观看Tensorflow案例实战视频课程09 神经网路模型架构
import numpy as np import tensorflow as tf import matplotlib.pyplot as plt import input_data
mnist=input_data.read_data_sets('data/',one_hot=True)
#NETWORK TOPOLOGIES n_hidden_1=256 n_hidden_2=128 n_input=784 n_classes=10 #INPUTS AND OUTPUTS x=tf.placeholder("float",[None,n_input]) y=tf.placeholder("float",[None,n_classes]) #NETWORK PARAMETERS stddev=0.1 weights={ 'w1':tf.Variable(tf.random_normal([n_input,n_hidden_1],stddev=stddev)), 'w2':tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2],stddev=stddev)), 'out':tf.Variable(tf.random_normal([n_hidden_2,n_classes],stddev=stddev)) } biases={ 'b1':tf.Variable(tf.random_normal([n_hidden_1])), 'b2':tf.Variable(tf.random_normal([n_hidden_2])), 'out':tf.Variable(tf.random_normal([n_classes])) } print("NETWORK READY")