{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from numpy import loadtxt\n",
"from keras.models import load_model\n",
"import tensorflow as tf\n",
"from keras import backend as K\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/userdata/kerasData\r\n"
]
}
],
"source": [
"!pwd"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def f1(y_true, y_pred):\n",
" \n",
" def recall(y_true, y_pred):\n",
" \"\"\"Recall metric.\n",
"\n",
" Only computes a batch-wise average of recall.\n",
"\n",
" Computes the recall, a metric for multi-label classification of\n",
" how many relevant items are selected.\n",
" \"\"\"\n",
" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
" possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
" recall = true_positives / (possible_positives + K.epsilon())\n",
" return recall\n",
"\n",
" def precision(y_true, y_pred):\n",
" \"\"\"Precision metric.\n",
"\n",
" Only computes a batch-wise average of precision.\n",
"\n",
" Computes the precision, a metric for multi-label classification of\n",
" how many selected items are relevant.\n",
" \"\"\"\n",
" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
" predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
" precision = true_positives / (predicted_positives + K.epsilon())\n",
" return precision\n",
" precision = precision(y_true, y_pred)\n",
" recall = recall(y_true, y_pred)\n",
" return 2*((precision*recall)/(precision+recall+K.epsilon()))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 2781 images belonging to 2 classes.\n"
]
}
],
"source": [
"INIT_LR = 1e-4\n",
"batch_size = 16\n",
"NUM_EPOCHS = 200\n",
"\n",
"image_size=(128, 128)\n",
"class_mode=\"binary\"\n",
"image_generator = tf.keras.preprocessing.image.ImageDataGenerator()\n",
"TestGenerator = image_generator.flow_from_directory(\n",
" \"/userdata/kerasData/preloaded/flowDirectory/validation\",\n",
" target_size=image_size,\n",
" batch_size=batch_size,\n",
" seed=1000,\n",
" class_mode=class_mode)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"separable_conv2d (SeparableC (None, 128, 128, 16) 211 \n",
"_________________________________________________________________\n",
"activation (Activation) (None, 128, 128, 16) 0 \n",
"_________________________________________________________________\n",
"batch_normalization (BatchNo (None, 128, 128, 16) 64 \n",
"_________________________________________________________________\n",
"max_pooling2d (MaxPooling2D) (None, 64, 64, 16) 0 \n",
"_________________________________________________________________\n",
"separable_conv2d_1 (Separabl (None, 64, 64, 32) 688 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 64, 64, 32) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_1 (Batch (None, 64, 64, 32) 128 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32) 0 \n",
"_________________________________________________________________\n",
"separable_conv2d_2 (Separabl (None, 32, 32, 64) 2400 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 32, 32, 64) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_2 (Batch (None, 32, 32, 64) 256 \n",
"_________________________________________________________________\n",
"separable_conv2d_3 (Separabl (None, 32, 32, 64) 4736 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 32, 32, 64) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_3 (Batch (None, 32, 32, 64) 256 \n",
"_________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 16384) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 128) 2097280 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 128) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_4 (Batch (None, 128) 512 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 128) 16512 \n",
"_________________________________________________________________\n",
"activation_5 (Activation) (None, 128) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_5 (Batch (None, 128) 512 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 1) 129 \n",
"_________________________________________________________________\n",
"activation_6 (Activation) (None, 1) 0 \n",
"=================================================================\n",
"Total params: 2,123,684\n",
"Trainable params: 2,122,820\n",
"Non-trainable params: 864\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model_128 = load_model(\"/userdata/kerasData/output/model/best_model128_128_e-4.h5\", custom_objects={\"f1\":f1})\n",
"model_128.summary()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"174/174 [==============================] - 419s 2s/step - loss: 0.7333 - accuracy: 0.5045 - f1: 0.6095 - recall: 0.8166 - precision: 0.5016\n"
]
},
{
"data": {
"text/plain": [
"[0.7332570552825928,\n",
" 0.5044947862625122,\n",
" 0.6094719767570496,\n",
" 0.8166065216064453,\n",
" 0.5015521049499512]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_128.evaluate(TestGenerator)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"separable_conv2d (SeparableC (None, 128, 128, 16) 211 \n",
"_________________________________________________________________\n",
"activation (Activation) (None, 128, 128, 16) 0 \n",
"_________________________________________________________________\n",
"batch_normalization (BatchNo (None, 128, 128, 16) 64 \n",
"_________________________________________________________________\n",
"max_pooling2d (MaxPooling2D) (None, 64, 64, 16) 0 \n",
"_________________________________________________________________\n",
"separable_conv2d_1 (Separabl (None, 64, 64, 32) 688 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 64, 64, 32) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_1 (Batch (None, 64, 64, 32) 128 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32) 0 \n",
"_________________________________________________________________\n",
"separable_conv2d_2 (Separabl (None, 32, 32, 64) 2400 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 32, 32, 64) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_2 (Batch (None, 32, 32, 64) 256 \n",
"_________________________________________________________________\n",
"separable_conv2d_3 (Separabl (None, 32, 32, 64) 4736 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 32, 32, 64) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_3 (Batch (None, 32, 32, 64) 256 \n",
"_________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 16384) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 128) 2097280 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 128) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_4 (Batch (None, 128) 512 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 128) 16512 \n",
"_________________________________________________________________\n",
"activation_5 (Activation) (None, 128) 0 \n",
"_________________________________________________________________\n",
"batch_normalization_5 (Batch (None, 128) 512 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 2) 258 \n",
"_________________________________________________________________\n",
"activation_6 (Activation) (None, 2) 0 \n",
"=================================================================\n",
"Total params: 2,123,813\n",
"Trainable params: 2,122,949\n",
"Non-trainable params: 864\n",
"_________________________________________________________________\n",
"Found 3485 images belonging to 2 classes.\n"
]
}
],
"source": [
"INIT_LR = 1e-4\n",
"batch_size = 16\n",
"NUM_EPOCHS = 200\n",
"\n",
"image_size=(2048, 1536)\n",
"class_mode=\"categorical\"\n",
"\n",
"\n",
"model_e5 = load_model('/userdata/kerasData/best_model_e5.h5', custom_objects={\"f1\":f1})\n",
"model_e5.summary()\n",
"\n",
"\n",
"image_generator = tf.keras.preprocessing.image.ImageDataGenerator()\n",
"testGenerator = image_generator.flow_from_directory(\n",
" \"/userdata/kerasData/preloaded/flowDirectory/test\",\n",
" target_size=image_size,\n",
" seed=1000,\n",
" batch_size=batch_size,\n",
" class_mode=class_mode)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 3485 images belonging to 2 classes.\n"
]
}
],
"source": [
"\n",
"image_size=(2048, 1536)\n",
"class_mode=\"categorical\"\n",
"image_generator = tf.keras.preprocessing.image.ImageDataGenerator()\n",
"testGenerator = image_generator.flow_from_directory(\n",
" \"/userdata/kerasData/preloaded/flowDirectory/test\",\n",
" target_size=image_size,\n",
" seed=1000,\n",
" batch_size=batch_size,\n",
" class_mode=class_mode)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"ename": "InvalidArgumentError",
"evalue": "2 root error(s) found.\n (0) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n\t [[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_39]]\n (1) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n0 successful operations.\n0 derived errors ignored. [Op:__inference_test_function_2377]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nFunction call stack:\ntest_function -> test_function\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-6de600d60988>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_e5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestGenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)\u001b[0m\n\u001b[1;32m 1079\u001b[0m step_num=step):\n\u001b[1;32m 1080\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1081\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1082\u001b[0m \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1083\u001b[0m \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[0;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;31m# stateless function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 645\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[0mcanon_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanon_kwds\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2418\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2419\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2422\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 1663\u001b[0m if isinstance(t, (ops.Tensor,\n\u001b[1;32m 1664\u001b[0m resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m self.captured_inputs)\n\u001b[0m\u001b[1;32m 1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1667\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1744\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1745\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1747\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m outputs = execute.execute_with_cancellation(\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mInvalidArgumentError\u001b[0m: 2 root error(s) found.\n (0) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n\t [[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_39]]\n (1) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n0 successful operations.\n0 derived errors ignored. [Op:__inference_test_function_2377]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nFunction call stack:\ntest_function -> test_function\n"
]
}
],
"source": [
"model_e5.evaluate(testGenerator)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"language": "python",
"name": "python3"
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"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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