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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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