我正在使用他的代码遵循这个 Tensorflow 教程，并试图达到 8:00 显示的结果，但出于某种原因，我必须在 Windows 上使用带有 Jupyter 的 Docker，而不是他的 IDLE。Docker 机器在“Linux 2.6 / 3.x / 4.x (64-bit)”上运行。
他的代码：
from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
def add_layer(inputs, in_size, out_size, activation_function=None):
# add one more layer and return the output of this layer
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
# Make up some 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(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# add hidden layer
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 prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# important step
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
# plot the real data
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):
# training
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
# to visualize the result and improvement
try:
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict={xs: x_data})
# plot the prediction
lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
plt.pause(0.1)
来自： https ://github.com/erroneousboat/tensorflow-python3-jupyter ，我使用 Python 3.4 和 Jupyter 1.0.0 拉取了 Tensorflow，并将 Matplotlib 升级到 1.5.1
对于 Jupyter，我已经%matplotlib notebook在顶部添加了。
当我运行这段代码时，我想plt.pause(0.1)在底部给我一个弃用警告和一个 NotImplementedError：
/usr/local/lib/python3.4/site-packages/matplotlib/backend_bases.py:2437: MatplotlibDeprecationWarning: Using default event loop until function specific to this GUI is implemented
warnings.warn(str, mplDeprecation)
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-1-a34f2ae50337> in <module>()
59 # plot the prediction
60 lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
---> 61 plt.pause(0.1)
/usr/local/lib/python3.4/site-packages/matplotlib/pyplot.py in pause(interval)
297 canvas.draw()
298 show(block=False)
--> 299 canvas.start_event_loop(interval)
300 return
301
/usr/local/lib/python3.4/site-packages/matplotlib/backends/backend_nbagg.py in start_event_loop(self, timeout)
192
193 def start_event_loop(self, timeout):
--> 194 FigureCanvasBase.start_event_loop_default(self, timeout)
195
196 def stop_event_loop(self):
/usr/local/lib/python3.4/site-packages/matplotlib/backend_bases.py in start_event_loop_default(self, timeout)
2443 self._looping = True
2444 while self._looping and counter * timestep < timeout:
-> 2445 self.flush_events()
2446 time.sleep(timestep)
2447 counter += 1
/usr/local/lib/python3.4/site-packages/matplotlib/backend_bases.py in flush_events(self)
2388 backends with GUIs.
2389 """
-> 2390 raise NotImplementedError
2391
2392 def start_event_loop(self, timeout):
NotImplementedError:
然后，整个程序停在了第一个错误的行。
我对其进行了测试并确认问题出在pause.
import matplotlib.pyplot as plt
import time
%matplotlib notebook
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.ion()
plt.show()
lines = ax.plot([1,2,3],[2,3,4], 'r-', lw=3)
plt.pause(1)
ax.lines[0].remove()
运行代码时出现错误，并且该行仍然存在。没有plt.pause(1)，这条线确实消失了。
我试图用 替换pause，time.sleep(1)但直到最后一个结果完成后，该数字才会显示lines。
知道如何解决这个问题吗？先感谢您。