68 lines
2.1 KiB
Python
Executable File
68 lines
2.1 KiB
Python
Executable File
#!/usr/bin/python
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from __future__ import print_function
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from keras.models import Sequential
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from keras.models import Model
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from keras.layers import Input
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from keras.layers import Dense
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from keras.layers import LSTM
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from keras.layers import GRU
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from keras.layers import SimpleRNN
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from keras.layers import Dropout
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from keras import losses
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import h5py
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from keras import backend as K
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import numpy as np
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def binary_crossentrop2(y_true, y_pred):
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return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
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print('Build model...')
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#model = Sequential()
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#model.add(Dense(16, activation='tanh', input_shape=(None, 25)))
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#model.add(GRU(12, dropout=0.0, recurrent_dropout=0.0, activation='tanh', recurrent_activation='sigmoid', return_sequences=True))
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#model.add(Dense(2, activation='sigmoid'))
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main_input = Input(shape=(None, 25), name='main_input')
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x = Dense(16, activation='tanh')(main_input)
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x = GRU(12, dropout=0.1, recurrent_dropout=0.1, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
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x = Dense(2, activation='sigmoid')(x)
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model = Model(inputs=main_input, outputs=x)
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batch_size = 64
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print('Loading data...')
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with h5py.File('features.h5', 'r') as hf:
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all_data = hf['features'][:]
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print('done.')
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window_size = 1500
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nb_sequences = len(all_data)/window_size
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print(nb_sequences, ' sequences')
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x_train = all_data[:nb_sequences*window_size, :-2]
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x_train = np.reshape(x_train, (nb_sequences, window_size, 25))
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y_train = np.copy(all_data[:nb_sequences*window_size, -2:])
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y_train = np.reshape(y_train, (nb_sequences, window_size, 2))
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all_data = 0;
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x_train = x_train.astype('float32')
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y_train = y_train.astype('float32')
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print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
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# try using different optimizers and different optimizer configs
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model.compile(loss=binary_crossentrop2,
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optimizer='adam',
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metrics=['binary_accuracy'])
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print('Train...')
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=200,
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validation_data=(x_train, y_train))
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model.save("newweights.hdf5")
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