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TensorFlow - 感知器的隐藏层

在本章中，无涯教程将专注于从x和f(x)的已知点集中学习的网络，由单个隐藏层将构建此简单网络。

解释感知器隐藏层的代码如下所示-

#Importing the necessary modules 
import tensorflow as tf 
import numpy as np 
import math, random 
import matplotlib.pyplot as plt 

np.random.seed(1000) 
function_to_learn=lambda x: np.cos(x) + 0.1*np.random.randn(*x.shape) 
layer_1_neurons=10 
NUM_points=1000 

#训练参数
batch_size=100 
NUM_EPOCHS=1500 

all_x=np.float32(np.random.uniform(-2*math.pi, 2*math.pi, (1, NUM_points))).T 
   np.random.shuffle(all_x) 

train_size=int(900) 
#训练给定集合中的前 700 个点 x_training=all_x[:train_size]
y_training=function_to_learn(x_training)

#训练给定集合中的最后 300 个点 x_validation=all_x[train_size:]
y_validation=function_to_learn(x_validation) 

plt.figure(1) 
plt.scatter(x_training, y_training, c='blue', label='train') 
plt.scatter(x_validation, y_validation, c='pink', label='validation') 
plt.legend() 
plt.show()

X=tf.placeholder(tf.float32, [None, 1], name="X")
Y=tf.placeholder(tf.float32, [None, 1], name="Y")

#first layer 
#神经元数=10
w_h=tf.Variable(
   tf.random_uniform([1, layer_1_neurons],\minval=-1, maxval=1, dtype=tf.float32)) 
b_h=tf.Variable(tf.zeros([1, layer_1_neurons], dtype=tf.float32)) 
h=tf.nn.sigmoid(tf.matmul(X, w_h) + b_h)

#output layer 
#Number of neurons=10 
w_o=tf.Variable(
   tf.random_uniform([layer_1_neurons, 1],\minval=-1, maxval=1, dtype=tf.float32)) 
b_o=tf.Variable(tf.zeros([1, 1], dtype=tf.float32)) 

#建立模型
model=tf.matmul(h, w_o) + b_o 

#最小化成本函数（模型 - Y）
train_op=tf.train.AdamOptimizer().minimize(tf.nn.l2_loss(model - Y)) 

#开始学习阶段
sess=tf.Session() sess.run(tf.initialize_all_variables()) 

errors=[] 
for i in range(NUM_EPOCHS): 
   for start, end in zip(range(0, len(x_training), batch_size),\
      range(batch_size, len(x_training), batch_size)): 
      sess.run(train_op, feed_dict={X: x_training[start:end],\Y: y_training[start:end]})
   cost=sess.run(tf.nn.l2_loss(model - y_validation),\feed_dict={X:x_validation}) 
   errors.append(cost) 
   
   if i%100 == 0: 
      print("epoch %d, cost=%g" % (i, cost)) 
      
plt.plot(errors,label='MLP Function Approximation') plt.xlabel('epochs') 
plt.ylabel('cost') 
plt.legend() 
plt.show()