代码在这里！

我正在使用tensorflow来获取mnist数据集并使用简单的逻辑回归图。

import tensorflow as tf

(x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data(path='mnist.npz')
x_train,x_test = tf.reshape(x_train,[len(x_train),784]),tf.reshape(x_test,[len(x_test),784])

使用tf.one_hot获得一种热编码

y_train,y_test = tf.one_hot(y_train,10),tf.one_hot(y_test,10)

learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

tf图形输入

x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,10])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

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

费用

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

优化器

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(W))
    X_train,X_test = sess.run(x_train).astype('float32'),sess.run(x_test).astype('float32')
    Y_train,Y_test = sess.run(y_train).astype('float32'),sess.run(y_test).astype('float32')

    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(60000/batch_size)
        for i in range(total_batch):
            batch_x,batch_y = X_train[i*batch_size:i*batch_size+total_batch],Y_train[i*batch_size:i*batch_size+total_batch]

            _,c = sess.run([optimizer,cost],feed_dict = {x:batch_x,y:batch_y})

            avg_cost += c/total_batch

        if (epoch+1) % display_step == 0:
            print("Epoch:",(epoch+1),"cost=",avg_cost,"W = ",sess.run(W))

    print("Optimization Finished!")