Tensorflow实现单神经网络

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#tensorflow实现模型评估

#训练集
#测试集
#验证集

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
sess=tf.Session()

#数据集准备
x_vals=np.random.normal(1,0.1,100)
y_vals=np.repeat(10.,100)
x_data=tf.placeholder(dtype=tf.float32,shape=[None,1])
y_target=tf.placeholder(dtype=tf.float32,shape=[None,1])
batch_size=25
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)))  #剩下的做测试标签
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]
A=tf.Variable(tf.random_normal(shape=[1,1]))

#声明算法模型、损失函数和优化器算法
my_output=tf.matmul(x_data,A)
loss=tf.reduce_mean(tf.square(my_output-y_target))

#初始化变量
init=tf.global_variables_initializer()
sess.run(init)
my_opt=tf.train.GradientDescentOptimizer(0.02)
train_step=my_opt.minimize(loss)

#接下来进行模型的迭代训练
for i in range(100):
    rand_index=np.random.choice(len(x_vals_train),size=batch_size)
    rand_x=np.transpose([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})
    if (i+1)%25==0:
        print(str(i+1)+"===Loss:"+str(sess.run(loss,feed_dict={x_data:rand_x,y_target:rand_y})))