Python lasagne.layers 模块，BatchNormLayer() 实例源码

def create_model(incoming, options):
    conv_num_filters1 = 100
    conv_num_filters2 = 150
    conv_num_filters3 = 200
    filter_size1 = 5
    filter_size2 = 5
    filter_size3 = 3
    pool_size = 2
    encode_size = options['BOTTLENECK']
    dense_mid_size = options['DENSE']
    pad_in = 'valid'
    pad_out = 'full'
    scaled_tanh = create_scaled_tanh()

    conv2d1 = Conv2DLayer(incoming, num_filters=conv_num_filters1, filter_size=filter_size1, pad=pad_in, name='conv2d1', nonlinearity=scaled_tanh)
    maxpool2d3 = MaxPool2DLayer(conv2d1, pool_size=pool_size, name='maxpool2d3')
    bn2 = BatchNormLayer(maxpool2d3, name='batchnorm2')
    conv2d4 = Conv2DLayer(bn2, num_filters=conv_num_filters2, filter_size=filter_size2, pad=pad_in, name='conv2d4', nonlinearity=scaled_tanh)
    maxpool2d6 = MaxPool2DLayer(conv2d4, pool_size=pool_size, name='maxpool2d6', pad=(1,0))
    bn3 = BatchNormLayer(maxpool2d6, name='batchnorm3')
    conv2d7 = Conv2DLayer(bn3, num_filters=conv_num_filters3, filter_size=filter_size3, pad=pad_in, name='conv2d7', nonlinearity=scaled_tanh)
    reshape9 = ReshapeLayer(conv2d7, shape=([0], -1), name='reshape9')  # 3000
    reshape9_output = reshape9.output_shape[1]
    bn8 = BatchNormLayer(reshape9, name='batchnorm8')
    dense10 = DenseLayer(bn8, num_units=dense_mid_size, name='dense10', nonlinearity=scaled_tanh)
    bn11 = BatchNormLayer(dense10, name='batchnorm11')
    bottleneck = DenseLayer(bn11, num_units=encode_size, name='bottleneck', nonlinearity=linear)
    # print_network(bottleneck)
    dense12 = DenseLayer(bottleneck, num_units=dense_mid_size, W=bottleneck.W.T, name='dense12', nonlinearity=linear)
    dense13 = DenseLayer(dense12, num_units=reshape9_output, W=dense10.W.T, nonlinearity=scaled_tanh, name='dense13')
    reshape14 = ReshapeLayer(dense13, shape=([0], conv_num_filters3, 3, 5), name='reshape14')  # 32 x 4 x 7
    deconv2d19 = Deconv2DLayer(reshape14, conv2d7.input_shape[1], conv2d7.filter_size, stride=conv2d7.stride,
                               W=conv2d7.W, flip_filters=not conv2d7.flip_filters, name='deconv2d19', nonlinearity=scaled_tanh)
    upscale2d16 = Upscale2DLayer(deconv2d19, scale_factor=pool_size, name='upscale2d16')
    deconv2d17 = Deconv2DLayer(upscale2d16, conv2d4.input_shape[1], conv2d4.filter_size, stride=conv2d4.stride,
                               W=conv2d4.W, flip_filters=not conv2d4.flip_filters, name='deconv2d17', nonlinearity=scaled_tanh)
    upscale2d18 = Upscale2DLayer(deconv2d17, scale_factor=pool_size, name='upscale2d18')
    deconv2d19 = Deconv2DLayer(upscale2d18, 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)
    reshape20 = ReshapeLayer(deconv2d19, ([0], -1), name='reshape20')
    return reshape20, bottleneck

def create_model(incoming, options):
    conv_num_filters1 = 100
    conv_num_filters2 = 150
    conv_num_filters3 = 200
    filter_size1 = 5
    filter_size2 = 5
    filter_size3 = 3
    pool_size = 2
    encode_size = options['BOTTLENECK']
    dense_mid_size = options['DENSE']
    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)
    bn1 = BatchNormLayer(conv2d1, name='batchnorm1')
    maxpool2d2 = MaxPool2DLayer(bn1, 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)
    bn2 = BatchNormLayer(conv2d3, name='batchnorm2')
    maxpool2d4 = MaxPool2DLayer(bn2, 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)
    bn3 = BatchNormLayer(conv2d5, name='batchnorm3')
    reshape6 = ReshapeLayer(bn3, 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)
    bn4 = BatchNormLayer(dense7, name='batchnorm4')
    dropout4 = DropoutLayer(bn4, 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