基于TensorFlow的单层神经网络

1、创建计算会话，导入必要的编程库。

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets

sess = tf.Session()

2、加载Iris数据集

iris = datasets.load_iris()
x_vals = np.array([x[0:3] for x in iris.data])
y_vals = np.array([x[3] for x in iris.data])

3、将数据集正则化

seed = 2
tf.set_random_seed(seed)
np.random.seed(seed)

train_indices = np.random.choice(len(x_vals),round(len(x_vals)*0.8),replace = False)
test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))

x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]

def normalize_cols(m):
    col_max = m.max(axis = 0)
    col_min = m.min(axis = 0)
    return (m-col_min)/(col_max - col_min)
    
x_vals_train = np.nan_to_num(normalize_cols(x_vals_train))
x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))

4、声明占位符

batch_size =50
x_data = tf.placeholder(shape = [None,3],dtype = tf.float32)
y_target = tf.placeholder(shape = [None,1],dtype = tf.float32)

5、声明隐含层

hidden_layer_nodes =5
A1 = tf.Variable(tf.random_normal(shape=[3,hidden_layer_nodes]))
b1 = tf.Variable(tf.random_normal(shape=[hidden_layer_nodes]))
A2 = tf.Variable(tf.random_normal(shape=[hidden_layer_nodes,1]))
b2 = tf.Variable(tf.random_normal(shape=[1]))

6、定义模型，损失函数等

hidden_output = tf.nn.relu(tf.add(tf.matmul(x_data, A1),b1))
final_output = tf.nn.relu(tf.add(tf.matmul(hidden_output, A2),b2))

loss = tf.reduce_mean(tf.square(y_target - final_output))

7、声明优化函数、初始化模型变量

my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)

8、遍历迭代训练模型,绘制出损失函数的图像。

loss_vec = []

test_vec = []

for i in range(500):
    rand_index = np.random.choice(len(x_vals_train),size=batch_size)
    rand_x = x_vals_train[rand_index]
    rand_y = np.transpose([y_vals_train[rand_index]])
    sess.run(train_step, feed_dict = {x_data:rand_x,y_target:rand_y})
    
    temp_loss = sess.run(loss,feed_dict = {x_data:rand_x,y_target:rand_y})
    loss_vec.append(np.sqrt(temp_loss))
    
    test_temp_loss = sess.run(loss,feed_dict = {x_data:x_vals_test,y_target:np.transpose([y_vals_test])})
    test_vec.append(np.sqrt(test_temp_loss))
    
    if(i+1)%50:
        print('Generation:' + str(i+1) +'.Loss = ' + str(temp_loss))
        

plt.plot(loss_vec, 'k-', label='Train Loss')
plt.plot(test_vec, 'r--', label='Train Loss')
plt.show()

9、运行结果