Tensorflow入门到实战五(卷积神经网络)

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

参数:
**input : ** 输入的要做卷积的图片,要求为一个张量,shape为 [ batch, in_height, in_weight, in_channel ],其中batch为图片的数量,in_height 为图片高度,in_weight 为图片宽度,in_channel 为图片的通道数,灰度图该值为1,彩色图为3。(也可以用其它值,但是具体含义不是很理解)
filter: 卷积核,要求也是一个张量,shape为 [ filter_height, filter_weight, in_channel, out_channels ],其中 filter_height 为卷积核高度,filter_weight 为卷积核宽度,in_channel 是图像通道数 ,和 input 的 in_channel 要保持一致,out_channel 是卷积核数量。
strides: 卷积时在图像每一维的步长,这是一个一维的向量,[ 1, strides, strides, 1],第一位和最后一位固定必须是1
padding: string类型,值为“SAME” 和 “VALID”,表示的是卷积的形式,是否考虑边界。"SAME"是考虑边界,不足的时候用0去填充周围,"VALID"则不考虑
use_cudnn_on_gpu: bool类型,是否使用cudnn加速,默认为true

最近在用tensorflow搭建卷积神经网络,遇到了一个比较棘手的问题,我一直理解的padding有两个值,一个是SAME,一个是VALID,如果padding设置为SAME,则说明输入图片大小和输出图片大小是一致的,如果是VALID则图片经过滤波器后可能会变小。 

 1.VALID模式

    输出的宽和高为

o_w = (i_w - f_w + 1)/ s_w #(结果向上取整)
o_h = (i_h - f_h + 1)/ s_h  #(结果向上取整)

2. SAME模式

    输出的宽和高为

o_w = i_w / s_w#(结果向上取整)
o_h = i_h / s_h#(结果向上取整)
inputs = tf.Variable(tf.random_normal([1,5,5,3]))
filters = tf.Variable(tf.random_normal([3,3,3,7]))

result = tf.nn.conv2d(inputs,filters,strides=[1,2,2,1],padding='SAME')
init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)
print(result.shape)

(1, 3, 3, 7)
inputs = tf.Variable(tf.random_normal([1,5,5,3]))
filters = tf.Variable(tf.random_normal([3,3,3,7]))

result = tf.nn.conv2d(inputs,filters,strides=[1,2,2,1],padding='VALID')
init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)
print(result.shape)
(1, 2, 2, 7)

卷积神经网络:

"""
卷积神经网络
两个卷积层,两个全连接层
输入 [sample * 28 * 28 * 1 ] (灰度图)
[ 28 * 28 *1 ]  --> (32个卷积核,每个大小5*5*1,sample方式卷积) --> [ 28 * 28 * 32] --> (池化 2*2 ,步长2)--> [14 *14 *32]
[ 14 * 14 *32]  --> (64个卷积核,每个大小  5 * 5 * 32,sample方式卷积) -->  [14 * 14 *64] --> (池化 2*2 ,步长2)--> [7 * 7 *64] 
[ 7 * 7 * 64] --> reshape 成列向量 --> (7 * 7 * 64)
[sample * (7*7*64)]  全连接层1 weights:[7*7*64 , 1024]  --> [sample * 1024]
[sample * 1024]      全连接层2 weights:[1024,10]        -->  [sample *10]
输出:10个分类
"""

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
    return result

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    # stride [1, x_movement, y_movement, 1]
    # Must have strides[0] = strides[3] = 1
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    # stride [1, x_movement, y_movement, 1]
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784])/255.   # 28x28
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)

#把xs的形状变成[-1,28,28,1],-1代表先不考虑输入的图片例子多少这个维度,后面的1是channel的数量
#因为我们输入的图片是黑白的,因此channel是1,例如如果是RGB图像,那么channel就是3。
x_image = tf.reshape(xs, [-1, 28, 28, 1])
# print(x_image.shape)  # [n_samples, 28,28,1]

#建立卷积层
## 本层我们的卷积核patch的大小是5x5,因为黑白图片channel是1所以输入是1,输出是32个featuremap ??? 32 是什么意思?
W_conv1 = weight_variable([5,5, 1,32])  # patch 5x5, in size 1, out size 32
b_conv1 = bias_variable([32])
#卷积运算
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)                                         # output size 14x14x32


W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64   ??? 64 是什么意思?
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)                                         # output size 7x7x64

#全连接层
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

## fc2 layer ##
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)


# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1]))       # loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()


if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
    init = tf.initialize_all_variables()
else:
    init = tf.global_variables_initializer()
sess.run(init)

# 原来是1000 为了快一点 改成了1000
for i in range(500):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images[:1000], mnist.test.labels[:1000]))

 

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