How do I use tensor board with tf.layers?

Question

As the weights are not explicitly defined, how can I pass them to a summary writer?

For exemple:

conv1 = tf.layers.conv2d(
    tf.reshape(X,[FLAGS.batch,3,160,320]),
    filters = 16,
    kernel_size = (8,8),
    strides=(4, 4),
    padding='same',
    kernel_initializer=tf.contrib.layers.xavier_initializer(),
    bias_initializer=tf.zeros_initializer(),
    kernel_regularizer=None,
    name = 'conv1',
    activation = tf.nn.elu
    )

=>

summarize_tensor(
    ??????
)

Thanks!

Answer 1

While Da Tong's answer is complete, it took me a while to realize how to use it. To save time for another beginner, you need to add the following to you code to add all trainable variables to the tensorboard summary:

for var in tf.trainable_variables():
    tf.summary.histogram(var.name, var)
merged_summary = tf.summary.merge_all()

Answer 2

That depends on what you are going to record in TensorBoard. If you want to put every variables into TensorBoard, call tf.all_variables() or tf.trainable_variables() will give you all the variables. Note that the tf.layers.conv2d is just a wrapper of creating a Conv2D instance and call apply method of it. You can unwrap it like this:

conv1_layer = tf.layers.Conv2D(
    filters = 16,
    kernel_size = (8,8),
    strides=(4, 4),
    padding='same',
    kernel_initializer=tf.contrib.layers.xavier_initializer(),
    bias_initializer=tf.zeros_initializer(),
    kernel_regularizer=None,
    name = 'conv1',
    activation = tf.nn.elu
)

conv1 = conv1_layer.apply(tf.reshape(X,[FLAGS.batch,3,160,320]))

Then you can use conv1_layer.kernel to access the kernel weights.