欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

matplotlib实现TensorFlow训练过程的可视化

程序员文章站 2024-03-19 18:27:22
...

matplotlib实现TensorFlow训练过程的可视化

本篇博客介绍使用matplotlib实现TensorFlow训练过程的可视化,下面是代码:

# encoding:utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


# 添加层
def add_layer(inputs, in_size, out_size, activation_function=None):
    W = tf.Variable(tf.random_normal([in_size, out_size]))
    b = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, W) + b
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

# 生成输入数据、噪点和输出数据
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])

# 隐藏层和输出层
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function=None)

# 损失值
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                     reduction_indices=[1]))

# 用梯度下降更新loss
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

# 初始化所有参数
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

figure = plt.figure()
ax = figure.add_subplot(1, 1, 1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()
# 训练1000次
for i in range(1000):
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
        # print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value = sess.run(prediction, feed_dict={xs: x_data, ys: y_data})
        lines = ax.plot(x_data, prediction_value, 'r-', lw=5)

        plt.pause(0.1)

结果:

可以看到一条红线不断地去拟合数据点。

matplotlib实现TensorFlow训练过程的可视化