resnet.py

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def resUnit(input_layer,i):
    with tf.variable_scope("res_unit"+str(i)):
        part1 = slim.batch_norm(input_layer,activation_fn=None)
        part2 = tf.nn.relu(part1)
        part3 = slim.conv2d(part2,64,[3,3],activation_fn=None)
        part4 = slim.batch_norm(part3,activation_fn=None)
        part5 = tf.nn.relu(part4)
        part6 = slim.conv2d(part5,64,[3,3],activation_fn=None)
        output = input_layer + part6
        return output

tf.reset_default_graph()

input_layer = tf.placeholder(shape=[None,32,32,3],dtype=tf.float32,name='input')
label_layer = tf.placeholder(shape=[None],dtype=tf.int32)
label_oh = slim.layers.one_hot_encoding(label_layer,10)

layer1 = slim.conv2d(input_layer,64,[3,3],normalizer_fn=slim.batch_norm,scope='conv_'+str(0))
for i in range(5):
    for j in range(units_between_stride):
        layer1 = resUnit(layer1,j + (i*units_between_stride))
    layer1 = slim.conv2d(layer1,64,[3,3],stride=[2,2],normalizer_fn=slim.batch_norm,scope='conv_s_'+str(i))
    
top = slim.conv2d(layer1,10,[3,3],normalizer_fn=slim.batch_norm,activation_fn=None,scope='conv_top')

output = slim.layers.softmax(slim.layers.flatten(top))

loss = tf.reduce_mean(-tf.reduce_sum(label_oh * tf.log(output) + 1e-10, reduction_indices=[1]))
trainer = tf.train.AdamOptimizer(learning_rate=0.001)
update = trainer.minimize(loss)