with tf.variable_scope('layer1-conv1'):

conv1_weights = tf.get_variable("weight",[CONV1_SIZE,CONV1_SIZE,NUM_CHANNELS,CONV1_DEEP],initializer=tf.truncated_normal_initializer(stddev=0.1))

conv1_biases = tf.get_variable("bias",[CONV1_DEEP],initializer=tf.constant_initializer(0.0))

conv1 = tf.nn.conv2d(input_tensor,conv1_weights,strides=[1,1,1,1],padding='SAME')

relu1 = tf.nn.relu(tf.nn.bias_add(conv1,conv1_biases))

with tf.name_scope('layer2-pool1'):

pool1 = tf.nn.max_pool(relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

with tf.variable_scope('layer3-conv2'):

conv2_weights = tf.get_variable("weight", [CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP],

initializer=tf.truncated_normal_initializer(stddev=0.1))

conv2_biases = tf.get_variable("bias", [CONV2_DEEP], initializer=tf.constant_initializer(0.0))

conv2 = tf.nn.conv2d(pool1, conv2_weights,strides=[1,1,1,1], padding='SAME')

relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))

with tf.name_scope('layer4-pool2'):

pool2 = tf.nn.max_pool(relu2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

pool_shape = pool2.get_shape().as_list()

nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]

reshaped = tf.reshape(pool2,[pool_shape[0],nodes])

with tf.variable_scope('layer5-fc1'):

fc1_weights = tf.get_variable("weight", [nodes,FC_SIZE],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None:

tf.add_to_collection('losses',regularizer(fc1_weights))

fc1_biases = tf.get_variable("bias", [FC_SIZE], initializer=tf.constant_initializer(0.1))

fc1 = tf.nn.relu(tf.matmul(reshaped,fc1_weights) + fc1_biases)

if train:

fc1 = tf.nn.dropout(fc1,0.5)

with tf.variable_scope('layer6-fc2'):

fc2_weights = tf.get_variable("weight", [FC_SIZE, NUM_LABELS],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None:

tf.add_to_collection('losses', regularizer(fc2_weights))

fc2_biases = tf.get_variable("bias", [NUM_LABELS], initializer=tf.constant_initializer(0.1))

logit = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)