自从我会用第一个神经网络以后，我第一次吧数据送入网络进行训练，获得了成就感，今天我又一次在原来的基础上，改写了网络模型，现在我可以在Alexnet上运行自己的网络结构了，
Alexnet的网络结构
数据预处理和之前的都一样，可以参考上一篇 的模型，下面的这篇模型直接改写网络的inference结构就可以了：
#=========================================================================
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，**函数relu()
parameters = []
# 第1个卷积层
with tf.name_scope('conv1') as scope:
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope)
print_activations(conv1)
parameters += [kernel, biases]
# 添加LRN层和最大池化层
lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='lrn1')
pool1 = tf.nn.max_pool(lrn1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool1')
print_activations(pool1)
# 设计第2个卷积层
with tf.name_scope('conv2') as scope:
kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[192], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv2)
# 对第2个卷积层的输出进行处理，同样也是先做LRN处理再做最大化池处理。
lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='lrn2')
pool2 = tf.nn.max_pool(lrn2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool2')
print_activations(pool2)
# 设计第3个卷积层
with tf.name_scope('conv3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv3 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv3)
# 设计第4个卷积层
with tf.name_scope('conv4') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv4 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv4)
# 设计第5个卷积层
with tf.name_scope('conv5') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv5 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv5)
# 最大池化层
pool5 = tf.nn.max_pool(conv5,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool5')
print_activations(pool5)
# return pool5, parameters
#全连接层3
#128个神经元，将之前pool层的输出reshape成一行，**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,4096], stddev = 0.005, dtype = tf.float32),
name = 'weights', dtype = tf.float32)
biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [4096]),
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，**函数relu()
with tf.variable_scope('local4') as scope:
weights = tf.Variable(tf.truncated_normal(shape=[4096,4096], stddev = 0.005, dtype = tf.float32),
name = 'weights',dtype = tf.float32)
biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [4096]),
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，在这里是2类，所以这个层输出的是两个得分。
with tf.variable_scope('softmax_linear') as scope:
weights = tf.Variable(tf.truncated_normal(shape=[4096, 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
#========================================================================
其他部分的代码都不变化，结果训练出来的结果是这样的：
加载一张图片进行测试一番：
这个还需要改很多的参数，我里面也有修改的地方，全连接层用的是4096 图像resize的大小是224*224，剩下的都不需要修改了，好了，今天就讲到这里了。
很开心，就这么莫名其妙的成功了，但是准确率不是很高，在数据预处理的点，就暂用了很多的内存，我都不知道是什么原因。