I am following this Tensorflow tutorial using his code and trying to reach the result shown at 8:00, but I must use Docker on Windows with Jupyter for some reason, instead of his IDLE. The Docker machine is running on "Linux 2.6 / 3.x / 4.x (64-bit)".
His code:
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)
From: https://github.com/erroneousboat/tensorflow-python3-jupyter, I pulled Tensorflow with Python 3.4 and Jupyter 1.0.0 and upgrade Matplotlib to 1.5.1
For Jupyter, I've already added %matplotlib notebook on the top.
When I run this code, I think plt.pause(0.1) at the bottom threw me a deprecation warning and a 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:
Then, the whole program stopped at the first erred line.
I tested it and confirmed the problem is with 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()
The error occurred when the code is run, and the line is still there. Without plt.pause(1), the line is gone indeed.
I tried to replace pause with time.sleep(1), but the figure wouldn't show up until the last result of lines is finished.
Any idea how to solve this? Thank you in advance.
