Tensorflow 逻辑回归处理mnist数据集

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
 
 
mnist = input_data.read_data_sets('./data/mnist_data/', one_hot=True)
train_img = mnist.train.images
train_label = mnist.train.labels
test_img = mnist.test.images
test_label = mnist.test.labels

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))


actv = tf.nn.softmax(tf.matmul(x, W) + b)

cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1))

learning_rate = 0.01
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))

accr = tf.reduce_mean(tf.cast(pred, tf.float32))

init = tf.global_variables_initializer()
training_epochs = 100
batch_size = 100
display_step = 5
 
sess = tf.Session()
sess.run(init)
 
for epoch in range(training_epochs):
    avg_cost = 0.
    num_batch = int(mnist.train.num_examples / batch_size)
    for i in range(num_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feeds_train = {x: batch_xs, y: batch_ys}
        sess.run(optm, feed_dict=feeds_train)
        avg_cost += sess.run(cost, feed_dict=feeds_train) / num_batch
    
    if epoch % display_step == 0:
        feeds_test = {x: mnist.test.images, y: mnist.test.labels}
        train_acc = sess.run(accr, feed_dict=feeds_train)
        test_acc = sess.run(accr, feed_dict=feeds_test)
        print("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f" % (epoch, training_epochs, avg_cost, train_acc, test_acc))
print("DONE")