Tensorflow学习记录9--alexnet网络

alexnet总结

总共八层，作者说层数越多应该效果会更好，但是考虑GPU的计算能力整成八层，作者说任意减掉一层都会是识别效果下降，前俩层有卷积层，局部激活响应归一化lrn，激活函数relu以及池化层，中间俩层只有卷积和激活函数relu，第五层的卷积层只有卷积和池化层，没有lrn和relu，第六七八层又全都是全链接层，最后一层的输出为1000个神经元。
其中使用了relu，dropout，lrn，image patch，pca等方法来优化结果。

具体网络结构如下：

#conv1 (Convolution)kernel size:11 stride:4 pad:0 out_layer:96
#lrn
#relu
#pool1(MAX Pooling)kernel size :3 stride:2 pad:0

#conv2 (Convolution)kernel size:5 stride:1 pad:2 out_layer:256
#lrn
#relu
#pool2(MAX Pooling)kernel size :3 stride:2 pad:0

#conv3(Convolution)kernel size:3 stride:1 pad:1 out_layer:384

#conv4(Convolution)kernel size:3 stride:1 pad:1 out_layer:384

#conv5(Convolution)kernel size:3 stride:1 pad:1 out_layer:256
#pool5(MAX Pooling)kernel size:3 stride:2 pad:0

#fc6 
#relu6
#drop6 out 4096

#fc7
#relu7
#drop7 out 4096

#fc8 out 1000

疑问：
1. 输出层数跟其他东西有关系吗？？步长

注意事项

1 卷积需要设置的参数

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)

1. W也就是filter
   举个例子:
   W=(11,11,3,96),11*11为卷积滤波器的高和宽，3为输入的维数，96为输出的维数,96可以理解为有96个这样，同样大小的滤波器，

2. Strides
   一般这么设置，strides=[1, k, k, 1]，代表以k为步长

3. padding

1. “SAME”, “VALID”了俩个选项
2. SAME那么卷积后生成的图像大小为 W/k向上取整，即还有剩下的就补全，k为步长，也可以这样理解，如果能够整除，也加一，如果不能整除，反正也要加一，所以可以这么写。
3. VALID代表不会再原有的输入的基础上再添加新的像素，卷积后生成的图像大小为(w-f+1)/k向上取整,也可以等于(w-f)/k + 1,可以理解为最后一个填满了减掉11,前面除以步长则为前面的个数，最后再加一则为输出，自行体会哈哈哈

1. 实例
   假设输入图像为224×224×3,即为input
   W=(11,11,3,96)
   strides=[1, 4, 4, 1]
   padding=same
   这样，输出的矩形框的大小就为55×55,227/4 向上取整。

2 ReLUs与Local Response Normalization

收敛效果比tanh，sigmoid快，并且不容易梯度消失，但是，使用ReLU f(x)=max(0,x)后，激活函数后的值没有了区域空间，一般在Relu之后会做一个normalization。思想是找周围n个核的激活函数的值做归一化。具体如下图：
未添加图

3 pooling注意事项

tf.nn.max_pool(value, ksize, strides, padding, data_format=’NHWC’, name=None)
1. value,输入，shape为[batch,height,width,channels]
2. ksize=[1,k_h,k_w,1],pooling核的大小
3. stride=[1,s_h,s_w,1]
4. pading=padding,与上面一致
5. data_format

1. 有NHWC，NCHW俩种选择。
2. NHWC:[batch, in_height, in_width, in_channels]
3. NCHW:[batch, in_channels, in_height, in_width]

4 数据处理

image patch

为了防止过拟合，最简单的方式是增加数据，patch也可以用来扩展数据集。
从256×256中随机提出224×224的patches，一般的patch的方式有：

1. Flat
2. Linear
3. Quadratic
4. Edged

另一种方式是利用PCA来扩展数据。

下面是一个alexnet网络来训练手写数字识别的例子

# -*- coding: utf-8 -*-
# 输入数据
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

import tensorflow as tf

# 定义网络超参数
learning_rate = 0.001
training_iters = 200000
batch_size = 64
display_step = 20

# 定义网络参数
n_input = 784 # 输入的维度
n_classes = 10 # 标签的维度
dropout = 0.8 # Dropout 的概率

# 占位符输入
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)

# 卷积操作
def conv2d(name, l_input, w, b):
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b), name=name)

# 最大下采样操作
def max_pool(name, l_input, k):
    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)

# 归一化操作
def norm(name, l_input, lsize=4):
    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)

# 定义整个网络 
def alex_net(_X, _weights, _biases, _dropout):
    # 向量转为矩阵
    # 这个是把x的-1维即最后一维，即每一副图像从一维变为28*28*1维的图像
    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])

    # 卷积层
    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
    # 下采样层
    pool1 = max_pool('pool1', conv1, k=2)
    # 归一化层
    norm1 = norm('norm1', pool1, lsize=4)
    # Dropout
    norm1 = tf.nn.dropout(norm1, _dropout)

    # 卷积
    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
    # 下采样
    pool2 = max_pool('pool2', conv2, k=2)
    # 归一化
    norm2 = norm('norm2', pool2, lsize=4)
    # Dropout
    norm2 = tf.nn.dropout(norm2, _dropout)

    # 卷积
    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
    # 下采样
    pool3 = max_pool('pool3', conv3, k=2)
    # 归一化
    norm3 = norm('norm3', pool3, lsize=4)
    # Dropout
    norm3 = tf.nn.dropout(norm3, _dropout)

    # 全连接层，先把特征图转为向量
    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) 
    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1') 
    # 全连接层
    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation

    # 网络输出层
    out = tf.matmul(dense2, _weights['out']) + _biases['out']
    return out

# 存储所有的网络参数
weights = {
    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),
    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),
    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),
    'wd1': tf.Variable(tf.random_normal([4096, 1024])),
    #'wd1': tf.Variable(tf.random_normal([4\*4\*256, 1024])),
    'wd2': tf.Variable(tf.random_normal([1024, 1024])),
    'out': tf.Variable(tf.random_normal([1024, 10]))
}
biases = {
    'bc1': tf.Variable(tf.random_normal([64])),
    'bc2': tf.Variable(tf.random_normal([128])),
    'bc3': tf.Variable(tf.random_normal([256])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'bd2': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# 构建模型
pred = alex_net(x, weights, biases, keep_prob)

# 定义损失函数和学习步骤
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 测试网络
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 初始化所有的共享变量
init = tf.initialize_all_variables()

# 开启一个训练
with tf.Session() as sess:
    sess.run(init)
    step = 1
    # Keep training until reach max iterations
    #while step \* batch_size < training_iters:
    while step * batch_size < training_iters:
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # 获取批数据
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
        if step % display_step == 0:
            # 计算精度
            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            # 计算损失值
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            #print "Iter " + str(step\*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
            print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
        step += 1
    print "Optimization Finished!"
    # 计算测试精度
    print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})

input_data代码如下：

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import gzip
import os
import tempfile

import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets