Tensorflow visualize convolutions

conv_summary.py

channels = 32
img_size = 128

W_conv1 = weight_variable([5, 5, 1, channels])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1))
# Produces a tensor of size [-1, img_size, img_size, channels]

## Prepare for visualization
# Take only convolutions of first image, discard convolutions for other images.
V = tf.slice(h_conv1, (0, 0, 0, 0), (1, -1, -1, -1), name='slice_first_input')
V = tf.reshape(V, (img_size, img_size, channels))

# Reorder so the channels are in the first dimension, x and y follow.
V = tf.transpose(V, (2, 0, 1))
# Bring into shape expected by image_summary
V = tf.reshape(V, (-1, img_size, img_size, 1))

tf.image_summary("first_conv", V)

Just an update: use tf.summary.image("first_conv", V) instead of tf.image_summary("first_conv", V). The code will be

channels = 32
img_size = 128
W_conv1 = weight_variable([5, 5, 1, channels])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1))
V = tf.slice(h_conv1, (0, 0, 0, 0), (1, -1, -1, -1), name='slice_first_input')
V = tf.reshape(V, (img_size, img_size, channels))
V = tf.transpose(V, (2, 0, 1))
V = tf.reshape(V, (-1, img_size, img_size, 1))
tf.summary.image("first_conv", V)