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67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
#!/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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print('Build model...')
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main_input = Input(shape=(None, 22), name='main_input')
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#x = Dense(44, activation='relu')(main_input)
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#x = GRU(44, dropout=0.0, recurrent_dropout=0.0, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
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x=main_input
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x = GRU(128, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
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#x = GRU(128, return_sequences=True)(x)
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#x = GRU(22, activation='relu', return_sequences=True)(x)
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x = Dense(22, activation='sigmoid')(x)
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#x = Dense(22, activation='softplus')(x)
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model = Model(inputs=main_input, outputs=x)
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batch_size = 32
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print('Loading data...')
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with h5py.File('denoise_data.h5', 'r') as hf:
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all_data = hf['denoise_data'][:]
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print('done.')
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window_size = 500
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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, :-22]
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x_train = np.reshape(x_train, (nb_sequences, window_size, 22))
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y_train = np.copy(all_data[:nb_sequences*window_size, -22:])
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y_train = np.reshape(y_train, (nb_sequences, window_size, 22))
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#y_train = -20*np.log10(np.add(y_train, .03));
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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='mean_squared_error',
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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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