densenet.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 denseBlock(input_layer,i,j):
    with tf.variable_scope("dense_unit"+str(i)):
        nodes = []
        a = slim.conv2d(input_layer,64,[3,3],normalizer_fn=slim.batch_norm)
        nodes.append(a)
        for z in range(j):
            b = slim.conv2d(tf.concat(3,nodes),64,[3,3],normalizer_fn=slim.batch_norm)
            nodes.append(b)
        return b

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):
    layer1 = denseBlock(layer1,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)

Maybe:
tf.concat(3,nodes)
-->
tf.concat(nodes,3)
?