bugs related to usage of tensorflow BatchNormalization

[jdongca2003]

Hi Surg,
I found a bug related to the usage of tensorflow BatchNormalization.
in ../othello/tensorflow/OthelloNNet.py

check API of tf.layers.batch_normalization (https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization)

See parameter "training". We need to create a bool placeholder to check whether it is in the training phase.

That is, After line 25 in tensorflow/OthelloNNet.py, update
self.isTraining = tf.placeholder(tf.bool, name="is_training")
h_conv1 = Relu(BatchNormalization(self.conv2d(x_image, args.num_channels, 'same'), axis=3, training=self.isTraining)) # batch_size x board_x x board_y x num_channels
h_conv2 = Relu(BatchNormalization(self.conv2d(h_conv1, args.num_channels, 'same'), axis=3, training=self.isTraining)) # batch_size x board_x x board_y x num_channels
h_conv3 = Relu(BatchNormalization(self.conv2d(h_conv2, args.num_channels, 'valid'), axis=3, training=self.isTraining)) # batch_size x (board_x-2) x (board_y-2) x num_channels
h_conv4 = Relu(BatchNormalization(self.conv2d(h_conv3, args.num_channels, 'valid'), axis=3, training=self.isTraining)) # batch_size x (board_x-4) x (board_y-4) x num_channels

s_fc1 = Dropout(Relu(BatchNormalization(Dense(h_conv4_flat, 1024), axis=1, training=self.isTraining)), rate=self.dropout)
s_fc2 = Dropout(Relu(BatchNormalization(Dense(s_fc1, 512), axis=1, training=self.isTraining)), rate=self.dropout)

After Line 50, add
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.train_step = tf.train.AdamOptimizer(self.args.lr).minimize(self.total_loss)

Also directly generate action probability after line 37
self.prob = tf.nn.softmax(self.pi)

In tensorflow/NNet.py, in train member function, update
input_dict = {self.nnet.input_boards: boards, self.nnet.target_pis: pis, self.nnet.target_vs: vs, self.nnet.dropout: args.dropout, self.nnet.isTraining: True}

In predict function,
prob, v = self.sess.run([self.nnet.prob, self.nnet.v], feed_dict={self.nnet.input_boards: board, self.nnet.dropout: 0, self.nnet.isTraining: False})
comment out line: "pi = np.exp(pi) / np.sum(np.exp(pi))"
return prob[0], v[0]

If you like, you can give me a permission. I can create a bug feature branch and update the above change and other changes so that you can review them.

Thanks

Jianxiong

[suragnair]

@SourKream

Thanks for the observation Jianxiong. Can you please submit a Pull Request for the same.

[SourKream]

Made the suggested fixes. Thanks @jdongca2003. Well spotted.