windpuller.py 文件源码

def __init__(self, input_shape, lr=0.01, n_layers=2, n_hidden=8, rate_dropout=0.2, loss='risk_estimation'):
        print("initializing..., learing rate %s, n_layers %s, n_hidden %s, dropout rate %s." %(lr, n_layers, n_hidden, rate_dropout))
        self.model = Sequential()
        self.model.add(Dropout(rate=rate_dropout, input_shape=(input_shape[0], input_shape[1])))
        for i in range(0, n_layers - 1):
            self.model.add(LSTM(n_hidden * 4, return_sequences=True, activation='tanh',
                                recurrent_activation='hard_sigmoid', kernel_initializer='glorot_uniform',
                                recurrent_initializer='orthogonal', bias_initializer='zeros',
                                dropout=rate_dropout, recurrent_dropout=rate_dropout))
        self.model.add(LSTM(n_hidden, return_sequences=False, activation='tanh',
                                recurrent_activation='hard_sigmoid', kernel_initializer='glorot_uniform',
                                recurrent_initializer='orthogonal', bias_initializer='zeros',
                                dropout=rate_dropout, recurrent_dropout=rate_dropout))
        self.model.add(Dense(1, kernel_initializer=initializers.glorot_uniform()))
        # self.model.add(BatchNormalization(axis=-1, moving_mean_initializer=Constant(value=0.5),
        #               moving_variance_initializer=Constant(value=0.25)))
        self.model.add(BatchRenormalization(axis=-1, beta_init=Constant(value=0.5)))
        self.model.add(Activation('relu_limited'))
        opt = RMSprop(lr=lr)
        self.model.compile(loss=loss,
                      optimizer=opt,
                      metrics=['accuracy'])