mlp-fine-tuning-tuner.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Import Libraries","metadata":{}},{"cell_type":"code","source":"from tensorflow import keras\nfrom keras import backend as K\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nimport keras_tuner as kt\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.models import Sequential\nfrom keras.initializers import HeNormal\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow import keras\nfrom keras.datasets import mnist \nfrom tensorflow.keras.regularizers import l2\nfrom keras import layers\nfrom keras.utils import np_utils\nfrom sklearn.metrics import precision_score, recall_score, confusion_matrix, ConfusionMatrixDisplay","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:10:55.839896Z","iopub.execute_input":"2022-09-20T13:10:55.840357Z","iopub.status.idle":"2022-09-20T13:11:01.725118Z","shell.execute_reply.started":"2022-09-20T13:10:55.840262Z","shell.execute_reply":"2022-09-20T13:11:01.724157Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"# # detect and init the TPU\n# tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect()\n\n# # instantiate a distribution strategy\n# tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:11:01.726846Z","iopub.execute_input":"2022-09-20T13:11:01.727493Z","iopub.status.idle":"2022-09-20T13:11:01.735036Z","shell.execute_reply.started":"2022-09-20T13:11:01.727431Z","shell.execute_reply":"2022-09-20T13:11:01.733402Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"## Define the metrics\nF measure is defined according to [this](https://deepai.org/machine-learning-glossary-and-terms/f-score)","metadata":{}},{"cell_type":"code","source":"# def f_measure(y_true, y_pred):\n    \n#     f = 2*precision_score(y_true, y_pred)*recall_score(y_true,y_pred)/(precision_score(y_true, y_pred) +\n#                                                                      recall_score(y_true,y_pred) + K.epsilon())\n#     return tf.convert_to_tensor(f) \ndef precision_m(y_true, y_pred):\n    \"\"\"Precision metric.\n    Only computes a batch-wise average of precision.\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\n\ndef recall_m(y_true, y_pred):\n    \"\"\"Recall metric.\n        Only computes a batch-wise average of recall.\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\ndef f_measure(y_true, y_pred):\n    precision = precision_m(y_true, y_pred)\n    recall = recall_m(y_true, y_pred)\n    return 2 * ((precision * recall) / (precision + recall + K.epsilon()))","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:11:01.737052Z","iopub.execute_input":"2022-09-20T13:11:01.737565Z","iopub.status.idle":"2022-09-20T13:11:01.750634Z","shell.execute_reply.started":"2022-09-20T13:11:01.737531Z","shell.execute_reply":"2022-09-20T13:11:01.749584Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"## Data Preprocessing","metadata":{}},{"cell_type":"code","source":"# load the datasheet\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# transform the y data to categorical\ny_train = np_utils.to_categorical(y_train, 10)\ny_test = np_utils.to_categorical(y_test, 10)\n\n# x data normalisation\nnormalisation = False\nif normalisation:\n    x_train = x_train / np.max(x_train)\n    x_test = x_test / np.max(x_test)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:11:01.753123Z","iopub.execute_input":"2022-09-20T13:11:01.753567Z","iopub.status.idle":"2022-09-20T13:11:02.214882Z","shell.execute_reply.started":"2022-09-20T13:11:01.753515Z","shell.execute_reply":"2022-09-20T13:11:02.213912Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n11493376/11490434 [==============================] - 0s 0us/step\n11501568/11490434 [==============================] - 0s 0us/step\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## General Parameters","metadata":{}},{"cell_type":"code","source":"length_validation = 0.2\nepochs = 1000\ninput_shape = [28, 28]\nneurons_output = 10\nbatch_size = 256\n\ntotal_neurons_hidden1 = [64, 128]\ntotal_neurons_hidden2 = [256, 512]\ntotal_alphas = [1e-1, 1e-3, 1e-6]\ntotal_learning_rates = [1e-1, 1e-2, 1e-3]","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:11:02.217238Z","iopub.execute_input":"2022-09-20T13:11:02.218170Z","iopub.status.idle":"2022-09-20T13:11:02.224301Z","shell.execute_reply.started":"2022-09-20T13:11:02.218130Z","shell.execute_reply":"2022-09-20T13:11:02.223269Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"## Build Model","metadata":{}},{"cell_type":"code","source":"def build_model(hp):\n    \n    neurons_hidden1 = hp.Choice('neurons_hidden1', values=total_neurons_hidden1)\n    neurons_hidden2 = hp.Choice('neurons_hidden2', values=total_neurons_hidden2)\n    alpha = keras.regularizers.L2(l2=hp.Choice('alpha', values=total_alphas))\n    learning_rate = hp.Choice('learning_rate', values=total_learning_rates)\n    initialiser = keras.initializers.HeNormal()\n    \n#     with tpu_strategy.scope():\n    model = Sequential()\n    model.add(Flatten(input_shape=input_shape))\n    model.add(Dense(neurons_hidden1, activation='relu', kernel_regularizer=alpha, kernel_initializer=initialiser))\n    model.add(Dense(neurons_hidden2, activation='relu', kernel_regularizer=alpha, kernel_initializer=initialiser))\n    model.add(Dense(neurons_output, activation='softmax'))\n    model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=learning_rate),\n                  loss=tf.keras.losses.CategoricalCrossentropy(),\n                  metrics=['accuracy', precision_m, recall_m, f_measure])\n    \n    return model\n    ","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:11:02.225806Z","iopub.execute_input":"2022-09-20T13:11:02.226356Z","iopub.status.idle":"2022-09-20T13:11:02.236430Z","shell.execute_reply.started":"2022-09-20T13:11:02.226320Z","shell.execute_reply":"2022-09-20T13:11:02.235500Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"## Define the Tuner","metadata":{}},{"cell_type":"code","source":"tuner = kt.RandomSearch(\n    build_model, objective=kt.Objective(\"f_measure\", direction='max'))\n\nearly_stopping = EarlyStopping(\n    monitor=\"loss\",\n    patience=200,\n    restore_best_weights=True)","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:11:02.239707Z","iopub.execute_input":"2022-09-20T13:11:02.239970Z","iopub.status.idle":"2022-09-20T13:11:05.204433Z","shell.execute_reply.started":"2022-09-20T13:11:02.239946Z","shell.execute_reply":"2022-09-20T13:11:05.203501Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"2022-09-20 13:11:02.347583: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:02.448525: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:02.449243: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:02.450636: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2022-09-20 13:11:02.450954: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:02.451659: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:02.452281: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:04.693726: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:04.694541: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:04.695238: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-09-20 13:11:04.695842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15401 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Tune the Model","metadata":{}},{"cell_type":"code","source":"tuner.search(x_train, y_train,\n             epochs=epochs,\n             validation_split=length_validation,\n             callbacks=[early_stopping])","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:11:05.208697Z","iopub.execute_input":"2022-09-20T13:11:05.211149Z","iopub.status.idle":"2022-09-20T13:25:35.600018Z","shell.execute_reply.started":"2022-09-20T13:11:05.211112Z","shell.execute_reply":"2022-09-20T13:25:35.597827Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"\nSearch: Running Trial #1\n\nValue             |Best Value So Far |Hyperparameter\n64                |?                 |neurons_hidden1\n256               |?                 |neurons_hidden2\n1e-06             |?                 |alpha\n0.1               |?                 |learning_rate\n\n","output_type":"stream"},{"name":"stderr","text":"2022-09-20 13:11:05.578531: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/1000\n1500/1500 [==============================] - 7s 3ms/step - loss: 457.7430 - accuracy: 0.1034 - precision_m: 0.0057 - recall_m: 5.4167e-04 - f_measure: 7.5335e-04 - val_loss: 2.3080 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 2/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3139 - accuracy: 0.1061 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3116 - val_accuracy: 0.0914 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 3/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3152 - accuracy: 0.1019 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3108 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 4/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3148 - accuracy: 0.1033 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3110 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 5/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3146 - accuracy: 0.1022 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3178 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 6/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3143 - accuracy: 0.1025 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3120 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 7/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1043 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3190 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 8/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3145 - accuracy: 0.1049 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3121 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 9/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3143 - accuracy: 0.1042 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3141 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 10/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3150 - accuracy: 0.1024 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3231 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 11/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3139 - accuracy: 0.1040 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3132 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 12/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1032 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3253 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 13/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3146 - accuracy: 0.1033 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3152 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 14/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1034 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3192 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 15/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3142 - accuracy: 0.1040 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3127 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 16/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3138 - accuracy: 0.1046 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3192 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 17/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1053 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3090 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 18/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3150 - accuracy: 0.1017 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3203 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 19/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3146 - accuracy: 0.1043 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3219 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 20/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1016 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3109 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 21/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3140 - accuracy: 0.1049 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3174 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 22/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3141 - accuracy: 0.1050 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3145 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 23/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3151 - accuracy: 0.1019 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3262 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 24/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3139 - accuracy: 0.1030 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3153 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 25/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1035 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3112 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 26/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1033 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3127 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 27/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1026 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3141 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 28/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3144 - accuracy: 0.1062 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3202 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 29/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1037 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3062 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 30/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1048 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3210 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 31/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3146 - accuracy: 0.1023 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3224 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 32/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1022 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3091 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 33/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3150 - accuracy: 0.1010 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3154 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 34/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3143 - accuracy: 0.1038 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3112 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 35/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1040 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3145 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 36/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1028 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3314 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 37/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3142 - accuracy: 0.1016 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3083 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 38/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3153 - accuracy: 0.1043 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3119 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 39/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3153 - accuracy: 0.1001 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3110 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 40/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1047 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3096 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 41/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3145 - accuracy: 0.1026 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3215 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 42/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3139 - accuracy: 0.1045 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3074 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 43/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3156 - accuracy: 0.1010 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3165 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 44/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1035 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3096 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 45/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3146 - accuracy: 0.1042 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3119 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 46/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3148 - accuracy: 0.1035 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3167 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 47/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3137 - accuracy: 0.1050 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3289 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 48/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3139 - accuracy: 0.1064 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3212 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 49/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3138 - accuracy: 0.1050 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3038 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 50/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3150 - accuracy: 0.1038 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3125 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 51/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3147 - accuracy: 0.1037 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3173 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 52/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3146 - accuracy: 0.1047 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3093 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 53/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1031 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3106 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 54/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3150 - accuracy: 0.1030 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3194 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 55/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1046 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3152 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 56/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1046 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3145 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 57/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3142 - accuracy: 0.1048 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3102 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 58/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3148 - accuracy: 0.1044 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3265 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 59/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3150 - accuracy: 0.1013 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3117 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 60/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3146 - accuracy: 0.1028 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3246 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 61/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1032 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3121 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 62/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3153 - accuracy: 0.1025 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3093 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 63/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3142 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 64/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3150 - accuracy: 0.1011 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3208 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 65/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1031 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3184 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 66/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1030 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3077 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 67/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3151 - accuracy: 0.1032 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3105 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 68/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3145 - accuracy: 0.1051 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3087 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 69/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3145 - accuracy: 0.1022 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3078 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 70/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3140 - accuracy: 0.1049 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3201 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 71/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3140 - accuracy: 0.1037 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3149 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 72/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1036 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3125 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 73/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3148 - accuracy: 0.1038 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3074 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 74/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3127 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 75/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3142 - accuracy: 0.1045 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3152 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 76/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1017 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3123 - val_accuracy: 0.0914 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 77/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3140 - accuracy: 0.1081 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3215 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 78/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1023 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3091 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 79/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3154 - accuracy: 0.1026 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3117 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 80/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3146 - accuracy: 0.1047 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3225 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 81/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3148 - accuracy: 0.1036 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3090 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 82/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3154 - accuracy: 0.1040 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3159 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 83/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3153 - accuracy: 0.1004 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3067 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 84/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3139 - accuracy: 0.1036 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3154 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 85/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3143 - accuracy: 0.1016 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3204 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 86/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1029 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3101 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 87/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3136 - accuracy: 0.1037 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3440 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 88/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3142 - accuracy: 0.1058 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3159 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 89/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1038 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3141 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 90/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1044 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3214 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 91/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3145 - accuracy: 0.1069 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3167 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 92/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3150 - accuracy: 0.1033 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3091 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 93/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3149 - accuracy: 0.1046 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3222 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 94/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1035 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3095 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 95/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1027 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3083 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 96/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1031 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3085 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 97/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3149 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3230 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 98/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1038 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3149 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 99/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3148 - accuracy: 0.1033 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3132 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 100/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3152 - accuracy: 0.1039 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3154 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 101/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3149 - accuracy: 0.1038 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3180 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 102/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1027 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3121 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 103/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1028 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3116 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 104/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1025 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3103 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 105/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3137 - accuracy: 0.1056 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3122 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 106/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.0998 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3190 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 107/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3139 - accuracy: 0.1024 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3101 - val_accuracy: 0.0914 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 108/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3147 - accuracy: 0.1033 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3125 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 109/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1036 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3093 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 110/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1011 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3139 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 111/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1027 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3177 - val_accuracy: 0.0914 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 112/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1019 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3280 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 113/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1043 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3253 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 114/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3143 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3246 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 115/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3145 - accuracy: 0.1051 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3141 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 116/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3074 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 117/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3138 - accuracy: 0.1037 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3327 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 118/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3134 - accuracy: 0.1042 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3115 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 119/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3139 - accuracy: 0.1026 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3187 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 120/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3138 - accuracy: 0.1053 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3099 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 121/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1053 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3282 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 122/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1008 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3081 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 123/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1038 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3201 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 124/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3145 - accuracy: 0.1025 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3156 - val_accuracy: 0.0998 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 125/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3145 - accuracy: 0.1053 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3093 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 126/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3147 - accuracy: 0.1010 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3216 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 127/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1028 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3089 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 128/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1012 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3216 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 129/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1031 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3232 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 130/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3150 - accuracy: 0.1023 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3159 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 131/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3139 - accuracy: 0.1049 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3138 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 132/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3147 - accuracy: 0.1032 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3099 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 133/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3145 - accuracy: 0.1046 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3087 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 134/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1031 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3123 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 135/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1034 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3170 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 136/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3150 - accuracy: 0.1020 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3065 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 137/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3147 - accuracy: 0.1037 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3177 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 138/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1032 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3108 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 139/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3146 - accuracy: 0.1035 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3141 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 140/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1057 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3134 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 141/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3146 - accuracy: 0.1028 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3202 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 142/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1029 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3219 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 143/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3137 - accuracy: 0.1036 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3114 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 144/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3146 - accuracy: 0.1043 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3074 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 145/1000\n1500/1500 [==============================] - 6s 4ms/step - loss: 2.3146 - accuracy: 0.1044 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3238 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 146/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3146 - accuracy: 0.1024 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3257 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 147/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3145 - accuracy: 0.1042 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3105 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 148/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3147 - accuracy: 0.1018 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3096 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 149/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3140 - accuracy: 0.1034 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3185 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 150/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1034 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3156 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 151/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3142 - accuracy: 0.1060 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3149 - val_accuracy: 0.1035 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 152/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1046 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3185 - val_accuracy: 0.0975 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 153/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3138 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3059 - val_accuracy: 0.0995 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 154/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3153 - accuracy: 0.1029 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3068 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 155/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3145 - accuracy: 0.1018 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3158 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 156/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3146 - accuracy: 0.1055 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3094 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 157/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1015 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3143 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 158/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3143 - accuracy: 0.1044 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3125 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 159/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3134 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 160/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1074 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3271 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 161/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3148 - accuracy: 0.1032 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3169 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 162/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1021 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3127 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 163/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1054 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3059 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 164/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3147 - accuracy: 0.1049 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3111 - val_accuracy: 0.1060 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 165/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3140 - accuracy: 0.1049 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3272 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 166/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3149 - accuracy: 0.1044 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3116 - val_accuracy: 0.0997 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 167/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3145 - accuracy: 0.1040 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3199 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 168/1000\n1500/1500 [==============================] - 5s 3ms/step - loss: 2.3146 - accuracy: 0.1034 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3167 - val_accuracy: 0.0989 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 169/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3149 - accuracy: 0.1025 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3251 - val_accuracy: 0.0956 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 170/1000\n1500/1500 [==============================] - 5s 4ms/step - loss: 2.3149 - accuracy: 0.1027 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00 - val_loss: 2.3141 - val_accuracy: 0.1081 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00 - val_f_measure: 0.0000e+00\nEpoch 171/1000\n 407/1500 [=======>......................] - ETA: 2s - loss: 2.3151 - accuracy: 0.1041 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - f_measure: 0.0000e+00","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_17/1425110755.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m              \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m              \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlength_validation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m              callbacks=[early_stopping])\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/base_tuner.py\u001b[0m in \u001b[0;36msearch\u001b[0;34m(self, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_trial_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_kwargs\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    180\u001b[0m             \u001b[0;31m# `results` is None indicates user updated oracle in `run_trial()`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;32mis\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;32m/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/tuner.py\u001b[0m in \u001b[0;36mrun_trial\u001b[0;34m(self, trial, *args, **kwargs)\u001b[0m\n\u001b[1;32m    292\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_checkpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m             \u001b[0mcopied_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"callbacks\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m             \u001b[0mobj_value\u001b[0m \u001b[0;34m=\u001b[0m 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tuner_utils.convert_to_metrics_dict(\n\u001b[1;32m    224\u001b[0m             \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moracle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"HyperModel.fit()\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/hypermodel.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, hp, model, *args, **kwargs)\u001b[0m\n\u001b[1;32m    135\u001b[0m             \u001b[0mIf\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mit\u001b[0m \u001b[0mshould\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mthe\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m     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variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\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;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\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[1;32m    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\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/python3.7/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   3038\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3039\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3040\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3042\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/python3.7/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   1962\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   1963\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1964\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1965\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1966\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/python3.7/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    594\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    595\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--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\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    598\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/opt/conda/lib/python3.7/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;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"markdown","source":"## Get the optimal hyperparameters","metadata":{}},{"cell_type":"code","source":"optimal_hyperparameters = tuner.get_best_hyperparameters(num_trials=1)[0]\n\nneurons_hidden1 = optimal_hyperparameters.get('neurons_hidden1')\nneurons_hidden2 = optimal_hyperparameters.get('neurons_hidden2')\nalpha = optimal_hyperparameters.get('alpha')\nlearning_rate = optimal_hyperparameters.get('learning_rate')\n\nprint(f'Best number of neurons in the first hidden layer: ' +str(neurons_hidden1))\nprint(f'Best number of neurons in the second hidden layer: ' +str(neurons_hidden2))\nprint(f'Best value for alpha: ' +str(alpha))\nprint(f'Best value for learning_rate: ' +str(learning_rate))","metadata":{"execution":{"iopub.status.busy":"2022-09-20T13:25:35.601086Z","iopub.status.idle":"2022-09-20T13:25:35.601607Z","shell.execute_reply.started":"2022-09-20T13:25:35.601323Z","shell.execute_reply":"2022-09-20T13:25:35.601358Z"},"trusted":true},"execution_count":null,"outputs":[]}]}