邏輯迴歸

# _*_ encoding=utf8 _*_

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

mnist = input_data.read_data_sets("data/", one_hot=True)
# 設定學習率，
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

#圖片都是28*28 = 784的  預測為0-9 10個數字  10分類
 

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


# 初始化引數的值
W = tf.Variable(tf.zeros([28*28, 10]),name="W")
b = tf.Variable(tf.zeros([10]))

# 前向傳播
pred = tf.nn.softmax(tf.matmul(x, W) + b)
# 使用交叉熵作為損失函式
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=
 
1))
# 梯度下降求最佳的cost
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(W) #<tf.Variable 'W:0' shape=(784, 10) dtype=float32_ref>

    for epoch in range(training_epochs):
        # 得到資料總的batch
 

        total_batch = int(mnist.train.num_examples / batch_size)
        # print(total_batch) #550 個batch  55000張訓練資料
        # 每一訓練 都去求得平均的cost
        avg_cost = 0

        #batch訓練 遍歷所以得batch
        for i in range(total_batch):
            batch_x,batch_y = mnist.train.next_batch(batch_size)

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

            avg_cost += c / total_batch

        # 計算每一次迭代的cost
        if (epoch + 1) % display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))

    #評估模型  訓練出來的結果tf.argmax(pred, 1) 找10個數最大的那個 和真實值y對比
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 計算準確率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    # 使用mnist的test資料進行評估
    # 首先執行之前的所有必要的操作來產生這個計算這個tensor需要的輸入，然後通過這些輸入產生這個tensor。
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))