Tensorflow学习(一)

最新推荐文章于 2025-03-16 17:18:06 发布

diaoxie5337

最新推荐文章于 2025-03-16 17:18:06 发布

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文章标签： python

原文链接：http://www.cnblogs.com/simplekinght/p/9314234.html

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本文通过使用TensorFlow和Python，构建并训练了一个简单的神经网络模型。该模型使用了ReLU激活函数，并通过梯度下降优化器进行训练。文章详细展示了如何生成数据、定义网络结构、设置损失函数以及可视化训练过程。

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不知道应该写些什么，所以先空着?

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#import tensorflow.examples.tutorials.mnist.input_data as input_data

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)
predition = add_layer(l1,10,1,activation_function=None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i %50 ==0:
        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)
        plt.pause(0.1)
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

转载于:https://www.cnblogs.com/simplekinght/p/9314234.html