Tensorflow：形状必须等于等级，但对于tf.norm来说形状必须为4和1，

Question

I'm trying to find the norm all the of filters in a conv2d layer. Please find the code for the same below

conv1 = tf.layers.conv2d(
                         inputs=input_layer,
                         filters=32,
                         strides=(1, 1),
                         kernel_size=[3, 3],
                         padding="valid",
                         activation=tf.nn.relu,
                         use_bias=True,
                         kernel_regularizer=tf.nn.l2_loss,
                         bias_regularizer=tf.nn.l2_loss,
                         name="conv1")

var = [v for v in tf.trainable_variables() if "conv1" in v.name]
print(tf.norm(var,axis=4))

Shapes must be equal rank, but are 4 and 1 From merging shape 0 with other shapes. for 'norm/packed' (op: 'Pack') with input shapes: [3,3,3,32], [32].

I have tried with multiple axis values from "None to 4" and none work. Can someone explain what is the problem and how can it be solved?

Answer 1

There are two bugs in your code. One is that var contains a list of tensors while tf.norm() expects just one tensor. Furthermore, the weights have a dimension of 4 and dimensions are numbered from 0, so the fourth dimension's axis is 3. This code (tested):

import tensorflow as tf

input_layer = tf.random_uniform( shape = ( 2, 10, 10, 2 ) )

conv1 = tf.layers.conv2d(
                         inputs=input_layer,
                         filters=32,
                         strides=(1, 1),
                         kernel_size=[3, 3],
                         padding="valid",
                         activation=tf.nn.relu,
                         use_bias=True,
                         kernel_regularizer=tf.nn.l2_loss,
                         bias_regularizer=tf.nn.l2_loss,
                         name="conv1")

var = [v for v in tf.trainable_variables() if "conv1" in v.name][ 0 ]
print( tf.norm( var, axis = 3 ) )

will output:

Tensor("norm/Squeeze:0", shape=(3, 3, 2), dtype=float32)

with no error.