model.py

import tensorflow as tf   
def inference(images, batch_size, n_classes):  
    with tf.variable_scope('conv1') as scope:  
          
        weights = tf.Variable(tf.truncated_normal(shape=[3,3,3,64], stddev = 1.0, dtype = tf.float32),   
                              name = 'weights', dtype = tf.float32)  
          
        biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [64]),  
                             name = 'biases', dtype = tf.float32)  
#conv1          
        conv = tf.nn.conv2d(images, weights, strides=[1,1,1,1], padding='SAME')  
        pre_activation = tf.nn.bias_add(conv, biases)  
        conv1 = tf.nn.relu(pre_activation, name= scope.name)  
          
#pool1  
    with tf.variable_scope('pooling1_lrn') as scope:  
        pool1 = tf.nn.max_pool(conv1, ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME', name='pooling1')  
        norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1')  
  
#conv2  
    with tf.variable_scope('conv2') as scope:  
        weights = tf.Variable(tf.truncated_normal(shape=[3,3,64,16], stddev = 0.1, dtype = tf.float32),   
                              name = 'weights', dtype = tf.float32)  
          
        biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [16]),  
                             name = 'biases', dtype = tf.float32)  
          
        conv = tf.nn.conv2d(norm1, weights, strides = [1,1,1,1],padding='SAME')  
        pre_activation = tf.nn.bias_add(conv, biases)  
        conv2 = tf.nn.relu(pre_activation, name='conv2')  
  
#pool2  
    with tf.variable_scope('pooling2_lrn') as scope:  
        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001/9.0,beta=0.75,name='norm2')  
        pool2 = tf.nn.max_pool(norm2, ksize=[1,3,3,1], strides=[1,1,1,1],padding='SAME',name='pooling2')  
  
#fc1  
    with tf.variable_scope('local3') as scope:  
        reshape = tf.reshape(pool2, shape=[batch_size, -1])  
        dim = reshape.get_shape()[1].value  
        weights = tf.Variable(tf.truncated_normal(shape=[dim,128], stddev = 0.005, dtype = tf.float32),  
                             name = 'weights', dtype = tf.float32)  
          
        biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [128]),   
                             name = 'biases', dtype=tf.float32)  
          
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)  
          
#fc2   
    with tf.variable_scope('local4') as scope:  
        weights = tf.Variable(tf.truncated_normal(shape=[128,128], stddev = 0.005, dtype = tf.float32),  
                              name = 'weights',dtype = tf.float32)  
          
        biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [128]),  
                             name = 'biases', dtype = tf.float32)  
          
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')  
  
#dropout          
#    with tf.variable_scope('dropout') as scope:  
#        drop_out = tf.nn.dropout(local4, 0.8)  
              
          
#softmax  
    with tf.variable_scope('softmax_linear') as scope:  
        weights = tf.Variable(tf.truncated_normal(shape=[128, n_classes], stddev = 0.005, dtype = tf.float32),  
                              name = 'softmax_linear', dtype = tf.float32)  
          
        biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [n_classes]),  
                             name = 'biases', dtype = tf.float32)  
          
        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')  
  
    return softmax_linear  
  
#-----------------------------------------------------------------------------  
#loss  
def losses(logits, labels):  
    with tf.variable_scope('loss') as scope:  
        cross_entropy =tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='xentropy_per_example')  
        loss = tf.reduce_mean(cross_entropy, name='loss')  
        tf.summary.scalar(scope.name+'/loss', loss)  
    return loss  
  
#--------------------------------------------------------------------------  
#training  
def trainning(loss, learning_rate):  
    with tf.name_scope('optimizer'):  
        optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate)  
        global_step = tf.Variable(0, name='global_step', trainable=False)  
        train_op = optimizer.minimize(loss, global_step= global_step)  
    return train_op  
  
#-----------------------------------------------------------------------  
#evaluation
def evaluation(logits, labels):  
    with tf.variable_scope('accuracy') as scope:  
        correct = tf.nn.in_top_k(logits, labels, 1)  
        correct = tf.cast(correct, tf.float16)  
        accuracy = tf.reduce_mean(correct)  
        tf.summary.scalar(scope.name+'/accuracy', accuracy)  
    return accuracy