model.py

import tensorflow as tf

image_size = 32
input_images = tf.placeholder(tf.float32,
                              shape=[None, image_size, image_size, 3], 
                              name="input_images")

# First CONV layer
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96],
                                         dtype=tf.float32,
                                         stddev=1e-1),
                     name="conv1_weights")
conv = tf.nn.conv2d(input_images, kernel, [1, 4, 4, 1], padding="SAME")
bias = tf.Variable(tf.truncated_normal([96]))
conv_with_bias = tf.nn.bias_add(conv, bias)
conv1 = tf.nn.relu(conv_with_bias, name="conv1")