epoch 97/1000\n","700/700 [==============================] - 0s 84us/step - loss: 0.1275 - acc: 0.9886 - val_loss: 0.4032 - val_acc: 0.8700\n","Epoch 98/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.1254 - acc: 0.9886 - val_loss: 0.4044 - val_acc: 0.8733\n","Epoch 99/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.1226 - acc: 0.9886 - val_loss: 0.4072 - val_acc: 0.8667\n","Epoch 100/1000\n","700/700 [==============================] - 0s 84us/step - loss: 0.1202 - acc: 0.9871 - val_loss: 0.4147 - val_acc: 0.8600\n","Epoch 101/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.1185 - acc: 0.9886 - val_loss: 0.4019 - val_acc: 0.8733\n","Epoch 102/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.1164 - acc: 0.9886 - val_loss: 0.4052 - val_acc: 0.8667\n","Epoch 103/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.1144 - acc: 0.9886 - val_loss: 0.4039 - val_acc: 0.8667\n","Epoch 104/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.1123 - acc: 0.9900 - val_loss: 0.4058 - val_acc: 0.8633\n","Epoch 105/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.1104 - acc: 0.9900 - val_loss: 0.4011 - val_acc: 0.8800\n","Epoch 106/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.1089 - acc: 0.9914 - val_loss: 0.4061 - val_acc: 0.8700\n","Epoch 107/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.1066 - acc: 0.9900 - val_loss: 0.4050 - val_acc: 0.8800\n","Epoch 108/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.1050 - acc: 0.9900 - val_loss: 0.4011 - val_acc: 0.8700\n","Epoch 109/1000\n","700/700 [==============================] - 0s 87us/step - loss: 0.1033 - acc: 0.9914 - val_loss: 0.4010 - val_acc: 0.8667\n","Epoch 110/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.1017 - acc: 0.9914 - val_loss: 0.4026 - val_acc: 0.8667\n","Epoch 111/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0996 - acc: 0.9914 - val_loss: 0.4068 - val_acc: 0.8700\n","Epoch 112/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0983 - acc: 0.9914 - val_loss: 0.4048 - val_acc: 0.8700\n","Epoch 113/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0959 - acc: 0.9914 - val_loss: 0.4049 - val_acc: 0.8700\n","Epoch 114/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0944 - acc: 0.9929 - val_loss: 0.4046 - val_acc: 0.8700\n","Epoch 115/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0928 - acc: 0.9929 - val_loss: 0.4078 - val_acc: 0.8600\n","Epoch 116/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0912 - acc: 0.9929 - val_loss: 0.4000 - val_acc: 0.8733\n","Epoch 117/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0895 - acc: 0.9929 - val_loss: 0.4063 - val_acc: 0.8633\n","Epoch 118/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0882 - acc: 0.9929 - val_loss: 0.4100 - val_acc: 0.8633\n","Epoch 119/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0874 - acc: 0.9914 - val_loss: 0.4118 - val_acc: 0.8633\n","Epoch 120/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0853 - acc: 0.9929 - val_loss: 0.4097 - val_acc: 0.8633\n","Epoch 121/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0842 - acc: 0.9929 - val_loss: 0.4070 - val_acc: 0.8633\n","Epoch 122/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0823 - acc: 0.9957 - val_loss: 0.4063 - val_acc: 0.8700\n","Epoch 123/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0810 - acc: 0.9943 - val_loss: 0.4068 - val_acc: 0.8667\n","Epoch 124/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0797 - acc: 0.9957 - val_loss: 0.4085 - val_acc: 0.8700\n","Epoch 125/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0786 - acc: 0.9957 - val_loss: 0.4047 - val_acc: 0.8733\n","Epoch 126/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0771 - acc: 0.9957 - val_loss: 0.4018 - val_acc: 0.8767\n","Epoch 127/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0759 - acc: 0.9957 - val_loss: 0.4062 - val_acc: 0.8667\n","Epoch 128/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0749 - acc: 0.9957 - val_loss: 0.4062 - val_acc: 0.8667\n","Epoch 129/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0735 - acc: 0.9957 - val_loss: 0.4064 - val_acc: 0.8667\n","Epoch 130/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0727 - acc: 0.9957 - val_loss: 0.4127 - val_acc: 0.8633\n","Epoch 131/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0712 - acc: 0.9957 - val_loss: 0.4072 - val_acc: 0.8633\n","Epoch 132/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0703 - acc: 0.9957 - val_loss: 0.4098 - val_acc: 0.8633\n","Epoch 133/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0693 - acc: 0.9971 - val_loss: 0.4132 - val_acc: 0.8633\n","Epoch 134/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0678 - acc: 0.9971 - val_loss: 0.4172 - val_acc: 0.8633\n","Epoch 135/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0673 - acc: 0.9957 - val_loss: 0.4116 - val_acc: 0.8633\n","Epoch 136/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0660 - acc: 0.9957 - val_loss: 0.4077 - val_acc: 0.8667\n","Epoch 137/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0650 - acc: 0.9971 - val_loss: 0.4061 - val_acc: 0.8633\n","Epoch 138/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0640 - acc: 0.9971 - val_loss: 0.4090 - val_acc: 0.8733\n","Epoch 139/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0631 - acc: 0.9971 - val_loss: 0.4118 - val_acc: 0.8600\n","Epoch 140/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0621 - acc: 0.9971 - val_loss: 0.4106 - val_acc: 0.8700\n","Epoch 141/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0614 - acc: 0.9986 - val_loss: 0.4112 - val_acc: 0.8633\n","Epoch 142/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0601 - acc: 0.9986 - val_loss: 0.4109 - val_acc: 0.8700\n","Epoch 143/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0594 - acc: 0.9986 - val_loss: 0.4115 - val_acc: 0.8633\n","Epoch 144/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0585 - acc: 0.9986 - val_loss: 0.4131 - val_acc: 0.8633\n","Epoch 145/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0575 - acc: 0.9986 - val_loss: 0.4169 - val_acc: 0.8633\n","Epoch 146/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0568 - acc: 0.9986 - val_loss: 0.4131 - val_acc: 0.8733\n","Epoch 147/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0561 - acc: 0.9971 - val_loss: 0.4112 - val_acc: 0.8633\n","Epoch 148/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0553 - acc: 0.9986 - val_loss: 0.4137 - val_acc: 0.8633\n","Epoch 149/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0545 - acc: 0.9986 - val_loss: 0.4094 - val_acc: 0.8633\n","Epoch 150/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0536 - acc: 0.9986 - val_loss: 0.4064 - val_acc: 0.8733\n","Epoch 151/1000\n","700/700 [==============================] - 0s 84us/step - loss: 0.0530 - acc: 0.9986 - val_loss: 0.4142 - val_acc: 0.8667\n","Epoch 152/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0521 - acc: 0.9986 - val_loss: 0.4171 - val_acc: 0.8667\n","Epoch 153/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0513 - acc: 0.9986 - val_loss: 0.4143 - val_acc: 0.8667\n","Epoch 154/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0508 - acc: 0.9986 - val_loss: 0.4157 - val_acc: 0.8633\n","Epoch 155/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0501 - acc: 0.9986 - val_loss: 0.4129 - val_acc: 0.8633\n","Epoch 156/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0495 - acc: 0.9986 - val_loss: 0.4164 - val_acc: 0.8633\n","Epoch 157/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0487 - acc: 0.9986 - val_loss: 0.4188 - val_acc: 0.8667\n","Epoch 158/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0482 - acc: 0.9986 - val_loss: 0.4184 - val_acc: 0.8633\n","Epoch 159/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0475 - acc: 0.9986 - val_loss: 0.4173 - val_acc: 0.8700\n","Epoch 160/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0468 - acc: 0.9986 - val_loss: 0.4152 - val_acc: 0.8667\n","Epoch 161/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0462 - acc: 1.0000 - val_loss: 0.4179 - val_acc: 0.8667\n","Epoch 162/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0457 - acc: 1.0000 - val_loss: 0.4134 - val_acc: 0.8667\n","Epoch 163/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0450 - acc: 1.0000 - val_loss: 0.4190 - val_acc: 0.8700\n","Epoch 164/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0447 - acc: 1.0000 - val_loss: 0.4214 - val_acc: 0.8700\n","Epoch 165/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0439 - acc: 1.0000 - val_loss: 0.4202 - val_acc: 0.8667\n","Epoch 166/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0434 - acc: 1.0000 - val_loss: 0.4210 - val_acc: 0.8700\n","Epoch 167/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0429 - acc: 1.0000 - val_loss: 0.4198 - val_acc: 0.8667\n","Epoch 168/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0425 - acc: 1.0000 - val_loss: 0.4218 - val_acc: 0.8667\n","Epoch 169/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0418 - acc: 1.0000 - val_loss: 0.4223 - val_acc: 0.8700\n","Epoch 170/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0413 - acc: 1.0000 - val_loss: 0.4184 - val_acc: 0.8700\n","Epoch 171/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0408 - acc: 1.0000 - val_loss: 0.4213 - val_acc: 0.8700\n","Epoch 172/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0401 - acc: 1.0000 - val_loss: 0.4201 - val_acc: 0.8667\n","Epoch 173/1000\n","700/700 [==============================] - 0s 82us/step - loss: 0.0397 - acc: 1.0000 - val_loss: 0.4256 - val_acc: 0.8733\n","Epoch 174/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0393 - acc: 1.0000 - val_loss: 0.4234 - val_acc: 0.8700\n","Epoch 175/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0388 - acc: 1.0000 - val_loss: 0.4233 - val_acc: 0.8733\n","Epoch 176/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0384 - acc: 1.0000 - val_loss: 0.4249 - val_acc: 0.8700\n","Epoch 177/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0378 - acc: 1.0000 - val_loss: 0.4209 - val_acc: 0.8733\n","Epoch 178/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0375 - acc: 1.0000 - val_loss: 0.4244 - val_acc: 0.8733\n","Epoch 179/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0371 - acc: 1.0000 - val_loss: 0.4246 - val_acc: 0.8733\n","Epoch 180/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0366 - acc: 1.0000 - val_loss: 0.4248 - val_acc: 0.8733\n","Epoch 181/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0362 - acc: 1.0000 - val_loss: 0.4271 - val_acc: 0.8767\n","Epoch 182/1000\n","700/700 [==============================] - 0s 88us/step - loss: 0.0358 - acc: 1.0000 - val_loss: 0.4241 - val_acc: 0.8733\n","Epoch 183/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0355 - acc: 1.0000 - val_loss: 0.4262 - val_acc: 0.8733\n","Epoch 184/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0350 - acc: 1.0000 - val_loss: 0.4230 - val_acc: 0.8733\n","Epoch 185/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0346 - acc: 1.0000 - val_loss: 0.4253 - val_acc: 0.8767\n","Epoch 186/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0342 - acc: 1.0000 - val_loss: 0.4271 - val_acc: 0.8767\n","Epoch 187/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0337 - acc: 1.0000 - val_loss: 0.4255 - val_acc: 0.8733\n","Epoch 188/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0334 - acc: 1.0000 - val_loss: 0.4251 - val_acc: 0.8733\n","Epoch 189/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0330 - acc: 1.0000 - val_loss: 0.4258 - val_acc: 0.8733\n","Epoch 190/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0327 - acc: 1.0000 - val_loss: 0.4273 - val_acc: 0.8733\n","Epoch 191/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0324 - acc: 1.0000 - val_loss: 0.4300 - val_acc: 0.8733\n","Epoch 192/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0320 - acc: 1.0000 - val_loss: 0.4301 - val_acc: 0.8733\n","Epoch 193/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0316 - acc: 1.0000 - val_loss: 0.4272 - val_acc: 0.8800\n","Epoch 194/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0313 - acc: 1.0000 - val_loss: 0.4273 - val_acc: 0.8767\n","Epoch 195/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0310 - acc: 1.0000 - val_loss: 0.4283 - val_acc: 0.8733\n","Epoch 196/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0307 - acc: 1.0000 - val_loss: 0.4265 - val_acc: 0.8767\n","Epoch 197/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0303 - acc: 1.0000 - val_loss: 0.4319 - val_acc: 0.8733\n","Epoch 198/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0301 - acc: 1.0000 - val_loss: 0.4304 - val_acc: 0.8767\n","Epoch 199/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0298 - acc: 1.0000 - val_loss: 0.4334 - val_acc: 0.8733\n","Epoch 200/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0294 - acc: 1.0000 - val_loss: 0.4297 - val_acc: 0.8733\n","Epoch 201/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0291 - acc: 1.0000 - val_loss: 0.4317 - val_acc: 0.8733\n","Epoch 202/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0288 - acc: 1.0000 - val_loss: 0.4307 - val_acc: 0.8800\n","Epoch 203/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0286 - acc: 1.0000 - val_loss: 0.4340 - val_acc: 0.8733\n","Epoch 204/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0283 - acc: 1.0000 - val_loss: 0.4316 - val_acc: 0.8767\n","Epoch 205/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0279 - acc: 1.0000 - val_loss: 0.4327 - val_acc: 0.8733\n","Epoch 206/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0276 - acc: 1.0000 - val_loss: 0.4290 - val_acc: 0.8800\n","Epoch 207/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0274 - acc: 1.0000 - val_loss: 0.4306 - val_acc: 0.8800\n","Epoch 208/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0271 - acc: 1.0000 - val_loss: 0.4328 - val_acc: 0.8800\n","Epoch 209/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0269 - acc: 1.0000 - val_loss: 0.4325 - val_acc: 0.8800\n","Epoch 210/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0267 - acc: 1.0000 - val_loss: 0.4333 - val_acc: 0.8800\n","Epoch 211/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0264 - acc: 1.0000 - val_loss: 0.4339 - val_acc: 0.8767\n","Epoch 212/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0261 - acc: 1.0000 - val_loss: 0.4373 - val_acc: 0.8733\n","Epoch 213/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0259 - acc: 1.0000 - val_loss: 0.4377 - val_acc: 0.8733\n","Epoch 214/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0256 - acc: 1.0000 - val_loss: 0.4354 - val_acc: 0.8767\n","Epoch 215/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0254 - acc: 1.0000 - val_loss: 0.4347 - val_acc: 0.8733\n","Epoch 216/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0251 - acc: 1.0000 - val_loss: 0.4328 - val_acc: 0.8767\n","Epoch 217/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0250 - acc: 1.0000 - val_loss: 0.4354 - val_acc: 0.8733\n","Epoch 218/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0247 - acc: 1.0000 - val_loss: 0.4389 - val_acc: 0.8733\n","Epoch 219/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0245 - acc: 1.0000 - val_loss: 0.4346 - val_acc: 0.8800\n","Epoch 220/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0242 - acc: 1.0000 - val_loss: 0.4359 - val_acc: 0.8767\n","Epoch 221/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0240 - acc: 1.0000 - val_loss: 0.4370 - val_acc: 0.8800\n","Epoch 222/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0238 - acc: 1.0000 - val_loss: 0.4378 - val_acc: 0.8767\n","Epoch 223/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0236 - acc: 1.0000 - val_loss: 0.4377 - val_acc: 0.8767\n","Epoch 224/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0234 - acc: 1.0000 - val_loss: 0.4376 - val_acc: 0.8733\n","Epoch 225/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0232 - acc: 1.0000 - val_loss: 0.4404 - val_acc: 0.8767\n","Epoch 226/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0230 - acc: 1.0000 - val_loss: 0.4381 - val_acc: 0.8733\n","Epoch 227/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0228 - acc: 1.0000 - val_loss: 0.4382 - val_acc: 0.8800\n","Epoch 228/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0226 - acc: 1.0000 - val_loss: 0.4397 - val_acc: 0.8800\n","Epoch 229/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0224 - acc: 1.0000 - val_loss: 0.4390 - val_acc: 0.8767\n","Epoch 230/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0222 - acc: 1.0000 - val_loss: 0.4385 - val_acc: 0.8800\n","Epoch 231/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0221 - acc: 1.0000 - val_loss: 0.4407 - val_acc: 0.8800\n","Epoch 232/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0219 - acc: 1.0000 - val_loss: 0.4406 - val_acc: 0.8800\n","Epoch 233/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0217 - acc: 1.0000 - val_loss: 0.4430 - val_acc: 0.8800\n","Epoch 234/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0215 - acc: 1.0000 - val_loss: 0.4397 - val_acc: 0.8800\n","Epoch 235/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0213 - acc: 1.0000 - val_loss: 0.4419 - val_acc: 0.8800\n","Epoch 236/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0211 - acc: 1.0000 - val_loss: 0.4436 - val_acc: 0.8800\n","Epoch 237/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0210 - acc: 1.0000 - val_loss: 0.4411 - val_acc: 0.8800\n","Epoch 238/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0208 - acc: 1.0000 - val_loss: 0.4418 - val_acc: 0.8800\n","Epoch 239/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0206 - acc: 1.0000 - val_loss: 0.4426 - val_acc: 0.8767\n","Epoch 240/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0205 - acc: 1.0000 - val_loss: 0.4419 - val_acc: 0.8800\n","Epoch 241/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0203 - acc: 1.0000 - val_loss: 0.4422 - val_acc: 0.8800\n","Epoch 242/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0201 - acc: 1.0000 - val_loss: 0.4442 - val_acc: 0.8800\n","Epoch 243/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0200 - acc: 1.0000 - val_loss: 0.4460 - val_acc: 0.8767\n","Epoch 244/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0198 - acc: 1.0000 - val_loss: 0.4438 - val_acc: 0.8800\n","Epoch 245/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0197 - acc: 1.0000 - val_loss: 0.4447 - val_acc: 0.8800\n","Epoch 246/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0195 - acc: 1.0000 - val_loss: 0.4435 - val_acc: 0.8800\n","Epoch 247/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0194 - acc: 1.0000 - val_loss: 0.4458 - val_acc: 0.8800\n","Epoch 248/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0192 - acc: 1.0000 - val_loss: 0.4467 - val_acc: 0.8800\n","Epoch 249/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0191 - acc: 1.0000 - val_loss: 0.4468 - val_acc: 0.8800\n","Epoch 250/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0189 - acc: 1.0000 - val_loss: 0.4451 - val_acc: 0.8800\n","Epoch 251/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0188 - acc: 1.0000 - val_loss: 0.4452 - val_acc: 0.8800\n","Epoch 252/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0186 - acc: 1.0000 - val_loss: 0.4472 - val_acc: 0.8800\n","Epoch 253/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0185 - acc: 1.0000 - val_loss: 0.4495 - val_acc: 0.8800\n","Epoch 254/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0183 - acc: 1.0000 - val_loss: 0.4487 - val_acc: 0.8800\n","Epoch 255/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0182 - acc: 1.0000 - val_loss: 0.4478 - val_acc: 0.8800\n","Epoch 256/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0181 - acc: 1.0000 - val_loss: 0.4490 - val_acc: 0.8800\n","Epoch 257/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0179 - acc: 1.0000 - val_loss: 0.4479 - val_acc: 0.8800\n","Epoch 258/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0178 - acc: 1.0000 - val_loss: 0.4497 - val_acc: 0.8800\n","Epoch 259/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0177 - acc: 1.0000 - val_loss: 0.4491 - val_acc: 0.8800\n","Epoch 260/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0175 - acc: 1.0000 - val_loss: 0.4504 - val_acc: 0.8800\n","Epoch 261/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0174 - acc: 1.0000 - val_loss: 0.4509 - val_acc: 0.8800\n","Epoch 262/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0173 - acc: 1.0000 - val_loss: 0.4507 - val_acc: 0.8800\n","Epoch 263/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0172 - acc: 1.0000 - val_loss: 0.4507 - val_acc: 0.8800\n","Epoch 264/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0170 - acc: 1.0000 - val_loss: 0.4510 - val_acc: 0.8800\n","Epoch 265/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0169 - acc: 1.0000 - val_loss: 0.4511 - val_acc: 0.8800\n","Epoch 266/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0168 - acc: 1.0000 - val_loss: 0.4505 - val_acc: 0.8800\n","Epoch 267/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0167 - acc: 1.0000 - val_loss: 0.4516 - val_acc: 0.8800\n","Epoch 268/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0166 - acc: 1.0000 - val_loss: 0.4526 - val_acc: 0.8800\n","Epoch 269/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0165 - acc: 1.0000 - val_loss: 0.4512 - val_acc: 0.8800\n","Epoch 270/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0163 - acc: 1.0000 - val_loss: 0.4501 - val_acc: 0.8800\n","Epoch 271/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0162 - acc: 1.0000 - val_loss: 0.4520 - val_acc: 0.8800\n","Epoch 272/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0161 - acc: 1.0000 - val_loss: 0.4513 - val_acc: 0.8800\n","Epoch 273/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0160 - acc: 1.0000 - val_loss: 0.4532 - val_acc: 0.8800\n","Epoch 274/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0159 - acc: 1.0000 - val_loss: 0.4535 - val_acc: 0.8800\n","Epoch 275/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0158 - acc: 1.0000 - val_loss: 0.4535 - val_acc: 0.8800\n","Epoch 276/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0157 - acc: 1.0000 - val_loss: 0.4544 - val_acc: 0.8800\n","Epoch 277/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0156 - acc: 1.0000 - val_loss: 0.4530 - val_acc: 0.8800\n","Epoch 278/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0154 - acc: 1.0000 - val_loss: 0.4550 - val_acc: 0.8800\n","Epoch 279/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0154 - acc: 1.0000 - val_loss: 0.4552 - val_acc: 0.8800\n","Epoch 280/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0153 - acc: 1.0000 - val_loss: 0.4539 - val_acc: 0.8800\n","Epoch 281/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0152 - acc: 1.0000 - val_loss: 0.4547 - val_acc: 0.8800\n","Epoch 282/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0150 - acc: 1.0000 - val_loss: 0.4542 - val_acc: 0.8800\n","Epoch 283/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0149 - acc: 1.0000 - val_loss: 0.4542 - val_acc: 0.8800\n","Epoch 284/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0149 - acc: 1.0000 - val_loss: 0.4551 - val_acc: 0.8800\n","Epoch 285/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0148 - acc: 1.0000 - val_loss: 0.4560 - val_acc: 0.8800\n","Epoch 286/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0147 - acc: 1.0000 - val_loss: 0.4565 - val_acc: 0.8800\n","Epoch 287/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0146 - acc: 1.0000 - val_loss: 0.4566 - val_acc: 0.8800\n","Epoch 288/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0145 - acc: 1.0000 - val_loss: 0.4566 - val_acc: 0.8800\n","Epoch 289/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0144 - acc: 1.0000 - val_loss: 0.4579 - val_acc: 0.8800\n","Epoch 290/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0143 - acc: 1.0000 - val_loss: 0.4560 - val_acc: 0.8800\n","Epoch 291/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0142 - acc: 1.0000 - val_loss: 0.4573 - val_acc: 0.8800\n","Epoch 292/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0141 - acc: 1.0000 - val_loss: 0.4570 - val_acc: 0.8800\n","Epoch 293/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0140 - acc: 1.0000 - val_loss: 0.4570 - val_acc: 0.8800\n","Epoch 294/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0139 - acc: 1.0000 - val_loss: 0.4570 - val_acc: 0.8800\n","Epoch 295/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0139 - acc: 1.0000 - val_loss: 0.4574 - val_acc: 0.8800\n","Epoch 296/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0138 - acc: 1.0000 - val_loss: 0.4584 - val_acc: 0.8800\n","Epoch 297/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0137 - acc: 1.0000 - val_loss: 0.4580 - val_acc: 0.8800\n","Epoch 298/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0136 - acc: 1.0000 - val_loss: 0.4596 - val_acc: 0.8800\n","Epoch 299/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0135 - acc: 1.0000 - val_loss: 0.4596 - val_acc: 0.8800\n","Epoch 300/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0135 - acc: 1.0000 - val_loss: 0.4577 - val_acc: 0.8800\n","Epoch 301/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0134 - acc: 1.0000 - val_loss: 0.4597 - val_acc: 0.8800\n","Epoch 302/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0133 - acc: 1.0000 - val_loss: 0.4612 - val_acc: 0.8800\n","Epoch 303/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0132 - acc: 1.0000 - val_loss: 0.4599 - val_acc: 0.8800\n","Epoch 304/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0131 - acc: 1.0000 - val_loss: 0.4602 - val_acc: 0.8800\n","Epoch 305/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0131 - acc: 1.0000 - val_loss: 0.4606 - val_acc: 0.8800\n","Epoch 306/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0130 - acc: 1.0000 - val_loss: 0.4615 - val_acc: 0.8800\n","Epoch 307/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0129 - acc: 1.0000 - val_loss: 0.4626 - val_acc: 0.8800\n","Epoch 308/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0128 - acc: 1.0000 - val_loss: 0.4623 - val_acc: 0.8800\n","Epoch 309/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0127 - acc: 1.0000 - val_loss: 0.4628 - val_acc: 0.8800\n","Epoch 310/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0127 - acc: 1.0000 - val_loss: 0.4619 - val_acc: 0.8800\n","Epoch 311/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0126 - acc: 1.0000 - val_loss: 0.4617 - val_acc: 0.8800\n","Epoch 312/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0125 - acc: 1.0000 - val_loss: 0.4628 - val_acc: 0.8800\n","Epoch 313/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0125 - acc: 1.0000 - val_loss: 0.4637 - val_acc: 0.8800\n","Epoch 314/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0124 - acc: 1.0000 - val_loss: 0.4636 - val_acc: 0.8800\n","Epoch 315/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0123 - acc: 1.0000 - val_loss: 0.4634 - val_acc: 0.8800\n","Epoch 316/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0123 - acc: 1.0000 - val_loss: 0.4636 - val_acc: 0.8800\n","Epoch 317/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0122 - acc: 1.0000 - val_loss: 0.4639 - val_acc: 0.8800\n","Epoch 318/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0121 - acc: 1.0000 - val_loss: 0.4639 - val_acc: 0.8800\n","Epoch 319/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0120 - acc: 1.0000 - val_loss: 0.4646 - val_acc: 0.8800\n","Epoch 320/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0120 - acc: 1.0000 - val_loss: 0.4639 - val_acc: 0.8800\n","Epoch 321/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0119 - acc: 1.0000 - val_loss: 0.4656 - val_acc: 0.8800\n","Epoch 322/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0118 - acc: 1.0000 - val_loss: 0.4653 - val_acc: 0.8800\n","Epoch 323/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0118 - acc: 1.0000 - val_loss: 0.4651 - val_acc: 0.8800\n","Epoch 324/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0117 - acc: 1.0000 - val_loss: 0.4653 - val_acc: 0.8800\n","Epoch 325/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0116 - acc: 1.0000 - val_loss: 0.4666 - val_acc: 0.8800\n","Epoch 326/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0116 - acc: 1.0000 - val_loss: 0.4678 - val_acc: 0.8800\n","Epoch 327/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0115 - acc: 1.0000 - val_loss: 0.4664 - val_acc: 0.8800\n","Epoch 328/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0114 - acc: 1.0000 - val_loss: 0.4672 - val_acc: 0.8800\n","Epoch 329/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0114 - acc: 1.0000 - val_loss: 0.4672 - val_acc: 0.8800\n","Epoch 330/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0113 - acc: 1.0000 - val_loss: 0.4681 - val_acc: 0.8800\n","Epoch 331/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0113 - acc: 1.0000 - val_loss: 0.4683 - val_acc: 0.8800\n","Epoch 332/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0112 - acc: 1.0000 - val_loss: 0.4669 - val_acc: 0.8800\n","Epoch 333/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0112 - acc: 1.0000 - val_loss: 0.4680 - val_acc: 0.8800\n","Epoch 334/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0111 - acc: 1.0000 - val_loss: 0.4683 - val_acc: 0.8800\n","Epoch 335/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0110 - acc: 1.0000 - val_loss: 0.4682 - val_acc: 0.8800\n","Epoch 336/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0110 - acc: 1.0000 - val_loss: 0.4682 - val_acc: 0.8800\n","Epoch 337/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0109 - acc: 1.0000 - val_loss: 0.4698 - val_acc: 0.8800\n","Epoch 338/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0109 - acc: 1.0000 - val_loss: 0.4698 - val_acc: 0.8800\n","Epoch 339/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0108 - acc: 1.0000 - val_loss: 0.4700 - val_acc: 0.8800\n","Epoch 340/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0107 - acc: 1.0000 - val_loss: 0.4690 - val_acc: 0.8800\n","Epoch 341/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0107 - acc: 1.0000 - val_loss: 0.4705 - val_acc: 0.8800\n","Epoch 342/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0106 - acc: 1.0000 - val_loss: 0.4704 - val_acc: 0.8800\n","Epoch 343/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0106 - acc: 1.0000 - val_loss: 0.4699 - val_acc: 0.8800\n","Epoch 344/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0105 - acc: 1.0000 - val_loss: 0.4698 - val_acc: 0.8800\n","Epoch 345/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0105 - acc: 1.0000 - val_loss: 0.4697 - val_acc: 0.8800\n","Epoch 346/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0104 - acc: 1.0000 - val_loss: 0.4713 - val_acc: 0.8800\n","Epoch 347/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0104 - acc: 1.0000 - val_loss: 0.4709 - val_acc: 0.8800\n","Epoch 348/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0103 - acc: 1.0000 - val_loss: 0.4711 - val_acc: 0.8800\n","Epoch 349/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0103 - acc: 1.0000 - val_loss: 0.4710 - val_acc: 0.8800\n","Epoch 350/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0102 - acc: 1.0000 - val_loss: 0.4731 - val_acc: 0.8800\n","Epoch 351/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0102 - acc: 1.0000 - val_loss: 0.4723 - val_acc: 0.8800\n","Epoch 352/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0101 - acc: 1.0000 - val_loss: 0.4722 - val_acc: 0.8800\n","Epoch 353/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0101 - acc: 1.0000 - val_loss: 0.4719 - val_acc: 0.8800\n","Epoch 354/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0100 - acc: 1.0000 - val_loss: 0.4711 - val_acc: 0.8800\n","Epoch 355/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0100 - acc: 1.0000 - val_loss: 0.4723 - val_acc: 0.8800\n","Epoch 356/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0099 - acc: 1.0000 - val_loss: 0.4722 - val_acc: 0.8800\n","Epoch 357/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0099 - acc: 1.0000 - val_loss: 0.4732 - val_acc: 0.8800\n","Epoch 358/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0098 - acc: 1.0000 - val_loss: 0.4735 - val_acc: 0.8800\n","Epoch 359/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0098 - acc: 1.0000 - val_loss: 0.4734 - val_acc: 0.8800\n","Epoch 360/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0097 - acc: 1.0000 - val_loss: 0.4747 - val_acc: 0.8800\n","Epoch 361/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0097 - acc: 1.0000 - val_loss: 0.4755 - val_acc: 0.8800\n","Epoch 362/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0096 - acc: 1.0000 - val_loss: 0.4755 - val_acc: 0.8800\n","Epoch 363/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0096 - acc: 1.0000 - val_loss: 0.4759 - val_acc: 0.8800\n","Epoch 364/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0095 - acc: 1.0000 - val_loss: 0.4753 - val_acc: 0.8800\n","Epoch 365/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0095 - acc: 1.0000 - val_loss: 0.4747 - val_acc: 0.8800\n","Epoch 366/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0095 - acc: 1.0000 - val_loss: 0.4741 - val_acc: 0.8800\n","Epoch 367/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0094 - acc: 1.0000 - val_loss: 0.4763 - val_acc: 0.8800\n","Epoch 368/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0094 - acc: 1.0000 - val_loss: 0.4762 - val_acc: 0.8800\n","Epoch 369/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0093 - acc: 1.0000 - val_loss: 0.4760 - val_acc: 0.8800\n","Epoch 370/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0093 - acc: 1.0000 - val_loss: 0.4770 - val_acc: 0.8800\n","Epoch 371/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0092 - acc: 1.0000 - val_loss: 0.4766 - val_acc: 0.8800\n","Epoch 372/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0092 - acc: 1.0000 - val_loss: 0.4765 - val_acc: 0.8800\n","Epoch 373/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0091 - acc: 1.0000 - val_loss: 0.4758 - val_acc: 0.8800\n","Epoch 374/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0091 - acc: 1.0000 - val_loss: 0.4773 - val_acc: 0.8800\n","Epoch 375/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0091 - acc: 1.0000 - val_loss: 0.4764 - val_acc: 0.8800\n","Epoch 376/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0090 - acc: 1.0000 - val_loss: 0.4766 - val_acc: 0.8800\n","Epoch 377/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0090 - acc: 1.0000 - val_loss: 0.4771 - val_acc: 0.8800\n","Epoch 378/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0089 - acc: 1.0000 - val_loss: 0.4768 - val_acc: 0.8800\n","Epoch 379/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0089 - acc: 1.0000 - val_loss: 0.4781 - val_acc: 0.8800\n","Epoch 380/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0089 - acc: 1.0000 - val_loss: 0.4780 - val_acc: 0.8800\n","Epoch 381/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0088 - acc: 1.0000 - val_loss: 0.4778 - val_acc: 0.8800\n","Epoch 382/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0088 - acc: 1.0000 - val_loss: 0.4786 - val_acc: 0.8800\n","Epoch 383/1000\n","700/700 [==============================] - 0s 84us/step - loss: 0.0087 - acc: 1.0000 - val_loss: 0.4792 - val_acc: 0.8800\n","Epoch 384/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0087 - acc: 1.0000 - val_loss: 0.4793 - val_acc: 0.8800\n","Epoch 385/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0087 - acc: 1.0000 - val_loss: 0.4798 - val_acc: 0.8800\n","Epoch 386/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0086 - acc: 1.0000 - val_loss: 0.4795 - val_acc: 0.8800\n","Epoch 387/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0086 - acc: 1.0000 - val_loss: 0.4795 - val_acc: 0.8800\n","Epoch 388/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0085 - acc: 1.0000 - val_loss: 0.4804 - val_acc: 0.8767\n","Epoch 389/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0085 - acc: 1.0000 - val_loss: 0.4812 - val_acc: 0.8767\n","Epoch 390/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0085 - acc: 1.0000 - val_loss: 0.4805 - val_acc: 0.8767\n","Epoch 391/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0084 - acc: 1.0000 - val_loss: 0.4799 - val_acc: 0.8767\n","Epoch 392/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0084 - acc: 1.0000 - val_loss: 0.4807 - val_acc: 0.8767\n","Epoch 393/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0084 - acc: 1.0000 - val_loss: 0.4811 - val_acc: 0.8800\n","Epoch 394/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0083 - acc: 1.0000 - val_loss: 0.4812 - val_acc: 0.8800\n","Epoch 395/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0083 - acc: 1.0000 - val_loss: 0.4809 - val_acc: 0.8800\n","Epoch 396/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0083 - acc: 1.0000 - val_loss: 0.4811 - val_acc: 0.8767\n","Epoch 397/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0082 - acc: 1.0000 - val_loss: 0.4814 - val_acc: 0.8767\n","Epoch 398/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0082 - acc: 1.0000 - val_loss: 0.4810 - val_acc: 0.8767\n","Epoch 399/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0082 - acc: 1.0000 - val_loss: 0.4808 - val_acc: 0.8767\n","Epoch 400/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0081 - acc: 1.0000 - val_loss: 0.4810 - val_acc: 0.8800\n","Epoch 401/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0081 - acc: 1.0000 - val_loss: 0.4819 - val_acc: 0.8767\n","Epoch 402/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0080 - acc: 1.0000 - val_loss: 0.4814 - val_acc: 0.8800\n","Epoch 403/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0080 - acc: 1.0000 - val_loss: 0.4816 - val_acc: 0.8767\n","Epoch 404/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0080 - acc: 1.0000 - val_loss: 0.4827 - val_acc: 0.8767\n","Epoch 405/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0080 - acc: 1.0000 - val_loss: 0.4819 - val_acc: 0.8800\n","Epoch 406/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0079 - acc: 1.0000 - val_loss: 0.4826 - val_acc: 0.8767\n","Epoch 407/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0079 - acc: 1.0000 - val_loss: 0.4829 - val_acc: 0.8767\n","Epoch 408/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0079 - acc: 1.0000 - val_loss: 0.4836 - val_acc: 0.8767\n","Epoch 409/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0078 - acc: 1.0000 - val_loss: 0.4835 - val_acc: 0.8767\n","Epoch 410/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0078 - acc: 1.0000 - val_loss: 0.4838 - val_acc: 0.8767\n","Epoch 411/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0078 - acc: 1.0000 - val_loss: 0.4849 - val_acc: 0.8767\n","Epoch 412/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0077 - acc: 1.0000 - val_loss: 0.4845 - val_acc: 0.8767\n","Epoch 413/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0077 - acc: 1.0000 - val_loss: 0.4846 - val_acc: 0.8767\n","Epoch 414/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0077 - acc: 1.0000 - val_loss: 0.4849 - val_acc: 0.8767\n","Epoch 415/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0076 - acc: 1.0000 - val_loss: 0.4843 - val_acc: 0.8767\n","Epoch 416/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0076 - acc: 1.0000 - val_loss: 0.4847 - val_acc: 0.8767\n","Epoch 417/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0076 - acc: 1.0000 - val_loss: 0.4843 - val_acc: 0.8800\n","Epoch 418/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0076 - acc: 1.0000 - val_loss: 0.4852 - val_acc: 0.8767\n","Epoch 419/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0075 - acc: 1.0000 - val_loss: 0.4851 - val_acc: 0.8767\n","Epoch 420/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0075 - acc: 1.0000 - val_loss: 0.4858 - val_acc: 0.8767\n","Epoch 421/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0075 - acc: 1.0000 - val_loss: 0.4858 - val_acc: 0.8767\n","Epoch 422/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0074 - acc: 1.0000 - val_loss: 0.4865 - val_acc: 0.8767\n","Epoch 423/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0074 - acc: 1.0000 - val_loss: 0.4859 - val_acc: 0.8767\n","Epoch 424/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0074 - acc: 1.0000 - val_loss: 0.4870 - val_acc: 0.8767\n","Epoch 425/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0073 - acc: 1.0000 - val_loss: 0.4865 - val_acc: 0.8767\n","Epoch 426/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0073 - acc: 1.0000 - val_loss: 0.4861 - val_acc: 0.8767\n","Epoch 427/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0073 - acc: 1.0000 - val_loss: 0.4867 - val_acc: 0.8767\n","Epoch 428/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0073 - acc: 1.0000 - val_loss: 0.4871 - val_acc: 0.8767\n","Epoch 429/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0072 - acc: 1.0000 - val_loss: 0.4865 - val_acc: 0.8800\n","Epoch 430/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0072 - acc: 1.0000 - val_loss: 0.4866 - val_acc: 0.8767\n","Epoch 431/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0072 - acc: 1.0000 - val_loss: 0.4882 - val_acc: 0.8767\n","Epoch 432/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0071 - acc: 1.0000 - val_loss: 0.4882 - val_acc: 0.8767\n","Epoch 433/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0071 - acc: 1.0000 - val_loss: 0.4884 - val_acc: 0.8767\n","Epoch 434/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0071 - acc: 1.0000 - val_loss: 0.4888 - val_acc: 0.8767\n","Epoch 435/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0071 - acc: 1.0000 - val_loss: 0.4881 - val_acc: 0.8767\n","Epoch 436/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0070 - acc: 1.0000 - val_loss: 0.4886 - val_acc: 0.8767\n","Epoch 437/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0070 - acc: 1.0000 - val_loss: 0.4889 - val_acc: 0.8767\n","Epoch 438/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0070 - acc: 1.0000 - val_loss: 0.4885 - val_acc: 0.8800\n","Epoch 439/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0070 - acc: 1.0000 - val_loss: 0.4891 - val_acc: 0.8767\n","Epoch 440/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0069 - acc: 1.0000 - val_loss: 0.4886 - val_acc: 0.8800\n","Epoch 441/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0069 - acc: 1.0000 - val_loss: 0.4891 - val_acc: 0.8767\n","Epoch 442/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0069 - acc: 1.0000 - val_loss: 0.4892 - val_acc: 0.8800\n","Epoch 443/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0069 - acc: 1.0000 - val_loss: 0.4898 - val_acc: 0.8767\n","Epoch 444/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0068 - acc: 1.0000 - val_loss: 0.4899 - val_acc: 0.8767\n","Epoch 445/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0068 - acc: 1.0000 - val_loss: 0.4890 - val_acc: 0.8800\n","Epoch 446/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0068 - acc: 1.0000 - val_loss: 0.4890 - val_acc: 0.8800\n","Epoch 447/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0068 - acc: 1.0000 - val_loss: 0.4900 - val_acc: 0.8800\n","Epoch 448/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0067 - acc: 1.0000 - val_loss: 0.4899 - val_acc: 0.8800\n","Epoch 449/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0067 - acc: 1.0000 - val_loss: 0.4900 - val_acc: 0.8800\n","Epoch 450/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0067 - acc: 1.0000 - val_loss: 0.4908 - val_acc: 0.8767\n","Epoch 451/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0067 - acc: 1.0000 - val_loss: 0.4904 - val_acc: 0.8800\n","Epoch 452/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0066 - acc: 1.0000 - val_loss: 0.4910 - val_acc: 0.8767\n","Epoch 453/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0066 - acc: 1.0000 - val_loss: 0.4911 - val_acc: 0.8767\n","Epoch 454/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0066 - acc: 1.0000 - val_loss: 0.4915 - val_acc: 0.8767\n","Epoch 455/1000\n","700/700 [==============================] - 0s 85us/step - loss: 0.0066 - acc: 1.0000 - val_loss: 0.4916 - val_acc: 0.8767\n","Epoch 456/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0065 - acc: 1.0000 - val_loss: 0.4913 - val_acc: 0.8767\n","Epoch 457/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0065 - acc: 1.0000 - val_loss: 0.4910 - val_acc: 0.8800\n","Epoch 458/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0065 - acc: 1.0000 - val_loss: 0.4916 - val_acc: 0.8767\n","Epoch 459/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0065 - acc: 1.0000 - val_loss: 0.4924 - val_acc: 0.8767\n","Epoch 460/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0064 - acc: 1.0000 - val_loss: 0.4927 - val_acc: 0.8767\n","Epoch 461/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0064 - acc: 1.0000 - val_loss: 0.4929 - val_acc: 0.8767\n","Epoch 462/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0064 - acc: 1.0000 - val_loss: 0.4934 - val_acc: 0.8767\n","Epoch 463/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0064 - acc: 1.0000 - val_loss: 0.4932 - val_acc: 0.8800\n","Epoch 464/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0064 - acc: 1.0000 - val_loss: 0.4931 - val_acc: 0.8800\n","Epoch 465/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0063 - acc: 1.0000 - val_loss: 0.4929 - val_acc: 0.8800\n","Epoch 466/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0063 - acc: 1.0000 - val_loss: 0.4931 - val_acc: 0.8800\n","Epoch 467/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0063 - acc: 1.0000 - val_loss: 0.4939 - val_acc: 0.8767\n","Epoch 468/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0063 - acc: 1.0000 - val_loss: 0.4939 - val_acc: 0.8767\n","Epoch 469/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0062 - acc: 1.0000 - val_loss: 0.4941 - val_acc: 0.8767\n","Epoch 470/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0062 - acc: 1.0000 - val_loss: 0.4942 - val_acc: 0.8767\n","Epoch 471/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0062 - acc: 1.0000 - val_loss: 0.4947 - val_acc: 0.8767\n","Epoch 472/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0062 - acc: 1.0000 - val_loss: 0.4948 - val_acc: 0.8767\n","Epoch 473/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0062 - acc: 1.0000 - val_loss: 0.4943 - val_acc: 0.8800\n","Epoch 474/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0061 - acc: 1.0000 - val_loss: 0.4946 - val_acc: 0.8800\n","Epoch 475/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0061 - acc: 1.0000 - val_loss: 0.4953 - val_acc: 0.8767\n","Epoch 476/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0061 - acc: 1.0000 - val_loss: 0.4955 - val_acc: 0.8767\n","Epoch 477/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0061 - acc: 1.0000 - val_loss: 0.4954 - val_acc: 0.8767\n","Epoch 478/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0061 - acc: 1.0000 - val_loss: 0.4963 - val_acc: 0.8767\n","Epoch 479/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0060 - acc: 1.0000 - val_loss: 0.4958 - val_acc: 0.8767\n","Epoch 480/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0060 - acc: 1.0000 - val_loss: 0.4965 - val_acc: 0.8767\n","Epoch 481/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0060 - acc: 1.0000 - val_loss: 0.4962 - val_acc: 0.8767\n","Epoch 482/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0060 - acc: 1.0000 - val_loss: 0.4961 - val_acc: 0.8767\n","Epoch 483/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0060 - acc: 1.0000 - val_loss: 0.4964 - val_acc: 0.8767\n","Epoch 484/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0059 - acc: 1.0000 - val_loss: 0.4962 - val_acc: 0.8800\n","Epoch 485/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0059 - acc: 1.0000 - val_loss: 0.4970 - val_acc: 0.8767\n","Epoch 486/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0059 - acc: 1.0000 - val_loss: 0.4968 - val_acc: 0.8800\n","Epoch 487/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0059 - acc: 1.0000 - val_loss: 0.4969 - val_acc: 0.8800\n","Epoch 488/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0059 - acc: 1.0000 - val_loss: 0.4972 - val_acc: 0.8800\n","Epoch 489/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0058 - acc: 1.0000 - val_loss: 0.4968 - val_acc: 0.8800\n","Epoch 490/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0058 - acc: 1.0000 - val_loss: 0.4973 - val_acc: 0.8800\n","Epoch 491/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0058 - acc: 1.0000 - val_loss: 0.4974 - val_acc: 0.8800\n","Epoch 492/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0058 - acc: 1.0000 - val_loss: 0.4978 - val_acc: 0.8767\n","Epoch 493/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0058 - acc: 1.0000 - val_loss: 0.4977 - val_acc: 0.8800\n","Epoch 494/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0057 - acc: 1.0000 - val_loss: 0.4983 - val_acc: 0.8767\n","Epoch 495/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0057 - acc: 1.0000 - val_loss: 0.4983 - val_acc: 0.8767\n","Epoch 496/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0057 - acc: 1.0000 - val_loss: 0.4990 - val_acc: 0.8767\n","Epoch 497/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0057 - acc: 1.0000 - val_loss: 0.4984 - val_acc: 0.8800\n","Epoch 498/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0057 - acc: 1.0000 - val_loss: 0.4988 - val_acc: 0.8767\n","Epoch 499/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0057 - acc: 1.0000 - val_loss: 0.4993 - val_acc: 0.8767\n","Epoch 500/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0056 - acc: 1.0000 - val_loss: 0.4993 - val_acc: 0.8800\n","Epoch 501/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0056 - acc: 1.0000 - val_loss: 0.4996 - val_acc: 0.8767\n","Epoch 502/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0056 - acc: 1.0000 - val_loss: 0.4996 - val_acc: 0.8800\n","Epoch 503/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0056 - acc: 1.0000 - val_loss: 0.4996 - val_acc: 0.8800\n","Epoch 504/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0056 - acc: 1.0000 - val_loss: 0.5002 - val_acc: 0.8767\n","Epoch 505/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0055 - acc: 1.0000 - val_loss: 0.5002 - val_acc: 0.8800\n","Epoch 506/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0055 - acc: 1.0000 - val_loss: 0.4999 - val_acc: 0.8800\n","Epoch 507/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0055 - acc: 1.0000 - val_loss: 0.5003 - val_acc: 0.8800\n","Epoch 508/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0055 - acc: 1.0000 - val_loss: 0.5001 - val_acc: 0.8800\n","Epoch 509/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0055 - acc: 1.0000 - val_loss: 0.5007 - val_acc: 0.8767\n","Epoch 510/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0055 - acc: 1.0000 - val_loss: 0.5006 - val_acc: 0.8767\n","Epoch 511/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0054 - acc: 1.0000 - val_loss: 0.5006 - val_acc: 0.8800\n","Epoch 512/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0054 - acc: 1.0000 - val_loss: 0.5002 - val_acc: 0.8800\n","Epoch 513/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0054 - acc: 1.0000 - val_loss: 0.5003 - val_acc: 0.8800\n","Epoch 514/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0054 - acc: 1.0000 - val_loss: 0.5006 - val_acc: 0.8800\n","Epoch 515/1000\n","700/700 [==============================] - 0s 82us/step - loss: 0.0054 - acc: 1.0000 - val_loss: 0.5004 - val_acc: 0.8800\n","Epoch 516/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0054 - acc: 1.0000 - val_loss: 0.5017 - val_acc: 0.8767\n","Epoch 517/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.5012 - val_acc: 0.8800\n","Epoch 518/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.5016 - val_acc: 0.8800\n","Epoch 519/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.5021 - val_acc: 0.8767\n","Epoch 520/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.5027 - val_acc: 0.8767\n","Epoch 521/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.5028 - val_acc: 0.8767\n","Epoch 522/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.5028 - val_acc: 0.8767\n","Epoch 523/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0052 - acc: 1.0000 - val_loss: 0.5027 - val_acc: 0.8800\n","Epoch 524/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0052 - acc: 1.0000 - val_loss: 0.5027 - val_acc: 0.8800\n","Epoch 525/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0052 - acc: 1.0000 - val_loss: 0.5029 - val_acc: 0.8800\n","Epoch 526/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0052 - acc: 1.0000 - val_loss: 0.5032 - val_acc: 0.8767\n","Epoch 527/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0052 - acc: 1.0000 - val_loss: 0.5031 - val_acc: 0.8800\n","Epoch 528/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0052 - acc: 1.0000 - val_loss: 0.5036 - val_acc: 0.8767\n","Epoch 529/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0052 - acc: 1.0000 - val_loss: 0.5033 - val_acc: 0.8800\n","Epoch 530/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0051 - acc: 1.0000 - val_loss: 0.5029 - val_acc: 0.8800\n","Epoch 531/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0051 - acc: 1.0000 - val_loss: 0.5037 - val_acc: 0.8800\n","Epoch 532/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0051 - acc: 1.0000 - val_loss: 0.5040 - val_acc: 0.8800\n","Epoch 533/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0051 - acc: 1.0000 - val_loss: 0.5039 - val_acc: 0.8800\n","Epoch 534/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0051 - acc: 1.0000 - val_loss: 0.5035 - val_acc: 0.8800\n","Epoch 535/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0051 - acc: 1.0000 - val_loss: 0.5040 - val_acc: 0.8800\n","Epoch 536/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5046 - val_acc: 0.8767\n","Epoch 537/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5045 - val_acc: 0.8800\n","Epoch 538/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5046 - val_acc: 0.8800\n","Epoch 539/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5049 - val_acc: 0.8800\n","Epoch 540/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5048 - val_acc: 0.8800\n","Epoch 541/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5049 - val_acc: 0.8800\n","Epoch 542/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5049 - val_acc: 0.8800\n","Epoch 543/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.5055 - val_acc: 0.8800\n","Epoch 544/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.5053 - val_acc: 0.8800\n","Epoch 545/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.5057 - val_acc: 0.8800\n","Epoch 546/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.5058 - val_acc: 0.8800\n","Epoch 547/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.5061 - val_acc: 0.8800\n","Epoch 548/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.5057 - val_acc: 0.8800\n","Epoch 549/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.5062 - val_acc: 0.8800\n","Epoch 550/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0048 - acc: 1.0000 - val_loss: 0.5064 - val_acc: 0.8800\n","Epoch 551/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0048 - acc: 1.0000 - val_loss: 0.5067 - val_acc: 0.8800\n","Epoch 552/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0048 - acc: 1.0000 - val_loss: 0.5066 - val_acc: 0.8800\n","Epoch 553/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0048 - acc: 1.0000 - val_loss: 0.5067 - val_acc: 0.8800\n","Epoch 554/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0048 - acc: 1.0000 - val_loss: 0.5068 - val_acc: 0.8800\n","Epoch 555/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0048 - acc: 1.0000 - val_loss: 0.5073 - val_acc: 0.8800\n","Epoch 556/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0048 - acc: 1.0000 - val_loss: 0.5071 - val_acc: 0.8800\n","Epoch 557/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5070 - val_acc: 0.8833\n","Epoch 558/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5072 - val_acc: 0.8833\n","Epoch 559/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5075 - val_acc: 0.8833\n","Epoch 560/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5079 - val_acc: 0.8800\n","Epoch 561/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5082 - val_acc: 0.8800\n","Epoch 562/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5083 - val_acc: 0.8800\n","Epoch 563/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5077 - val_acc: 0.8833\n","Epoch 564/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0047 - acc: 1.0000 - val_loss: 0.5079 - val_acc: 0.8833\n","Epoch 565/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5077 - val_acc: 0.8833\n","Epoch 566/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5080 - val_acc: 0.8833\n","Epoch 567/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5083 - val_acc: 0.8833\n","Epoch 568/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5091 - val_acc: 0.8833\n","Epoch 569/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5088 - val_acc: 0.8833\n","Epoch 570/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5090 - val_acc: 0.8833\n","Epoch 571/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5092 - val_acc: 0.8833\n","Epoch 572/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.5094 - val_acc: 0.8833\n","Epoch 573/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5099 - val_acc: 0.8833\n","Epoch 574/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5100 - val_acc: 0.8833\n","Epoch 575/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5100 - val_acc: 0.8833\n","Epoch 576/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5098 - val_acc: 0.8833\n","Epoch 577/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5097 - val_acc: 0.8833\n","Epoch 578/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5100 - val_acc: 0.8833\n","Epoch 579/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5103 - val_acc: 0.8833\n","Epoch 580/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0045 - acc: 1.0000 - val_loss: 0.5107 - val_acc: 0.8833\n","Epoch 581/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5106 - val_acc: 0.8833\n","Epoch 582/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5107 - val_acc: 0.8833\n","Epoch 583/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5112 - val_acc: 0.8833\n","Epoch 584/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5108 - val_acc: 0.8833\n","Epoch 585/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5108 - val_acc: 0.8833\n","Epoch 586/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5111 - val_acc: 0.8833\n","Epoch 587/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5113 - val_acc: 0.8833\n","Epoch 588/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5116 - val_acc: 0.8833\n","Epoch 589/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.5113 - val_acc: 0.8833\n","Epoch 590/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5119 - val_acc: 0.8833\n","Epoch 591/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5119 - val_acc: 0.8833\n","Epoch 592/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5122 - val_acc: 0.8833\n","Epoch 593/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5121 - val_acc: 0.8833\n","Epoch 594/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5120 - val_acc: 0.8833\n","Epoch 595/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5123 - val_acc: 0.8833\n","Epoch 596/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5127 - val_acc: 0.8833\n","Epoch 597/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5124 - val_acc: 0.8833\n","Epoch 598/1000\n","700/700 [==============================] - 0s 86us/step - loss: 0.0043 - acc: 1.0000 - val_loss: 0.5128 - val_acc: 0.8833\n","Epoch 599/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5126 - val_acc: 0.8833\n","Epoch 600/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5127 - val_acc: 0.8833\n","Epoch 601/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5129 - val_acc: 0.8833\n","Epoch 602/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5130 - val_acc: 0.8833\n","Epoch 603/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5129 - val_acc: 0.8833\n","Epoch 604/1000\n","700/700 [==============================] - 0s 87us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5127 - val_acc: 0.8833\n","Epoch 605/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5128 - val_acc: 0.8833\n","Epoch 606/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5129 - val_acc: 0.8833\n","Epoch 607/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0042 - acc: 1.0000 - val_loss: 0.5130 - val_acc: 0.8833\n","Epoch 608/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5133 - val_acc: 0.8833\n","Epoch 609/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5140 - val_acc: 0.8833\n","Epoch 610/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5140 - val_acc: 0.8833\n","Epoch 611/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5144 - val_acc: 0.8833\n","Epoch 612/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5142 - val_acc: 0.8833\n","Epoch 613/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5140 - val_acc: 0.8833\n","Epoch 614/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5144 - val_acc: 0.8833\n","Epoch 615/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5151 - val_acc: 0.8833\n","Epoch 616/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5149 - val_acc: 0.8833\n","Epoch 617/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0041 - acc: 1.0000 - val_loss: 0.5151 - val_acc: 0.8833\n","Epoch 618/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5152 - val_acc: 0.8833\n","Epoch 619/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5155 - val_acc: 0.8833\n","Epoch 620/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5156 - val_acc: 0.8833\n","Epoch 621/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5156 - val_acc: 0.8833\n","Epoch 622/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5154 - val_acc: 0.8833\n","Epoch 623/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5157 - val_acc: 0.8833\n","Epoch 624/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5157 - val_acc: 0.8833\n","Epoch 625/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5158 - val_acc: 0.8833\n","Epoch 626/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5160 - val_acc: 0.8833\n","Epoch 627/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5165 - val_acc: 0.8833\n","Epoch 628/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5165 - val_acc: 0.8833\n","Epoch 629/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5164 - val_acc: 0.8833\n","Epoch 630/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5164 - val_acc: 0.8833\n","Epoch 631/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5166 - val_acc: 0.8833\n","Epoch 632/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5167 - val_acc: 0.8833\n","Epoch 633/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5167 - val_acc: 0.8833\n","Epoch 634/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5170 - val_acc: 0.8833\n","Epoch 635/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5173 - val_acc: 0.8833\n","Epoch 636/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5174 - val_acc: 0.8833\n","Epoch 637/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5175 - val_acc: 0.8833\n","Epoch 638/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0039 - acc: 1.0000 - val_loss: 0.5177 - val_acc: 0.8833\n","Epoch 639/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5175 - val_acc: 0.8833\n","Epoch 640/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5176 - val_acc: 0.8833\n","Epoch 641/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5176 - val_acc: 0.8833\n","Epoch 642/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5180 - val_acc: 0.8833\n","Epoch 643/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5182 - val_acc: 0.8833\n","Epoch 644/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5181 - val_acc: 0.8833\n","Epoch 645/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5179 - val_acc: 0.8833\n","Epoch 646/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5185 - val_acc: 0.8833\n","Epoch 647/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5188 - val_acc: 0.8833\n","Epoch 648/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5189 - val_acc: 0.8833\n","Epoch 649/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.5191 - val_acc: 0.8833\n","Epoch 650/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5192 - val_acc: 0.8833\n","Epoch 651/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5194 - val_acc: 0.8833\n","Epoch 652/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5192 - val_acc: 0.8833\n","Epoch 653/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5190 - val_acc: 0.8833\n","Epoch 654/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5194 - val_acc: 0.8833\n","Epoch 655/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5193 - val_acc: 0.8833\n","Epoch 656/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5198 - val_acc: 0.8833\n","Epoch 657/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5202 - val_acc: 0.8833\n","Epoch 658/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5200 - val_acc: 0.8833\n","Epoch 659/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5204 - val_acc: 0.8833\n","Epoch 660/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5203 - val_acc: 0.8833\n","Epoch 661/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.5202 - val_acc: 0.8833\n","Epoch 662/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5204 - val_acc: 0.8833\n","Epoch 663/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5207 - val_acc: 0.8833\n","Epoch 664/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5211 - val_acc: 0.8833\n","Epoch 665/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5212 - val_acc: 0.8833\n","Epoch 666/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5217 - val_acc: 0.8833\n","Epoch 667/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5218 - val_acc: 0.8867\n","Epoch 668/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5215 - val_acc: 0.8833\n","Epoch 669/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5218 - val_acc: 0.8833\n","Epoch 670/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5216 - val_acc: 0.8833\n","Epoch 671/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5217 - val_acc: 0.8833\n","Epoch 672/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5214 - val_acc: 0.8833\n","Epoch 673/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0036 - acc: 1.0000 - val_loss: 0.5217 - val_acc: 0.8833\n","Epoch 674/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5219 - val_acc: 0.8833\n","Epoch 675/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5221 - val_acc: 0.8833\n","Epoch 676/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5221 - val_acc: 0.8833\n","Epoch 677/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5223 - val_acc: 0.8867\n","Epoch 678/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5221 - val_acc: 0.8833\n","Epoch 679/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5219 - val_acc: 0.8833\n","Epoch 680/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5221 - val_acc: 0.8833\n","Epoch 681/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5228 - val_acc: 0.8867\n","Epoch 682/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5232 - val_acc: 0.8867\n","Epoch 683/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5232 - val_acc: 0.8867\n","Epoch 684/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5231 - val_acc: 0.8833\n","Epoch 685/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5229 - val_acc: 0.8833\n","Epoch 686/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0035 - acc: 1.0000 - val_loss: 0.5230 - val_acc: 0.8833\n","Epoch 687/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5230 - val_acc: 0.8833\n","Epoch 688/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5232 - val_acc: 0.8867\n","Epoch 689/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5232 - val_acc: 0.8833\n","Epoch 690/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5234 - val_acc: 0.8833\n","Epoch 691/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5239 - val_acc: 0.8867\n","Epoch 692/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5238 - val_acc: 0.8833\n","Epoch 693/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5241 - val_acc: 0.8833\n","Epoch 694/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5240 - val_acc: 0.8833\n","Epoch 695/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5242 - val_acc: 0.8833\n","Epoch 696/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5241 - val_acc: 0.8833\n","Epoch 697/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5244 - val_acc: 0.8833\n","Epoch 698/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5245 - val_acc: 0.8867\n","Epoch 699/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5247 - val_acc: 0.8867\n","Epoch 700/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.5245 - val_acc: 0.8833\n","Epoch 701/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5245 - val_acc: 0.8833\n","Epoch 702/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5247 - val_acc: 0.8833\n","Epoch 703/1000\n","700/700 [==============================] - 0s 85us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5249 - val_acc: 0.8833\n","Epoch 704/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5250 - val_acc: 0.8867\n","Epoch 705/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5253 - val_acc: 0.8867\n","Epoch 706/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5255 - val_acc: 0.8867\n","Epoch 707/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5253 - val_acc: 0.8867\n","Epoch 708/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5256 - val_acc: 0.8867\n","Epoch 709/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5255 - val_acc: 0.8867\n","Epoch 710/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5253 - val_acc: 0.8867\n","Epoch 711/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5256 - val_acc: 0.8867\n","Epoch 712/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5259 - val_acc: 0.8867\n","Epoch 713/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5262 - val_acc: 0.8867\n","Epoch 714/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0033 - acc: 1.0000 - val_loss: 0.5262 - val_acc: 0.8867\n","Epoch 715/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5262 - val_acc: 0.8867\n","Epoch 716/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5264 - val_acc: 0.8867\n","Epoch 717/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5265 - val_acc: 0.8867\n","Epoch 718/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5265 - val_acc: 0.8867\n","Epoch 719/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5266 - val_acc: 0.8867\n","Epoch 720/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5268 - val_acc: 0.8867\n","Epoch 721/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5268 - val_acc: 0.8867\n","Epoch 722/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5269 - val_acc: 0.8867\n","Epoch 723/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5268 - val_acc: 0.8867\n","Epoch 724/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5267 - val_acc: 0.8867\n","Epoch 725/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5271 - val_acc: 0.8867\n","Epoch 726/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5271 - val_acc: 0.8867\n","Epoch 727/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5271 - val_acc: 0.8867\n","Epoch 728/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5273 - val_acc: 0.8867\n","Epoch 729/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.5275 - val_acc: 0.8867\n","Epoch 730/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5278 - val_acc: 0.8867\n","Epoch 731/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5281 - val_acc: 0.8867\n","Epoch 732/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5279 - val_acc: 0.8867\n","Epoch 733/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5281 - val_acc: 0.8867\n","Epoch 734/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5284 - val_acc: 0.8867\n","Epoch 735/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5285 - val_acc: 0.8867\n","Epoch 736/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5282 - val_acc: 0.8867\n","Epoch 737/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5283 - val_acc: 0.8867\n","Epoch 738/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5285 - val_acc: 0.8867\n","Epoch 739/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5290 - val_acc: 0.8867\n","Epoch 740/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5288 - val_acc: 0.8867\n","Epoch 741/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5289 - val_acc: 0.8867\n","Epoch 742/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5292 - val_acc: 0.8867\n","Epoch 743/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5293 - val_acc: 0.8867\n","Epoch 744/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5291 - val_acc: 0.8867\n","Epoch 745/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.5292 - val_acc: 0.8867\n","Epoch 746/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5293 - val_acc: 0.8867\n","Epoch 747/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5297 - val_acc: 0.8867\n","Epoch 748/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5297 - val_acc: 0.8867\n","Epoch 749/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5298 - val_acc: 0.8867\n","Epoch 750/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5298 - val_acc: 0.8867\n","Epoch 751/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5300 - val_acc: 0.8867\n","Epoch 752/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5298 - val_acc: 0.8867\n","Epoch 753/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5301 - val_acc: 0.8867\n","Epoch 754/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5304 - val_acc: 0.8867\n","Epoch 755/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5305 - val_acc: 0.8867\n","Epoch 756/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5304 - val_acc: 0.8867\n","Epoch 757/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5304 - val_acc: 0.8867\n","Epoch 758/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5307 - val_acc: 0.8867\n","Epoch 759/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5304 - val_acc: 0.8867\n","Epoch 760/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5307 - val_acc: 0.8867\n","Epoch 761/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5311 - val_acc: 0.8867\n","Epoch 762/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.5314 - val_acc: 0.8867\n","Epoch 763/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5315 - val_acc: 0.8867\n","Epoch 764/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5314 - val_acc: 0.8867\n","Epoch 765/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5314 - val_acc: 0.8867\n","Epoch 766/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5316 - val_acc: 0.8867\n","Epoch 767/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5315 - val_acc: 0.8867\n","Epoch 768/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5317 - val_acc: 0.8867\n","Epoch 769/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5318 - val_acc: 0.8867\n","Epoch 770/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5317 - val_acc: 0.8867\n","Epoch 771/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5318 - val_acc: 0.8867\n","Epoch 772/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5318 - val_acc: 0.8867\n","Epoch 773/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5319 - val_acc: 0.8867\n","Epoch 774/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5318 - val_acc: 0.8867\n","Epoch 775/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5320 - val_acc: 0.8867\n","Epoch 776/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5323 - val_acc: 0.8867\n","Epoch 777/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5326 - val_acc: 0.8867\n","Epoch 778/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5323 - val_acc: 0.8867\n","Epoch 779/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5325 - val_acc: 0.8867\n","Epoch 780/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.5329 - val_acc: 0.8867\n","Epoch 781/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5332 - val_acc: 0.8867\n","Epoch 782/1000\n","700/700 [==============================] - 0s 81us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5332 - val_acc: 0.8867\n","Epoch 783/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5331 - val_acc: 0.8867\n","Epoch 784/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5335 - val_acc: 0.8867\n","Epoch 785/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5336 - val_acc: 0.8867\n","Epoch 786/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5338 - val_acc: 0.8867\n","Epoch 787/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5335 - val_acc: 0.8867\n","Epoch 788/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5335 - val_acc: 0.8867\n","Epoch 789/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5339 - val_acc: 0.8867\n","Epoch 790/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5339 - val_acc: 0.8867\n","Epoch 791/1000\n","700/700 [==============================] - 0s 82us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5340 - val_acc: 0.8867\n","Epoch 792/1000\n","700/700 [==============================] - 0s 80us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5343 - val_acc: 0.8867\n","Epoch 793/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5341 - val_acc: 0.8867\n","Epoch 794/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5342 - val_acc: 0.8867\n","Epoch 795/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5342 - val_acc: 0.8867\n","Epoch 796/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5343 - val_acc: 0.8867\n","Epoch 797/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5343 - val_acc: 0.8867\n","Epoch 798/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5345 - val_acc: 0.8867\n","Epoch 799/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.5347 - val_acc: 0.8867\n","Epoch 800/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5349 - val_acc: 0.8867\n","Epoch 801/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5347 - val_acc: 0.8867\n","Epoch 802/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5350 - val_acc: 0.8867\n","Epoch 803/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5351 - val_acc: 0.8867\n","Epoch 804/1000\n","700/700 [==============================] - 0s 83us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5352 - val_acc: 0.8867\n","Epoch 805/1000\n","700/700 [==============================] - 0s 84us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5353 - val_acc: 0.8867\n","Epoch 806/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5352 - val_acc: 0.8867\n","Epoch 807/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5354 - val_acc: 0.8867\n","Epoch 808/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5355 - val_acc: 0.8867\n","Epoch 809/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5359 - val_acc: 0.8867\n","Epoch 810/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5360 - val_acc: 0.8867\n","Epoch 811/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5359 - val_acc: 0.8867\n","Epoch 812/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5360 - val_acc: 0.8867\n","Epoch 813/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5362 - val_acc: 0.8867\n","Epoch 814/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5362 - val_acc: 0.8867\n","Epoch 815/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5362 - val_acc: 0.8867\n","Epoch 816/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5364 - val_acc: 0.8867\n","Epoch 817/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5363 - val_acc: 0.8867\n","Epoch 818/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5366 - val_acc: 0.8867\n","Epoch 819/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5367 - val_acc: 0.8867\n","Epoch 820/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5369 - val_acc: 0.8867\n","Epoch 821/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5369 - val_acc: 0.8867\n","Epoch 822/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5369 - val_acc: 0.8867\n","Epoch 823/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5368 - val_acc: 0.8867\n","Epoch 824/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5369 - val_acc: 0.8867\n","Epoch 825/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5371 - val_acc: 0.8867\n","Epoch 826/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5372 - val_acc: 0.8867\n","Epoch 827/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5373 - val_acc: 0.8867\n","Epoch 828/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5373 - val_acc: 0.8867\n","Epoch 829/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5374 - val_acc: 0.8867\n","Epoch 830/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5376 - val_acc: 0.8867\n","Epoch 831/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5375 - val_acc: 0.8867\n","Epoch 832/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5378 - val_acc: 0.8867\n","Epoch 833/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5378 - val_acc: 0.8867\n","Epoch 834/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5381 - val_acc: 0.8867\n","Epoch 835/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5381 - val_acc: 0.8867\n","Epoch 836/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5382 - val_acc: 0.8867\n","Epoch 837/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5384 - val_acc: 0.8867\n","Epoch 838/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5383 - val_acc: 0.8867\n","Epoch 839/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5387 - val_acc: 0.8867\n","Epoch 840/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5386 - val_acc: 0.8867\n","Epoch 841/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5388 - val_acc: 0.8867\n","Epoch 842/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.5389 - val_acc: 0.8867\n","Epoch 843/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5389 - val_acc: 0.8867\n","Epoch 844/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5389 - val_acc: 0.8867\n","Epoch 845/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5390 - val_acc: 0.8867\n","Epoch 846/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5390 - val_acc: 0.8867\n","Epoch 847/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5393 - val_acc: 0.8867\n","Epoch 848/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5392 - val_acc: 0.8867\n","Epoch 849/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5394 - val_acc: 0.8867\n","Epoch 850/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5393 - val_acc: 0.8867\n","Epoch 851/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5395 - val_acc: 0.8867\n","Epoch 852/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5395 - val_acc: 0.8867\n","Epoch 853/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5394 - val_acc: 0.8867\n","Epoch 854/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5399 - val_acc: 0.8867\n","Epoch 855/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5399 - val_acc: 0.8867\n","Epoch 856/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5400 - val_acc: 0.8867\n","Epoch 857/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5402 - val_acc: 0.8867\n","Epoch 858/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5401 - val_acc: 0.8867\n","Epoch 859/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5404 - val_acc: 0.8867\n","Epoch 860/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5404 - val_acc: 0.8867\n","Epoch 861/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5402 - val_acc: 0.8867\n","Epoch 862/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5400 - val_acc: 0.8867\n","Epoch 863/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5404 - val_acc: 0.8867\n","Epoch 864/1000\n","700/700 [==============================] - 0s 82us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5404 - val_acc: 0.8867\n","Epoch 865/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5405 - val_acc: 0.8867\n","Epoch 866/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.5406 - val_acc: 0.8867\n","Epoch 867/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5405 - val_acc: 0.8867\n","Epoch 868/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5409 - val_acc: 0.8867\n","Epoch 869/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5409 - val_acc: 0.8867\n","Epoch 870/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5410 - val_acc: 0.8867\n","Epoch 871/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5412 - val_acc: 0.8867\n","Epoch 872/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5411 - val_acc: 0.8867\n","Epoch 873/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5413 - val_acc: 0.8867\n","Epoch 874/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5415 - val_acc: 0.8867\n","Epoch 875/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5415 - val_acc: 0.8867\n","Epoch 876/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5416 - val_acc: 0.8867\n","Epoch 877/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5417 - val_acc: 0.8867\n","Epoch 878/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5418 - val_acc: 0.8867\n","Epoch 879/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5420 - val_acc: 0.8867\n","Epoch 880/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5422 - val_acc: 0.8867\n","Epoch 881/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5421 - val_acc: 0.8867\n","Epoch 882/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5422 - val_acc: 0.8867\n","Epoch 883/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5424 - val_acc: 0.8867\n","Epoch 884/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5422 - val_acc: 0.8867\n","Epoch 885/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5423 - val_acc: 0.8867\n","Epoch 886/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5424 - val_acc: 0.8867\n","Epoch 887/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5425 - val_acc: 0.8867\n","Epoch 888/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5426 - val_acc: 0.8867\n","Epoch 889/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5427 - val_acc: 0.8867\n","Epoch 890/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5426 - val_acc: 0.8867\n","Epoch 891/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.5427 - val_acc: 0.8867\n","Epoch 892/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5427 - val_acc: 0.8867\n","Epoch 893/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5429 - val_acc: 0.8867\n","Epoch 894/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5431 - val_acc: 0.8867\n","Epoch 895/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5431 - val_acc: 0.8867\n","Epoch 896/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5433 - val_acc: 0.8867\n","Epoch 897/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5432 - val_acc: 0.8867\n","Epoch 898/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5434 - val_acc: 0.8867\n","Epoch 899/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5437 - val_acc: 0.8867\n","Epoch 900/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5436 - val_acc: 0.8867\n","Epoch 901/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5437 - val_acc: 0.8867\n","Epoch 902/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5437 - val_acc: 0.8867\n","Epoch 903/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5439 - val_acc: 0.8867\n","Epoch 904/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5438 - val_acc: 0.8867\n","Epoch 905/1000\n","700/700 [==============================] - 0s 79us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5442 - val_acc: 0.8867\n","Epoch 906/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5441 - val_acc: 0.8867\n","Epoch 907/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5441 - val_acc: 0.8867\n","Epoch 908/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5444 - val_acc: 0.8867\n","Epoch 909/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5444 - val_acc: 0.8867\n","Epoch 910/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5444 - val_acc: 0.8867\n","Epoch 911/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5446 - val_acc: 0.8867\n","Epoch 912/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5447 - val_acc: 0.8867\n","Epoch 913/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5448 - val_acc: 0.8867\n","Epoch 914/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5448 - val_acc: 0.8867\n","Epoch 915/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5449 - val_acc: 0.8867\n","Epoch 916/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5449 - val_acc: 0.8867\n","Epoch 917/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5451 - val_acc: 0.8867\n","Epoch 918/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.5452 - val_acc: 0.8867\n","Epoch 919/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5451 - val_acc: 0.8867\n","Epoch 920/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5453 - val_acc: 0.8867\n","Epoch 921/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5455 - val_acc: 0.8867\n","Epoch 922/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5454 - val_acc: 0.8867\n","Epoch 923/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5458 - val_acc: 0.8867\n","Epoch 924/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5457 - val_acc: 0.8867\n","Epoch 925/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5458 - val_acc: 0.8867\n","Epoch 926/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5459 - val_acc: 0.8867\n","Epoch 927/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5462 - val_acc: 0.8867\n","Epoch 928/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5462 - val_acc: 0.8867\n","Epoch 929/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5464 - val_acc: 0.8867\n","Epoch 930/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5464 - val_acc: 0.8867\n","Epoch 931/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5464 - val_acc: 0.8867\n","Epoch 932/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5464 - val_acc: 0.8867\n","Epoch 933/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5464 - val_acc: 0.8867\n","Epoch 934/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5464 - val_acc: 0.8867\n","Epoch 935/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5465 - val_acc: 0.8867\n","Epoch 936/1000\n","700/700 [==============================] - 0s 77us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5467 - val_acc: 0.8867\n","Epoch 937/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5467 - val_acc: 0.8867\n","Epoch 938/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5469 - val_acc: 0.8867\n","Epoch 939/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5470 - val_acc: 0.8867\n","Epoch 940/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5470 - val_acc: 0.8867\n","Epoch 941/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5470 - val_acc: 0.8867\n","Epoch 942/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5470 - val_acc: 0.8867\n","Epoch 943/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5469 - val_acc: 0.8867\n","Epoch 944/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5472 - val_acc: 0.8867\n","Epoch 945/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5472 - val_acc: 0.8867\n","Epoch 946/1000\n","700/700 [==============================] - 0s 76us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5473 - val_acc: 0.8867\n","Epoch 947/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5474 - val_acc: 0.8867\n","Epoch 948/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5476 - val_acc: 0.8867\n","Epoch 949/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5478 - val_acc: 0.8867\n","Epoch 950/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5476 - val_acc: 0.8867\n","Epoch 951/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5479 - val_acc: 0.8867\n","Epoch 952/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5479 - val_acc: 0.8867\n","Epoch 953/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5481 - val_acc: 0.8867\n","Epoch 954/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5482 - val_acc: 0.8867\n","Epoch 955/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5483 - val_acc: 0.8867\n","Epoch 956/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5485 - val_acc: 0.8867\n","Epoch 957/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5486 - val_acc: 0.8867\n","Epoch 958/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5488 - val_acc: 0.8867\n","Epoch 959/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5487 - val_acc: 0.8867\n","Epoch 960/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5487 - val_acc: 0.8867\n","Epoch 961/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5487 - val_acc: 0.8867\n","Epoch 962/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5487 - val_acc: 0.8867\n","Epoch 963/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5486 - val_acc: 0.8867\n","Epoch 964/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5487 - val_acc: 0.8867\n","Epoch 965/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5490 - val_acc: 0.8867\n","Epoch 966/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5488 - val_acc: 0.8867\n","Epoch 967/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5490 - val_acc: 0.8867\n","Epoch 968/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5492 - val_acc: 0.8867\n","Epoch 969/1000\n","700/700 [==============================] - 0s 63us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5492 - val_acc: 0.8867\n","Epoch 970/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5492 - val_acc: 0.8867\n","Epoch 971/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5493 - val_acc: 0.8867\n","Epoch 972/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5496 - val_acc: 0.8867\n","Epoch 973/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5497 - val_acc: 0.8867\n","Epoch 974/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5499 - val_acc: 0.8867\n","Epoch 975/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5499 - val_acc: 0.8867\n","Epoch 976/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5499 - val_acc: 0.8867\n","Epoch 977/1000\n","700/700 [==============================] - 0s 65us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5500 - val_acc: 0.8867\n","Epoch 978/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5501 - val_acc: 0.8867\n","Epoch 979/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5501 - val_acc: 0.8867\n","Epoch 980/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.5501 - val_acc: 0.8867\n","Epoch 981/1000\n","700/700 [==============================] - 0s 70us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5502 - val_acc: 0.8867\n","Epoch 982/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5503 - val_acc: 0.8867\n","Epoch 983/1000\n","700/700 [==============================] - 0s 78us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5502 - val_acc: 0.8867\n","Epoch 984/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5504 - val_acc: 0.8867\n","Epoch 985/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5506 - val_acc: 0.8867\n","Epoch 986/1000\n","700/700 [==============================] - 0s 69us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5506 - val_acc: 0.8867\n","Epoch 987/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5505 - val_acc: 0.8867\n","Epoch 988/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5508 - val_acc: 0.8867\n","Epoch 989/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5508 - val_acc: 0.8867\n","Epoch 990/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5509 - val_acc: 0.8867\n","Epoch 991/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5509 - val_acc: 0.8867\n","Epoch 992/1000\n","700/700 [==============================] - 0s 71us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5510 - val_acc: 0.8867\n","Epoch 993/1000\n","700/700 [==============================] - 0s 68us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5510 - val_acc: 0.8867\n","Epoch 994/1000\n","700/700 [==============================] - 0s 64us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5509 - val_acc: 0.8867\n","Epoch 995/1000\n","700/700 [==============================] - 0s 73us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5513 - val_acc: 0.8867\n","Epoch 996/1000\n","700/700 [==============================] - 0s 75us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5513 - val_acc: 0.8867\n","Epoch 997/1000\n","700/700 [==============================] - 0s 67us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5515 - val_acc: 0.8867\n","Epoch 998/1000\n","700/700 [==============================] - 0s 66us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5517 - val_acc: 0.8900\n","Epoch 999/1000\n","700/700 [==============================] - 0s 72us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5516 - val_acc: 0.8867\n","Epoch 1000/1000\n","700/700 [==============================] - 0s 74us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.5517 - val_acc: 0.8867\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"dFn9l_5NpcKP","colab_type":"code","outputId":"eab9c9e6-e55a-40cf-f32e-eec0c619327e","executionInfo":{"status":"ok","timestamp":1569160783257,"user_tz":-540,"elapsed":1048,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}},"colab":{"base_uri":"https://localhost:8080/","height":283}},"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","\n","fig, loss_ax = plt.subplots()\n","\n","acc_ax = loss_ax.twinx()\n","\n","loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n","loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')\n","\n","acc_ax.plot(hist.history['acc'], 'b', label='train acc')\n","acc_ax.plot(hist.history['val_acc'], 'g', label = 'val acc')\n","\n","loss_ax.set_xlabel(\"epoch\")\n","loss_ax.set_ylabel('loss')\n","acc_ax.set_ylabel('accuray')\n","\n","loss_ax.legend(loc='upper left')\n","acc_ax.legend(loc='lower left')\n","\n","plt.show()"],"execution_count":23,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAacAAAEKCAYAAAC2bZqoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw+7/vPQVo8EAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"l0S2E3e4OTvA","colab_type":"text"},"source":["# 6. 모델 평가하기"]},{"cell_type":"code","metadata":{"id":"NkUsXDDBOUts","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":71},"outputId":"b4c2fa44-ec3b-49fc-9781-a269e9067207","executionInfo":{"status":"ok","timestamp":1569160787718,"user_tz":-540,"elapsed":914,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}}},"source":["loss_and_acc = model.evaluate(x_test, y_test, batch_size=32)\n","print('## evaluation ##')\n","print(loss_and_acc)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["10000/10000 [==============================] - 0s 22us/step\n","## evaluation ##\n","[0.6533987367000431, 0.8634]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"YIcw_KmOQZSz","colab_type":"text"},"source":["# 7. 모델 사용하기"]},{"cell_type":"code","metadata":{"id":"R9HzROs0OnVI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"e58e13ef-38fd-4f63-df01-69b9c49caebb","executionInfo":{"status":"ok","timestamp":1569160791789,"user_tz":-540,"elapsed":975,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}}},"source":["xhat = x_test\n","yhat = model.predict(xhat)\n","print('## yhat ##')\n","print(yhat)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["## yhat ##\n","[[3.0572395e-10 1.3347163e-12 1.2393883e-09 ... 9.9999988e-01\n","  1.7136955e-08 1.4515656e-07]\n"," [4.9815066e-05 3.8323201e-08 9.9932981e-01 ... 5.4613471e-12\n","  4.8711616e-05 3.0424149e-10]\n"," [3.2355853e-08 9.9770856e-01 4.0191549e-04 ... 1.6042695e-05\n","  1.5733842e-05 3.6245478e-06]\n"," ...\n"," [8.2371902e-13 3.0493018e-11 2.0526890e-12 ... 5.8170201e-08\n","  4.6668823e-08 1.9748870e-05]\n"," [1.4508766e-04 1.3267757e-07 8.3073601e-08 ... 4.8716810e-08\n","  2.5333495e-05 1.7353848e-09]\n"," [4.6682294e-09 1.5939696e-16 4.6280772e-07 ... 2.9117386e-18\n","  2.5280277e-13 7.5693566e-12]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2uixgRi3REOQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":612},"outputId":"2e311b41-c418-45fd-876e-3b59aa696f1c","executionInfo":{"status":"ok","timestamp":1569160834784,"user_tz":-540,"elapsed":5577,"user":{"displayName":"백서연","photoUrl":"","userId":"13608049772715071568"}}},"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","\n","i = 0\n","\n","plt.figure(figsize=(10,10))\n","for i in range(100):\n","  if np.argmax(y_test[i]) == np.argmax(yhat[i]):\n","    plt.subplot(10,10,i+1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    plt.imshow(x_test[i].reshape(28,28))\n","    plt.xlabel('R: ' + str(np.argmax(y_test[i]))+ ' P: ' + str(np.argmax(yhat[i])))\n","    i += 1\n","\n","plt.show()"],"execution_count":27,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkIAAAI/CAYAAACbG7YfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRYlZdCCkKIqiKErM8v9EivEr+WmEywAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 720x720 with 88 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"zHE-PprZRY7c","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}