TF-Slim > link > slim.stack() > slim.fully_connected()の簡略表記に使う

http://qiita.com/learn_tensorflow/items/3e46b2512a1bab73f5b2
のtrain.pyにて使われているSlimの学習中。

以下に注目した。

train.py

## NN のグラフ生成
hiddens = slim.stack(inputs_batch, slim.fully_connected, [2, 2], activation_fn=tf.nn.sigmoid, scope="hidden")
prediction = slim.fully_connected(hiddens, 1, activation_fn=tf.nn.sigmoid, scope="output")

Furthermore, TF-Slim's slim.stack operator allows a caller to repeatedly apply the same operation with different arguments to create a stack or tower of layers. slim.stack also creates a new tf.variable_scope for each operation created. For example, a simple way to create a Multi-Layer Perceptron (MLP):

# Verbose way:
x = slim.fully_connected(x, 32, scope='fc/fc_1')
x = slim.fully_connected(x, 64, scope='fc/fc_2')
x = slim.fully_connected(x, 128, scope='fc/fc_3')

# Equivalent, TF-Slim way using slim.stack:
slim.stack(x, slim.fully_connected, [32, 64, 128], scope='fc')

上の3行の書き方を1行で書くことができるとのことらしい。