How to visualize learned filters on tensorflow

Question

Similarly to the Caffe framework, where it is possible to watch the learned filters during CNNs training and it's resulting convolution with input images, I wonder if is it possible to do the same with TensorFlow?

A Caffe example can be viewed in this link:

http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb

Grateful for your help!

Answer 1

To see just a few conv1 filters in Tensorboard, you can use this code (it works for cifar10)

# this should be a part of the inference(images) function in cifar10.py file

# conv1
with tf.variable_scope('conv1') as scope:
  kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64],
                                       stddev=1e-4, wd=0.0)
  conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
  biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
  bias = tf.nn.bias_add(conv, biases)
  conv1 = tf.nn.relu(bias, name=scope.name)
  _activation_summary(conv1)

  with tf.variable_scope('visualization'):
    # scale weights to [0 1], type is still float
    x_min = tf.reduce_min(kernel)
    x_max = tf.reduce_max(kernel)
    kernel_0_to_1 = (kernel - x_min) / (x_max - x_min)

    # to tf.image_summary format [batch_size, height, width, channels]
    kernel_transposed = tf.transpose (kernel_0_to_1, [3, 0, 1, 2])

    # this will display random 3 filters from the 64 in conv1
    tf.image_summary('conv1/filters', kernel_transposed, max_images=3)

I also wrote a simple gist to display all 64 conv1 filters in a grid.