music.py 文件源码

def create_network():
    dp = tflearn.data_preprocessing.DataPreprocessing()
    dp.add_featurewise_zero_center()
    dp.add_featurewise_stdnorm()
    #dp.add_samplewise_zero_center()
    #dp.add_samplewise_stdnorm()

    network = tflearn.input_data(shape=[None, chunk_size])#, data_preprocessing=dp)

    # input is a real signal
    network = tf.complex(network, 0.0)

    # fft the input
    input_fft = tf.fft(network)
    input_orig_fft = input_fft
    input_fft = tf.stack([tf.real(input_fft), tf.imag(input_fft)], axis=2)
    fft_size = int(input_fft.shape[1])
    network = input_fft
    print("fft shape: " + str(input_fft.get_shape()))

    omg = fft_size

    nn_reg = None

    mask = network

    mask = tflearn.layers.fully_connected(mask, omg*2, activation="tanh", regularizer=nn_reg)
    mask = tflearn.layers.normalization.batch_normalization(mask)

    mask = tflearn.layers.fully_connected(mask, omg, activation="tanh", regularizer=nn_reg)
    mask = tflearn.layers.normalization.batch_normalization(mask)

    mask = tflearn.layers.fully_connected(mask, omg/2, activation="tanh", regularizer=nn_reg)
    mask = tflearn.layers.normalization.batch_normalization(mask)

    #mask = tflearn.layers.fully_connected(mask, omg/4, activation="tanh")
    mask = tflearn.reshape(mask, [-1, 1, omg/2])
    mask = tflearn.layers.recurrent.lstm(mask, omg/4)

    mask = tflearn.layers.fully_connected(mask, omg/2, activation="tanh", regularizer=nn_reg)
    mask = tflearn.layers.normalization.batch_normalization(mask)

    mask = tflearn.layers.fully_connected(mask, omg, activation="tanh", regularizer=nn_reg)
    mask = tflearn.layers.normalization.batch_normalization(mask)

    mask = tflearn.layers.fully_connected(mask, omg*2, activation="tanh", regularizer=nn_reg)
    mask = tflearn.layers.normalization.batch_normalization(mask)

    mask = tflearn.layers.fully_connected(mask, omg, activation="sigmoid", regularizer=nn_reg)

    real = tf.multiply(tf.real(input_orig_fft), mask)
    imag = tf.multiply(tf.imag(input_orig_fft), mask)    
    network = tf.real(tf.ifft(tf.complex(real, imag)))

    print("final shape: " + str(network.get_shape()))

    network = tflearn.regression(network, optimizer="adam", learning_rate=learning_rate, loss="mean_square")

    return network