pdfcoffee

Chapter 2
Let's see an example of a custom layer that simply multiplies an input by a matrix
named kernel (for the sake of simplicity, the import lines are skipped in this text but
are of course used in GitHub code):
class MyLayer(layers.Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1], self.output_dim),
initializer='uniform',
trainable=True)
def call(self, inputs):
# Do the multiplication and return
return tf.matmul(inputs, self.kernel)
Once the MyLayer() custom brick is defined, it can be composed just like any other
brick, as in this following example, where a Sequential model is defined by stacking
MyLayer with a softmax activation function:
model = tf.keras.Sequential([
MyLayer(20),
layers.Activation('softmax')])
So, in short, you can use Model subclassing if you are in the business of building
bricks.
In this section we have seen that tf.keras offers a higher API level, with three
different programming models: Sequential API, Functional API, and Model
subclassing. Now let's move our attention to callbacks, a different feature, which
is useful during training with tf.keras.
Callbacks
Callbacks are objects passed to a model to extend or modify behaviors during
training. There are a few useful callbacks that are commonly used in tf.keras:
• tf.keras.callbacks.ModelCheckpoint: This feature is used to save
checkpoints of your model at regular intervals and recover in case of
problems.
[ 67 ]