mirror of
https://github.com/TorqueGameEngines/Torque3D.git
synced 2026-01-20 20:54:46 +00:00
a745fc3757
added libraries: opus flac libsndfile updated: libvorbis libogg openal - Everything works as expected for now. Bare in mind libsndfile needed the check for whether or not it could find the xiph libraries removed in order for this to work.
68 lines
2.1 KiB
Python
68 lines
2.1 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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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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