gogogomc的博客

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# number 1 to 10 datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)# def add_layer(inputs, in_size, out_size, activation_function=None,):# # add one m

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data#softmax和交叉熵结合生成分类算法#从input_data中读取数据集，使用one_hot编码mnist = input_data.read_data_sets('MNIST_data',one_hot=True)#定义添加层#in_size,out_size输入单位输出单位大小def add_layer(inputs,

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt#定义添加层#in_size,out_size输入单位输出单位大小def add_layer(inputs, in_size,out_size,activation_function=None):with tf.name_scope(‘layer’):with tf.name_scope(‘weights’):Weights = tf.Variabl

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt#定义添加层#in_size,out_size输入单位输出单位大小def add_layer(inputs, in_size,out_size,activation_function=None):Weights = tf.Variable(tf.random_normal([in_size,out_size]))#矩阵 #tf.random.norma

import tensorflow as tfimport numpy as np#保证sess.run()能够正常运行tf.compat.v1.disable_eager_execution()#定义添加层#in_size,out_size输入单位输出单位大小def add_layer(inputs, in_size,out_size,activation_function=None):Weights = tf.Variable(tf.random.normal([in_size,out_s

import tensorflow as tf#保证sess.run()能够正常运行tf.compat.v1.disable_eager_execution()#变量statestate = tf.Variable(0,name = ‘counter’)#print(state.name)#常量oneone = tf.constant(1)new_value = tf.add(state,one)update = tf.compat.v1.assign(state, new_value)

import tensorflow as tf#保证sess.run()能够正常运行tf.compat.v1.disable_eager_execution()matrix1=tf.constant([[3,3]])#两个矩阵相乘matrix2=tf.constant([[2],[2]])product = tf.matmul(matrix1,matrix2)#method 1sess = tf.compat.v1.Session()result = sess.run(product)p