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tf.Module

Base neural network module class.

Used in the notebooks

Used in the guide Used in the tutorials

A module is a named container for tf.Variables, other tf.Modules and functions which apply to user input. For example a dense layer in a neural network might be implemented as a tf.Module:

class Dense(tf.Module):
  def __init__(self, input_dim, output_size, name=None):
    super(Dense, self).__init__(name=name)
    self.w = tf.Variable(
      tf.random.normal([input_dim, output_size]), name='w')
    self.b = tf.Variable(tf.zeros([output_size]), name='b')
  def __call__(self, x):
    y = tf.matmul(x, self.w) + self.b
    return tf.nn.relu(y)

You can use the Dense layer as you would expect:

d = Dense(input_dim=3, output_size=2)
d(tf.ones([1, 3]))
<tf.Tensor: shape=(1, 2), dtype=float32, numpy=..., dtype=float32)>

By subclassing tf.Module instead of object any tf.Variable or tf.Module instances assigned to object properties can be collected using the variables, trainable_variables or submodules property:

d.variables
    (<tf.Variable 'b:0' shape=(2,) dtype=float32, numpy=...,
    dtype=float32)>,
    <tf.Variable 'w:0' shape=(3, 2) dtype=float32, numpy=..., dtype=float32)>)

Subclasses of tf.Module can also take advantage of the _flatten method which can be used to implement tracking of any other types.

All tf.Module classes have an associated tf.name_scope which can be used to group operations in TensorBoard and create hierarchies for variable names which can help with debugging. We suggest using the name scope when creating n