基於卷積神經網絡的手寫數字識別

如果是爲了學習，可以參考：https://www.cnblogs.com/Ran-Chen/p/9220739.html

 

這裏只做一個問題記錄。

 

基於CNN的MNIST數據集識別。

關於MNIST數據集：http://yann.lecun.com/exdb/mnist/

 

關於導包問題1

import input_data

把input_data複製出來放在當前項目文件下。

 

關於tensorflow導包

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

這樣不會報錯。

 

關於tensorboad，一個可視化工具。

打開cmd，運行下面。最後的代表log路徑。（log是程序運行後保存的）

tensorboard --logdir=C:\Users\Administrator\log

 

全部代碼

import input_data
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

print("start")

#權重初始化
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

#偏置項初始化
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

#卷積
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

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


# get the data source
mnist = input_data.read_data_sets("MNIST/", one_hot=True)

# input image:pixel 28*28 = 784
with tf.name_scope('input'):
    x = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder('float', [None, 10])  # y_ is realistic result

with tf.name_scope('image'):
    x_image = tf.reshape(x, [-1, 28, 28, 1])  # any dim, width, height, channel(depth)
    tf.summary.image('input_image', x_image, 8)

# 卷積層1 the first convolution layer
with tf.name_scope('conv_layer1'):
    W_conv1 = weight_variable([5, 5, 1, 32])  # convolution kernel: 5*5*1, number of kernel: 32
    b_conv1 = bias_variable([32])
    #卷積核與輸入的x_image進行卷積，並通過relu激活函數，再最大池化處理
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  # make convolution, output: 28*28*32

#最大池化
with tf.name_scope('pooling_layer'):
    h_pool1 = max_pool_2x2(h_conv1)  # make pooling, output: 14*14*32

# 卷積層2 the second convolution layer
with tf.name_scope('conv_layer2'):
    W_conv2 = weight_variable([5, 5, 32, 64])  # convolution kernel: 5*5, depth: 32, number of kernel: 64
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  # output: 14*14*64

with tf.name_scope('pooling_layer'):
    h_pool2 = max_pool_2x2(h_conv2)  # output: 7*7*64


# 全連接層1 the first fully connected layer
with tf.name_scope('fc_layer3'):
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])  # size: 1*1024
    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)  # output: 1*1024

# dropout
with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    tf.summary.scalar('dropout_keep_probability', keep_prob)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


# the second fully connected layer
# train the model: y = softmax(x * w + b)
with tf.name_scope('output_fc_layer4'):
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])  # size: 1*10

with tf.name_scope('softmax'):
    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)  # output: 1*10

with tf.name_scope('lost'):
    cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
    tf.summary.scalar('lost', cross_entropy)

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    tf.summary.scalar('accuracy', accuracy)

merged = tf.summary.merge_all()
train_summary = tf.summary.FileWriter(r'./log', tf.get_default_graph())

# init all variables
init = tf.global_variables_initializer()

# run session
with tf.Session() as sess:
    sess.run(init)
    # train data: get w and b
    for i in range(2000):  # train 2000 times
        batch = mnist.train.next_batch(50)

        result, _ = sess.run([merged, train_step], feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
        # train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

        if i % 100 == 0:
            # train_accuracy = sess.run(accuracy, feed_dict)
            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})  # no dropout
            print('step %d, training accuracy %g' % (i, train_accuracy))

            # result = sess.run(merged, feed_dict={x: batch[0], y_: batch[1]})
            train_summary.add_summary(result, i)

    train_summary.close()

    print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))


#  open tensor_board in windows-cmd
#  tensorboard --logdir=C:\Users\Administrator\tf