如何用Tensorflow 快速搭建神经网络|莫烦

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import matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot(1,1,1)

ax.scatter(x_data,y_data)

plt.ion()

plt.show()

try:

ax.lines.remove(lines[0])

except Exception:

pass

predition_value = sess.run(predition,feed_dict={xs:x_data})

lines = ax.plot(x_data,predition_value,'r',lw=5)

ax.lines.remove(lines[0])

plt.pause(0.1)

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邓普斯•巴塞 · 2018-05-29 · 结果可视化 plot result 0

import tensorflow as tf

def add_layer(inputs,in_size,out_size,activation_function=None):
   Weights = tf.Variable(tf.random_normal([in_size,out_size]))
   biases = tf.Variable(tf.zeros([1,out_size])+0.1)
   Wx_plus_b = tf.matmul(inputs,Weights)+biases
   if activation_function is None:
       outputs =  Wx_plus_b
   else:
        outputs = activation_function ( Wx_plus_b)
    return outputs

x_data = np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data)-0.5+noise

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])
l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1,activation_function=None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
   sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
   if i%50==0:
       print( sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

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邓普斯•巴塞 · 2018-05-29 · Tensorflow 创建神经网络 0

import tensorflow as tf def add_layer(inputs,in_size,out_size,activation_function=None): Weights = tf.Variable(tf.random_normal([in_size,out_size])) biases = tf.Variable(tf.zeros([1,out_size])+0.1) Wx_plus_b = tf.matmul(inputs,Weights)+biases if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function ( Wx_plus_b) return outputs

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邓普斯•巴塞 · 2018-05-29 · Tensorflow 例子3 添加神经网络层 1

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叫70的90后 · 2018-04-10 · Tensorflow 处理结构 0

可视化神经网络

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奥蒂莉亚•法 · 2018-01-18 · Tensorboard 运用1 0

import tensorflow as tf
import numpy as np

#创建数据
x_data=np.radom.rand(100).astype(np.float32)
y_data=x_data*0.1+0.3

#创建初始结构

Weights=tf.Variable(tf.random_uniform([1],-1.0,1.0))

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寒无低处 · 2017-11-16 · Tensorflow 例子2 0