tensorflow(三)：簡單神經網絡實現手寫體識別MNIST

一、代碼

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 載入數據集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
# 每個批次的大小
batch_size = 100
# 計算一共有多少批次
n_batch = mnist.train.num_examples // batch_size
# 定義兩個placeholder
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
# 創建一個簡單的神經網絡
W_1 = tf.Variable(tf.truncated_normal([784, 2000], stddev=0.1))
b_1 = tf.Variable(tf.zeros([2000]) + 0.1)
L_1 = tf.nn.relu(tf.matmul(x, W_1) + b_1)

W_2 = tf.Variable(tf.truncated_normal([2000,10],stddev=0.1))
b_2 = tf.Variable(tf.zeros([10]) + 0.1)
prediction = tf.nn.softmax(tf.matmul(L_1,W_2) + b_2)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
train_step = tf.train.MomentumOptimizer(0.2,0.9).minimize(loss)

# 初始化變量
init = tf.global_variables_initializer()

# 結果存放在一個布爾型列表中
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))  # argmax返回一維張量中最大的值所在的位置
# 求準確率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(50):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("Iter" + str(epoch) + ",Testing Accuracy " + str(acc))

二、結果

 

Iter0,Testing Accuracy 0.561
Iter1,Testing Accuracy 0.7655
Iter2,Testing Accuracy 0.8711
Iter3,Testing Accuracy 0.8737
Iter4,Testing Accuracy 0.9675
Iter5,Testing Accuracy 0.9727
Iter6,Testing Accuracy 0.9741
Iter7,Testing Accuracy 0.9777
Iter8,Testing Accuracy 0.9783
Iter9,Testing Accuracy 0.9794
Iter10,Testing Accuracy 0.98
Iter11,Testing Accuracy 0.981
Iter12,Testing Accuracy 0.9815
Iter13,Testing Accuracy 0.9822
Iter14,Testing Accuracy 0.9812
Iter15,Testing Accuracy 0.9801
Iter16,Testing Accuracy 0.9811
Iter17,Testing Accuracy 0.9814
Iter18,Testing Accuracy 0.9811
Iter19,Testing Accuracy 0.9807
Iter20,Testing Accuracy 0.9808
Iter21,Testing Accuracy 0.9798
Iter22,Testing Accuracy 0.981
Iter23,Testing Accuracy 0.9809
Iter24,Testing Accuracy 0.9811
Iter25,Testing Accuracy 0.9812
Iter26,Testing Accuracy 0.9818
Iter27,Testing Accuracy 0.9821
Iter28,Testing Accuracy 0.9814
Iter29,Testing Accuracy 0.9812
Iter30,Testing Accuracy 0.9817
Iter31,Testing Accuracy 0.9817
Iter32,Testing Accuracy 0.9816
Iter33,Testing Accuracy 0.9811
Iter34,Testing Accuracy 0.9815
Iter35,Testing Accuracy 0.9819
Iter36,Testing Accuracy 0.9821
Iter37,Testing Accuracy 0.982
Iter38,Testing Accuracy 0.9822
Iter39,Testing Accuracy 0.9824
Iter40,Testing Accuracy 0.9823
Iter41,Testing Accuracy 0.982
Iter42,Testing Accuracy 0.9823
Iter43,Testing Accuracy 0.9826
Iter44,Testing Accuracy 0.9826
Iter45,Testing Accuracy 0.9824
Iter46,Testing Accuracy 0.9825
Iter47,Testing Accuracy 0.9824
Iter48,Testing Accuracy 0.9825
Iter49,Testing Accuracy 0.9823