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TF2.x API Docs module.py example fix.

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Doctest for module.py implemented. Also fixed doctest for Dense class in the same file.

Doctest for Dense class in the same file now runs properly and names of vars are consistent.

Doctest should work now, but I did have some trouble testing it, although I do have reqs installed.
This commit is contained in:
jpodivin 2020-06-19 17:41:40 +02:00
parent 4fba4cbfdc
commit e820edf7ae
1 changed files with 26 additions and 19 deletions
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45
tensorflow/python/module/module.py
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@ -40,18 +40,18 @@ class Module(tracking.AutoTrackable):
network might be implemented as a `tf.Module`:
>>> class Dense(tf.Module):
... def __init__(self, in_features, out_features, name=None):
... def __init__(self, input_dim, output_size, name=None):
... super(Dense, self).__init__(name=name)
... self.w = tf.Variable(
... tf.random.normal([in_features, out_features]), name='w')
... self.b = tf.Variable(tf.zeros([out_features]), name='b')
... 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(in_features=3, out_features=2)
>>> d = Dense(input_dim=3, output_size=2)
>>> d(tf.ones([1, 3]))
<tf.Tensor: shape=(1, 2), dtype=float32, numpy=..., dtype=float32)>
@ -77,22 +77,29 @@ class Module(tracking.AutoTrackable):
`with self.name_scope:` or you can annotate methods (apart from `__init__`)
with `@tf.Module.with_name_scope`.
```python
class MLP(tf.Module):
def __init__(self, input_size, sizes, name=None):
super(MLP, self).__init__(name=name)
self.layers = []
with self.name_scope:
for size in sizes:
self.layers.append(Dense(input_size=input_size, output_size=size))
input_size = size
>>> class MLP(tf.Module):
... def __init__(self, input_size, sizes, name=None):
... super(MLP, self).__init__(name=name)
... self.layers = []
... with self.name_scope:
... for size in sizes:
... self.layers.append(Dense(input_dim=input_size, output_size=size))
... input_size = size
... @tf.Module.with_name_scope
... def __call__(self, x):
... for layer in self.layers:
... x = layer(x)
... return x
>>> module = MLP(input_size=5, sizes=[5, 5])
>>> module.variables
(<tf.Variable 'mlp/b:0' shape=(5,) dtype=float32, numpy=..., dtype=float32)>,
<tf.Variable 'mlp/w:0' shape=(5, 5) dtype=float32, numpy=...,
dtype=float32)>,
<tf.Variable 'mlp/b:0' shape=(5,) dtype=float32, numpy=..., dtype=float32)>,
<tf.Variable 'mlp/w:0' shape=(5, 5) dtype=float32, numpy=...,
dtype=float32)>)
@tf.Module.with_name_scope
def __call__(self, x):
for layer in self.layers:
x = layer(x)
return x
```
"""
# AutoTrackable adds object attributes that users will not expect us to