TF：TF下CNN实现mnist数据集预测 96%采用placeholder用法+2层C及其max_pool法+隐藏层dropout法+输出层softmax法+目标函数cross_entropy法+AdamOptimizer算法

时间：2022-09-12 22:31:19
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):

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

def max_pool_2x2(x):

    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

xs = tf.placeholder(tf.float32, [None, 784]) # 28x28

ys = tf.placeholder(tf.float32, [None, 10])

keep_prob = tf.placeholder(tf.float32)

x_image = tf.reshape(xs, [-1, 28, 28, 1])  

## conv1 layer；

W_conv1 = weight_variable([5,5, 1,32])

b_conv1 = bias_variable([32])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

h_pool1 = max_pool_2x2(h_conv1)                        

W_conv2 = weight_variable([5,5, 32, 64])

b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

h_pool2 = max_pool_2x2(h_conv2)                          

W_fc1 = weight_variable([7*7*64, 1024])

b_fc1 = bias_variable([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)

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)              

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]))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)    

sess = tf.Session()

# important step

sess.run(tf.global_variables_initializer())

for i in range(10):

    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, mnist.test.labels))

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