結果視覺化

2021-08-18 14:07:18 字數 2217 閱讀 7113

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

def add_layer(inputs, input_size, output_size, activation_function = none):

weights = tf.variable(tf.random_normal([input_size, output_size]))

biases = tf.variable(tf.zeros([1, output_size]) + 0.1) #biases初始化為0.1的列向量

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

#create_real data

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

#define placeholder for inputs to network

xs = tf.placeholder(dtype=tf.float32,shape=[none, 1])

ys = tf.placeholder(dtype=tf.float32,shape=[none, 1])

#add hiden layer 輸入層輸出層乙個神經元(因為只有乙個屬性),隱層十個神經元

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

#add output layer

prediction = add_layer(l1, 10, 1, activation_function=none)

#the error between predic and real data

loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction - ys), reduction_indices=[1])) #相當於轉化為橫向量

train_step = tf.train.gradientdescentoptimizer(0.2).minimize(loss)

#initialize all variable

init = tf.initialize_all_variables()

sess = tf.session()

sess.run(init)fig = plt.figure()

ax = fig.add_subplot(1, 1, 1)

ax.scatter(x_data, y_data)

plt.ion()

for i in range(1000):

#training

sess.run(train_step, feed_dict=)

if i%50 == 0:try:

ax.lines.remove(lines[0])

except exception:

pass

prediction_value = sess.run(prediction, feed_dict=)

lines = ax.plot(x_data, prediction_value, color = 'red')

plt.pause(0.5)

while(true):

plt.pause(1)

plt.ion進入互動模式,最後的while迴圈保證介面不會自動關閉

剛打算用qq截圖,發現開啟qq介面後,每隔一秒,該圖介面會彈出擋住qq介面,也就是說使用plt,ion(),該圖一直在重新整理

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