# TF Layers Example¶

After a little bit of looking at tf.layers, I have realized that the functionality it implements is very good, but the documentation for it is quite minimal (and leaves lots of gaps). However, looking at the source makes things much clearer.

In fact, there is quite a bit of functionality that is not available to you if you use the functional api from tf.layers. If you instead use the underlying classes:

• tensorflow.python.layers.convolutional.Conv2D
• tensorflow.python.layers.core.Dense
• tensorflow.python.layers.core.Dropout
• tensorflow.python.layers.normalization.BatchNormalization

## Convolution¶

For example, let us define a convolutional layer like so:

import tensorflow as tf, numpy as np
from tensorflow.python.ops import init_ops
from tensorflow.python.layers import convolutional
x = 255 * np.random.rand(1, 50, 50, 3).astype(np.float32)
v = tf.Variable(x)
# Use glorot initialization
init = init_ops.VarianceScaling(scale=1.0, mode='fan_out')
# Use l2 regularization
reg = tf.nn.l2_loss
# Create an object representing the layer
conv_layer = convolutional.Conv2D(
kernel_initializer=init, kernel_regularizer=reg, name='conv')
# Now get the outputs
y = conv_layer.apply(x)


Now, we may want to get the weights that were defined to add some variable summaries, or maybe we want to inspect the losses. Now we can do so by looking at the properties of the conv_layer:

weights = conv_layer.trainable_weights
variables = conv_layer.variables
loss = conv_layer.losses


## Batch Norm¶

I wanted to include an example of batch norm, as there are a few things to be careful about. In particular, the apply method has a parameter training. We can see the importance of this with an example:

import tensorflow as tf, numpy as np
from tensorflow.python.ops import init_ops
from tensorflow.python.layers import normalization
x = 255 * np.random.rand(50, 50, 3).astype(np.float32)
v = tf.Variable(x)

bn_layer1 = normalization.BatchNormalization(name='bn1')
bn_layer2 = normalization.BatchNormalization(name='bn2')
y1 = bn_layer1.apply(v, training=True)
y2 = bn_layer2.apply(v, training=False)

sess = tf.Session()
sess.run(tf.global_variables_initializer())
y1_n, y2_n = sess.run([y1, y2])

print('Input mean and std: {:.2f}, {:.2f}'.format(np.mean(x), np.std(x)))
print('y1 mean and std: {:.2f}, {:.2f}'.format(np.mean(y1_n), np.std(y1_n)))
print('y2 mean and std: {:.2f}, {:.2f}'.format(np.mean(y2_n), np.std(y2_n)))


Will have output:

Input mean and std: 126.41, 74.26
y1 mean and std: -0.00, 1.00
y2 mean and std: 126.34, 74.22


This is because batch norm will subtract the batch mean and divide by the batch standard deviation during training time to approximate an estimate on the population mean and standard deviation. In this case we only had one example, so that meant it got zero centred.

Similarly, for test time, the batch norm layer will want to subtract the population mean and divide by the population standard deviation. When we start training, these values are initialized to 0 and 1 respectively. When training, the moving_mean and moving_variance need to be updated. By default the update ops are placed in tf.GraphKeys.UPDATE_OPS, so they need to be added as a dependency to the train_op. For example:

update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss)