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model.py
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model.py
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# =========================================================================
import tensorflow as tf
# =========================================================================
# 网络结构定义
# 输入参数:images,image batch、4D tensor、tf.float32、[batch_size, width, height, channels]
# 返回参数:logits, float、 [batch_size, n_classes]
def inference(images, batch_size, n_classes):
# 一个简单的卷积神经网络,卷积+池化层x2,全连接层x2,最后一个softmax层做分类。
# 卷积层1
# 64个3x3的卷积核(3通道),padding=’SAME’,表示padding后卷积的图与原图尺寸一致,激活函数relu()
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)
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)
# 池化层1
# 3x3最大池化,步长strides为2,池化后执行lrn()操作,局部响应归一化,对训练有利。
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')
# 卷积层2
# 16个3x3的卷积核(16通道),padding=’SAME’,表示padding后卷积的图与原图尺寸一致,激活函数relu()
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')
# 池化层2
# 3x3最大池化,步长strides为2,池化后执行lrn()操作,
# 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')
# 全连接层3
# 128个神经元,将之前pool层的输出reshape成一行,激活函数relu()
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)
# 全连接层4
# 128个神经元,激活函数relu()
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层
# with tf.variable_scope('dropout') as scope:
# drop_out = tf.nn.dropout(local4, 0.8)
# Softmax回归层
# 将前面的FC层输出,做一个线性回归,计算出每一类的得分
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计算
# 传入参数:logits,网络计算输出值。labels,真实值,在这里是0或者1
# 返回参数:loss,损失值
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
# --------------------------------------------------------------------------
# loss损失值优化
# 输入参数:loss。learning_rate,学习速率。
# 返回参数:train_op,训练op,这个参数要输入sess.run中让模型去训练。
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
# -----------------------------------------------------------------------
# 评价/准确率计算
# 输入参数:logits,网络计算值。labels,标签,也就是真实值,在这里是0或者1。
# 返回参数:accuracy,当前step的平均准确率,也就是在这些batch中多少张图片被正确分类了。
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