model.py

import tensorflow as tf


def inference(images, batch_size, n_classes):
    """

    :param images: image batch , 4D tensor, tf.float32, [batch_size, width, height, channels]
    :param batch_size:
    :param n_classes: 2 airport and port
    :return: output tensor with the computed logits , float, [batch_size, n_class]
    """

    #  conv1, shape = [kernel size, kernel size, channels, kernel numbers]

    with tf.variable_scope('conv1') as scope:
        weights =tf.get_variable('weight',
                                 shape=[3, 3, 3, 16], # 卷积核大小为3*3 深度为3 一共16个
                                 dtype=tf.float32,
                                 initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[16],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        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 and norm1
    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.get_variable('weight',
                                  shape=[3, 3, 16, 16],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))

        biases = tf.get_variable('biases', shape=[16],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        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 and norm2
    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')

    # local3
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.get_variable('weights',
                                  shape=[dim, 128],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases', shape=[128],
                                 dtype= tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)

    # local4
    with tf.variable_scope('local4') as scope:
        weights = tf.get_variable('weights',
                                  shape=[128, 128],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev= 0.005, dtype=tf.float32))
        biases = tf.get_variable('biases', shape=[128],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)

    # softmax
    with tf.variable_scope('softmax_linear') as scope:
        weights = tf.get_variable('softmax_linear',
                                  shape=[128, n_classes],
                                  dtype= tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases', shape=[n_classes],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0, 1))
        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)

    return softmax_linear


def losses(logits, label):
    """

    :param logits: logits tensor, float, [batch_size, n_classses]
    :param label: label tensor, tf.int32, [batch_size]
    :return: loss tensor of float type

    """
    with tf.variable_scope('loss') as scope:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits\
            (logits=logits, labels=label, name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.summary.scalar(scope.name+'/loss', loss)
    return loss


def trainning(loss, learning_rate):
    """
    Training ops , the Op returned by this function is what must be passed to
    'sess.run()' call to cause the odel to train
    :param loss: loss tensor from losses()
    :param learning_rate:
    :return:trainning op for training
    """
    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


def evaluation(logits, labels):
    """

    :param logits: Logits tensor, float [batch_size, num_classes]
    :param labels: Label tensor in32
    :return:  Ascalar int32 tensor with the number of examples(out of batch_size) that were predicted correctly
    """
    with tf.variable_scope('evaluation') 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