40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
#!/usr/bin/env python3
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import tensorflow as tf
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import settings
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from tensorflow.keras import layers
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from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector, Activation, Bidirectional, Reshape
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from keras.models import Model, Sequential
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import numpy as np
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import sys
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import pickle
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train_data_path = sys.argv[1]
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save_model_path = sys.argv[2]
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epochs = int(sys.argv[3])
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model = Sequential()
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model.add(LSTM(128,input_shape=(96, 128),return_sequences=True))
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model.add(Dropout(0.3))
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model.add(LSTM(512, return_sequences=True))
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model.add(Dropout(0.3))
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model.add(LSTM(128))
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model.add(Dense(128))
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model.add(Dropout(0.3))
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model.add(Dense(128*96))
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model.add(Activation('softmax'))
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model.add(Reshape((96, 128)))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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# load training data
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print('Traing Samples: {}'.format(train_data_path))
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train_X = np.load(train_data_path)['arr_0']
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# model training
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model.fit(train_X, train_X, epochs=epochs, batch_size=32)
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# save trained model
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pickle_path = '{}.pickle'.format(save_model_path)
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pickle.dump(model, open(pickle_path,'wb'))
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print("Model save to {}".format(pickle_path))
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