TF：基于CNN(2+1)实现MNIST手写数字图片识别准确率提高到99%

TF：基于CNN(2+1)实现MNIST手写数字图片识别准确率提高到99%

导读
与Softmax回归模型相比，使用两层卷积的神经网络模型借助了卷积的威力，准确率高非常大的提升。

目录

输出结果

代码实现

输出结果

Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
 
step 0, training accuracy 0.1
step 1000, training accuracy 0.98
step 2000, training accuracy 0.96
step 3000, training accuracy 1
step 4000, training accuracy 1
step 5000, training accuracy 0.98
step 6000, training accuracy 0.98
step 7000, training accuracy 1
step 8000, training accuracy 1
step 9000, training accuracy 1
step 10000, training accuracy 1
step 11000, training accuracy 1
step 12000, training accuracy 1
step 13000, training accuracy 0.98
step 14000, training accuracy 1
step 15000, training accuracy 1
step 16000, training accuracy 1
step 17000, training accuracy 1
step 18000, training accuracy 1
step 19000, training accuracy 1

代码实现

TF：基于CNN实现MNIST手写数字识别准确率提高到99%
 
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
 
……
 
if __name__ == '__main__':
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    x = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])
    x_image = tf.reshape(x, [-1, 28, 28, 1])  x_image就是输入的训练图像
 
 
    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)  是真正进行卷积计算，再选用ReLU作为激活函数
    h_pool1 = max_pool_2x2(h_conv1)  调用函数max_pool_2x2 进行一次池化操作。
 
  
    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)
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2  
 
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
 
 
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    sess = tf.InteractiveSession()
    sess.run(tf.global_variables_initializer())
 
 
    for i in range(20000):   训练20000步
        batch = mnist.train.next_batch(50)
         每100步报告一次在验证集上的准确度
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x: batch[0], y_: batch[1], keep_prob: 1.0})
            print("step %d, training accuracy %g" % (i, train_accuracy))
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    print("test accuracy %g" % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

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TF：基于CNN(2+1)实现MNIST手写数字识别准确率提高到99%