TensorFlow-matplotlib结果可视化

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TensorFlow-matplotlib结果可视化

硬件：NVIDIA-GTX1080

软件：Windows7、python3.6.5、tensorflow-gpu-1.4.0

一、基础知识

matplotlib为matlab在python中的接口

二、代码展示

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs, in_size, out_size, activate_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 activate_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activate_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])

hide_layer = add_layer(xs, 1, 10, tf.nn.relu)
prediction = add_layer(hide_layer, 10, 1, None)

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

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    #draw input output data
    fig = plt.figure()
    ax = fig.add_subplot(1,1,1)
    ax.scatter(x_data, y_data)
    plt.ion()
    plt.show()
    
    for step in range(3000):
        sess.run(train_step, feed_dict = {xs: x_data, ys:y_data})
        if step%50 == 0:
            #print(sess.run(loss, feed_dict = {xs: x_data, ys:y_data}))

            #draw input prediction loss
            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(1)

三、结果展示

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