人工智能实践：Tensorflow笔记 # 1 前向传播

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

这段代码是为了消除警告(否则会有一堆奇奇怪怪的输出，我有点强迫症…

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两层简单的全连接神经网络

#coding:utf-8
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

import tensorflow as tf
#定义输入和参数
x = tf.constant([[0.7,0.5]])
w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
#前向传播过程
a = tf.matmul(x,w1)
y = tf.matmul(a,w2)
#计算
with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print("y is\n",sess.run(y))

期望输出:

y is
 [[ 3.0904665]]

输入可变:

#coding:utf-8
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

x = tf.placeholder(tf.float32,shape=(1,2))
w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))

a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print("y is:\n",sess.run(y,feed_dict={x:[[0.7,0.5]]}))

喂多组数据:

#coding:utf-8
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

x = tf.placeholder(tf.float32,shape=(None,2))
w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))

a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print("y is:\n",sess.run(y,feed_dict={x:[[0.7,0.5],[0.2,0.3],[0.3,0.4],[0.4,0.5]]}))
    print("w1:\n",sess.run(w1))
    print("w2:\n",sess.run(w2))

y is:
 [[ 3.0904665 ]
 [ 1.2236414 ]
 [ 1.72707319]
 [ 2.23050475]]
w1:
 [[-0.81131822  1.48459876  0.06532937]
 [-2.4427042   0.0992484   0.59122431]]
w2:
 [[-0.81131822]
 [ 1.48459876]
 [ 0.06532937]]