def inference(input_tensor, train, regularizer):
    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, [(- 1), 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.matmul(fc1, fc2_weights) + fc2_biases)
    return logit
def inference(input_tensor, train, regularizer):
    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, [(- 1), 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.matmul(fc1, fc2_weights) + fc2_biases)
    return logit