policy.py 文件源码

def create_network(**kwargs):
        defaults = {
            'timesteps': 128,
            'data_dim': 14,
            'nb_filter': 64,
            'filter_length': 3,
            'pool_length': 2
        }
        params = defaults
        params.update(**kwargs)

        network = Sequential()

        network.add(Convolution1D(nb_filter=params['nb_filter'],
                                  filter_length=params['filter_length'],
                                  border_mode='valid',
                                  activation='relu',
                                  subsample_length=1,
                                  input_shape=(params['timesteps'], params[
                                      'data_dim'])))
        network.add(MaxPooling1D(pool_length=params['pool_length']))
        network.add(Dropout(0.5))

        # network.add(Convolution1D(nb_filter=params['nb_filter'],
        #                           filter_length=params['filter_length'],
        #                           border_mode='valid',
        #                           activation='relu',
        #                           subsample_length=1))
        # network.add(MaxPooling1D(pool_length=params['pool_length']))
        # network.add(Dropout(0.5))

        # network.add(Flatten())
        # # Note: Keras does automatic shape inference.
        # network.add(Dense(params['nb_filter'] * 4))
        # network.add(Activation('relu'))
        # network.add(Dropout(0.25))

        network.add(LSTM(64))
        network.add(Dropout(0.15))
        network.add(Dense(1))
        network.add(Activation('sigmoid'))

        network.compile(optimizer='rmsprop',
                        loss='binary_crossentropy',
                        metrics=['accuracy'])
        return network