pdfcoffee

Autoencoders
4. Let us now define our encoder. The encoder consists of three convolutional
layers, each followed by a max pooling layer. Since we are using the MNIST
dataset the shape of the input image is 28 × 28 (single channel) and the
output image is of size 4 × 4 (and since the last convolutional layer has 16
filters, the image has 16 channels):
class Encoder(K.layers.Layer):
def __init__(self, filters):
super(Encoder, self).__init__()
self.conv1 = Conv2D(filters=filters[0], kernel_size=3,
strides=1, activation='relu', padding='same')
self.conv2 = Conv2D(filters=filters[1], kernel_size=3,
strides=1, activation='relu', padding='same')
self.conv3 = Conv2D(filters=filters[2], kernel_size=3,
strides=1, activation='relu', padding='same')
self.pool = MaxPooling2D((2, 2), padding='same')
def call(self, input_features):
x = self.conv1(input_features)
#print("Ex1", x.shape)
x = self.pool(x)
#print("Ex2", x.shape)
x = self.conv2(x)
x = self.pool(x)
x = self.conv3(x)
x = self.pool(x)
return x
5. Next comes the decoder. It is the exact opposite of the encoder in design, and
instead of max pooling we are using upsampling to increase the size back.
Notice the commented print statements: you can use them to understand
how the shape gets modified after each step. Also notice both encoder and
decoder are still classes based on the TensorFlow Keras Layers class, but
now they have multiple layers inside them. So now you know how to build a
complex custom layer:
class Decoder(K.layers.Layer):
def __init__(self, filters):
super(Decoder, self).__init__()
self.conv1 = Conv2D(filters=filters[2], kernel_size=3,
strides=1, activation='relu', padding='same')
self.conv2 = Conv2D(filters=filters[1], kernel_size=3,
strides=1, activation='relu', padding='same')
self.conv3 = Conv2D(filters=filters[0], kernel_size=3,
strides=1, activation='relu', padding='valid')
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