TensorFlow - 感知器的隐藏层

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  简述

  在本章中，我们将重点关注我们必须从称为 x 和 f(x) 的已知点集学习的网络。单个隐藏层将构建这个简单的网络。

  感知器隐藏层的解释代码如下所示 -

  
  #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 
  #Training the parameters 
  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) 
  #Training the first 700 points in the given set x_training = all_x[:train_size] 
  y_training = function_to_learn(x_training)
  #Training the last 300 points in the given set 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 
  #Number of neurons = 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)) 
  #build the model 
  model = tf.matmul(h, w_o) + b_o 
  #minimize the cost function (model - Y) 
  train_op = tf.train.AdamOptimizer().minimize(tf.nn.l2_loss(model - Y)) 
  #Start the Learning phase 
  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()
  

  输出

  以下是函数层近似的表示 -

  

  这里两个数据以 W 的形状表示。这两个数据是：训练和验证，它们以不同的颜色表示，如图例部分可见。