avletters_convae_drop.py 文件源码

def create_model(incoming, options):
    input_p = 0.2
    hidden_p = 0.5
    conv_num_filters1 = int(100 / (1.0 - input_p))
    conv_num_filters2 = int(150 / (1.0 - hidden_p))
    conv_num_filters3 = int(200 / (1.0 - hidden_p))
    filter_size1 = 5
    filter_size2 = 5
    filter_size3 = 3
    pool_size = 2
    encode_size = int(options['BOTTLENECK'] / 0.5)
    dense_mid_size = int(options['DENSE'] / 0.5)
    pad_in = 'valid'
    pad_out = 'full'
    scaled_tanh = create_scaled_tanh()
    dropout0 = DropoutLayer(incoming, p=0.2, name='dropout0')
    conv2d1 = Conv2DLayer(dropout0, num_filters=conv_num_filters1, filter_size=filter_size1, pad=pad_in, name='conv2d1', nonlinearity=scaled_tanh)
    maxpool2d2 = MaxPool2DLayer(conv2d1, pool_size=pool_size, name='maxpool2d2')
    dropout1 = DropoutLayer(maxpool2d2, name='dropout1')
    conv2d3 = Conv2DLayer(dropout1, num_filters=conv_num_filters2, filter_size=filter_size2, pad=pad_in, name='conv2d3', nonlinearity=scaled_tanh)
    maxpool2d4 = MaxPool2DLayer(conv2d3, pool_size=pool_size, name='maxpool2d4', pad=(1,0))
    dropout2 = DropoutLayer(maxpool2d4, name='dropout2')
    conv2d5 = Conv2DLayer(dropout2, num_filters=conv_num_filters3, filter_size=filter_size3, pad=pad_in, name='conv2d5', nonlinearity=scaled_tanh)
    reshape6 = ReshapeLayer(conv2d5, shape=([0], -1), name='reshape6')  # 3000
    reshape6_output = reshape6.output_shape[1]
    dropout3 = DropoutLayer(reshape6, name='dropout3')
    dense7 = DenseLayer(dropout3, num_units=dense_mid_size, name='dense7', nonlinearity=scaled_tanh)
    dropout4 = DropoutLayer(dense7, name='dropout4')
    bottleneck = DenseLayer(dropout4, num_units=encode_size, name='bottleneck', nonlinearity=linear)
    # print_network(bottleneck)
    dense8 = DenseLayer(bottleneck, num_units=dense_mid_size, W=bottleneck.W.T, name='dense8', nonlinearity=linear)
    dense9 = DenseLayer(dense8, num_units=reshape6_output, W=dense7.W.T, nonlinearity=scaled_tanh, name='dense9')
    reshape10 = ReshapeLayer(dense9, shape=([0], conv_num_filters3, 3, 5), name='reshape10')  # 32 x 4 x 7
    deconv2d11 = Deconv2DLayer(reshape10, conv2d5.input_shape[1], conv2d5.filter_size, stride=conv2d5.stride,
                               W=conv2d5.W, flip_filters=not conv2d5.flip_filters, name='deconv2d11', nonlinearity=scaled_tanh)
    upscale2d12 = Upscale2DLayer(deconv2d11, scale_factor=pool_size, name='upscale2d12')
    deconv2d13 = Deconv2DLayer(upscale2d12, conv2d3.input_shape[1], conv2d3.filter_size, stride=conv2d3.stride,
                               W=conv2d3.W, flip_filters=not conv2d3.flip_filters, name='deconv2d13', nonlinearity=scaled_tanh)
    upscale2d14 = Upscale2DLayer(deconv2d13, scale_factor=pool_size, name='upscale2d14')
    deconv2d15 = Deconv2DLayer(upscale2d14, conv2d1.input_shape[1], conv2d1.filter_size, stride=conv2d1.stride,
                               crop=(1, 0), W=conv2d1.W, flip_filters=not conv2d1.flip_filters, name='deconv2d14', nonlinearity=scaled_tanh)
    reshape16 = ReshapeLayer(deconv2d15, ([0], -1), name='reshape16')
    return reshape16, bottleneck