Add RNN for VAD and speech/music classification

Based on two dense layers with a GRU layer in the middle

scripts/rnn_train.py

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