tutorial práctico de tensorflow 1.x (6) - modelo de clasificación simple

Objetivo

Este artículo tiene como objetivo presentar los puntos de conocimiento introductorios y ejemplos prácticos de tensorflow. Espero que todos los estudiantes novatos puedan dominar las operaciones relacionadas con tensorflow después de aprender.

Código de modelo de clasificación simple

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST", one_hot=True) # 读数据
batch_size = 64
n_batchs = mnist.train.num_examples // batch_size

x = tf.placeholder(dtype=tf.float32, shape=[None, 784], name='x') # 因为每张图片数据是 28*28=784 维的
y = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='y')  # 因为一共有 10 种类别的图片

# softmax((x * w) + b)
w = tf.Variable(tf.ones(shape=[784, 10]))
b = tf.Variable(tf.zeros(shape=[10]))
predict = tf.nn.softmax(tf.matmul(x, w) + b)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=y))
opt = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()

correct = tf.equal(tf.argmax(predict,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) # 计算准确率

with tf.Session() as sess:
    sess.run(init)
    total_batch = 0
    last_batch = 0
    best = 0
    for epoch in range(100):
        for _ in range(n_batchs):
            xx,yy = mnist.train.next_batch(batch_size)
            sess.run(opt, feed_dict={x:xx, y:yy})
        loss_value, acc = sess.run([loss, accuracy], feed_dict={x:mnist.test.images, y:mnist.test.labels})
        # 始终打印最好的准确率信息
        if acc > best:
            best = acc
            last_batch  = total_batch
            print('epoch:%d, loss:%f, acc:%f' % (epoch, loss_value, acc))
        if total_batch - last_batch > 5: # 训练早停条件
            print('when epoch-%d early stop train'%epoch)
            break
        total_batch += 1
        
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resultado de salida

Extracting MNIST/train-images-idx3-ubyte.gz
Extracting MNIST/train-labels-idx1-ubyte.gz
Extracting MNIST/t10k-images-idx3-ubyte.gz
Extracting MNIST/t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From <ipython-input-18-ec0f1616d772>:16: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

epoch:0, loss:1.697559, acc:0.819900
epoch:1, loss:1.627650, acc:0.887500
epoch:2, loss:1.604011, acc:0.897200
epoch:3, loss:1.592221, acc:0.902300
epoch:4, loss:1.585058, acc:0.904700
epoch:5, loss:1.579867, acc:0.907700
epoch:6, loss:1.575740, acc:0.909700
epoch:7, loss:1.572829, acc:0.911200
epoch:8, loss:1.570307, acc:0.912600
epoch:9, loss:1.567902, acc:0.913100
epoch:10, loss:1.565990, acc:0.913900
epoch:11, loss:1.564570, acc:0.916100
epoch:13, loss:1.561729, acc:0.917800
epoch:14, loss:1.560736, acc:0.917900
epoch:15, loss:1.559514, acc:0.918600
epoch:17, loss:1.557875, acc:0.919600
epoch:18, loss:1.557073, acc:0.920100
epoch:21, loss:1.554998, acc:0.920500
epoch:22, loss:1.554592, acc:0.920700
epoch:23, loss:1.553998, acc:0.921500
epoch:24, loss:1.553378, acc:0.922100
epoch:28, loss:1.551517, acc:0.922400
epoch:29, loss:1.551527, acc:0.922700
epoch:31, loss:1.550692, acc:0.923000
epoch:32, loss:1.550284, acc:0.923200
epoch:33, loss:1.550164, acc:0.923300
epoch:34, loss:1.549571, acc:0.923600
epoch:35, loss:1.549563, acc:0.923700
epoch:38, loss:1.548744, acc:0.923800
epoch:39, loss:1.548406, acc:0.924700
epoch:41, loss:1.547895, acc:0.924800
epoch:45, loss:1.547032, acc:0.925300
epoch:49, loss:1.546252, acc:0.925900
epoch:51, loss:1.545930, acc:0.926400
epoch:56, loss:1.545088, acc:0.926700
epoch:59, loss:1.544781, acc:0.927400
epoch:65, loss:1.544077, acc:0.927500
epoch:66, loss:1.543733, acc:0.927800
epoch:70, loss:1.543496, acc:0.928100
epoch:76, loss:1.542884, acc:0.928300
epoch:80, loss:1.542315, acc:0.928600
when epoch-86 early stop train    
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Referencia en este artículo

Referencia para este artículo: blog.csdn.net/qq_19672707…