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
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