evaluate_delta_features.py 文件源码

def compile_encoder(encoderpath=None):
    # create input
    if encoderpath:
        l_encoder = pickle.load(open(encoderpath, 'rb'))
        input_var = las.layers.get_all_layers(l_encoder)[0].input_var
        visualize_layer(las.layers.get_all_layers(l_encoder)[2], 40, 30)
    else:
        input_var = T.matrix('input', dtype='float32')
        weights, biases = autoencoder.load_dbn()
        en_activations = [sigmoid, sigmoid, sigmoid, linear]
        en_layersizes = [2000, 1000, 500, 50]
        l_input = InputLayer((None, 1200), input_var, name='input')
        l_encoder = autoencoder.create_model(l_input, weights[:4], biases[:4], en_activations, en_layersizes)
    print_network(l_encoder)

    encoded_features = las.layers.get_output(l_encoder)
    encode_fn = theano.function([input_var], encoded_features, allow_input_downcast=True)
    return encode_fn