VGG網絡模型

import tensorflow as tf

#卷積函數
def conv2d(x, W, b, strides=1):
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x)
#池化函數
def maxpool2d(x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
                          padding='SAME')

#權重參數
weights = {
    # 3x3 conv, 3 input, 24 outputs
    'wc1': tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc2': tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),

    'wc3': tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 128], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc4': tf.Variable(tf.truncated_normal(shape=[3, 3, 128, 128], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),

    'wc5': tf.Variable(tf.truncated_normal(shape=[3, 3, 128, 256], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc6': tf.Variable(tf.truncated_normal(shape=[3, 3, 256, 256], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc7': tf.Variable(tf.truncated_normal(shape=[3, 3, 256, 256], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),

    'wc8': tf.Variable(tf.truncated_normal(shape=[3, 3, 256, 512], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc9': tf.Variable(tf.truncated_normal(shape=[3, 3, 512, 512], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc10':tf.Variable(tf.truncated_normal(shape=[3, 3, 512, 512], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),

    'wc11': tf.Variable(tf.truncated_normal(shape=[3, 3, 512, 512], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc12': tf.Variable(tf.truncated_normal(shape=[3, 3, 512, 512], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wc13': tf.Variable(tf.truncated_normal(shape=[3, 3, 512, 512], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    # fully connected, 32*32*96 inputs, 1024 outputs
    'wd1': tf.Variable(tf.truncated_normal(shape=[4 * 4 * 512, 1024], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    'wd2': tf.Variable(tf.truncated_normal(shape=[1024, 1024], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32),
    # 1024 inputs, 10 outputs (class prediction)
    'out': tf.Variable(tf.truncated_normal(shape=[1024, 10], stddev=0.1, dtype=tf.float32),name='weights', dtype=tf.float32)
}

#偏置參數
biases = {
    'bc1': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[64]),name='biases', dtype=tf.float32),
    'bc2': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[64]),name='biases', dtype=tf.float32),
    'bc3': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),name='biases', dtype=tf.float32),
    'bc4': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),name='biases', dtype=tf.float32),
    'bc5': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[256]),name='biases', dtype=tf.float32),
    'bc6': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[256]),name='biases', dtype=tf.float32),
    'bc7': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[256]),name='biases', dtype=tf.float32),
    'bc8': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[512]),name='biases', dtype=tf.float32),
    'bc9': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[512]),name='biases', dtype=tf.float32),
    'bc10': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[512]),name='biases', dtype=tf.float32),
    'bc11': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[512]),name='biases', dtype=tf.float32),
    'bc12': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[512]),name='biases', dtype=tf.float32),
    'bc13': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[512]),name='biases', dtype=tf.float32),
    'bd1': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[1024]),name='biases', dtype=tf.float32),
    'bd2': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[1024]),name='biases', dtype=tf.float32),
    'out': tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[10]),name='biases', dtype=tf.float32)
}

#VGG模型
def inference(images, weights,biases, dropout):
    with tf.name_scope('conv1'):
        conv1 = conv2d(images, weights['wc1'], biases['bc1'])
    with tf.name_scope('conv2'):
        conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    with tf.name_scope('pool1'):
        pool1 = maxpool2d(conv2, k=2)
    print('pool1尺寸',pool1.shape)

    with tf.name_scope('conv3'):
        conv3 = conv2d(pool1, weights['wc3'], biases['bc3'])
    with tf.name_scope('conv4'):
        conv4 = conv2d(conv3, weights['wc4'], biases['bc4'])
    with tf.name_scope('pool2'):
        pool2 = maxpool2d(conv4, k=2)
    print('pool2尺寸',pool2.shape)

    with tf.name_scope('conv5'):
        conv5 = conv2d(pool2, weights['wc5'], biases['bc5'])
    with tf.name_scope('conv6'):
        conv6 = conv2d(conv5, weights['wc6'], biases['bc6'])
    with tf.name_scope('conv7'):
        conv7 = conv2d(conv6, weights['wc7'], biases['bc7'])
    with tf.name_scope('pool3'):
        pool3 = maxpool2d(conv7, k=2)
    print('pool3尺寸', pool3.shape)

    with tf.name_scope('conv8'):
        conv8 = conv2d(pool3, weights['wc8'], biases['bc8'])
    with tf.name_scope('conv9'):
        conv9 = conv2d(conv8, weights['wc9'], biases['bc9'])
    with tf.name_scope('conv10'):
        conv10 = conv2d(conv9, weights['wc10'], biases['bc10'])
    with tf.name_scope('pool4'):
        pool4 = maxpool2d(conv10, k=2)
    print('pool4尺寸', pool4.shape)

    with tf.name_scope('conv11'):
        conv11 = conv2d(pool4, weights['wc11'], biases['bc11'])
    with tf.name_scope('conv12'):
        conv12 = conv2d(conv11, weights['wc12'], biases['bc12'])
    with tf.name_scope('conv13'):
        conv13 = conv2d(conv12, weights['wc13'], biases['bc13'])
    with tf.name_scope('pool5'):
    pool5 = maxpool2d(conv13, k=2)
    print('pool5尺寸', pool5.shape)

    #用1*1的卷積代替全連接層
    with tf.name_scope('fc1'):
        fc1 = tf.reshape(pool5, [-1, weights['wd1'].get_shape().as_list()[0]])
        fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
        fc1 = tf.nn.relu(fc1)
    with tf.name_scope('dropout1'):
        fc1 = tf.nn.dropout(fc1, dropout)

    with tf.name_scope('fc2'):
        fc2 = tf.add(tf.matmul(fc1, weights['wd2']), biases['bd2'])
        fc2 = tf.nn.relu(fc2)
    with tf.name_scope('dropout2'):
        fc2 = tf.nn.dropout(fc2, dropout)

    with tf.name_scope('fc3'):
        out = tf.add(tf.matmul(fc2, weights['out']), biases['out'])
    return out

#softmax損失函數
def losses(logits, labels):
    with tf.name_scope('loss'):
        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('loss' + '/loss', loss)
    return loss

#Adam梯度下降
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

#預測準確度
def evaluation(logits, labels):
    with tf.name_scope('accuracy'):
        correct = tf.nn.in_top_k(logits, labels, 1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.summary.scalar('accuracy' + '/accuracy', accuracy)
    return accuracy