VGG網路模型

import tensorflow as tf

#卷積函式
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)
#池化函式
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='same')
#權重引數
weights = 
#偏置引數
biases = 
#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的卷積代替全連線層
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損失函式
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
#**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

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