Problem :

I’m starting my adventure with Tensorflow. I think I installed everything correctly, but when running this code, PyCharm returns an error:

Traceback (most recent call last): File “C:/Users/tymot/Desktop/myenv3/env/Tensorflow/all_good.py”, line 15, in <module> import matplotlib.pyplot as plt File “C:UserstymotAnaconda1libsite-packagesmatplotlibpyplot.py”, line 115, in <module> _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup() File “C:UserstymotAnaconda1libsite-packagesmatplotlibbackends__init__.py”, line 62, in pylab_setup [backend_name], 0) File “C:UserstymotAnaconda1libsite-packagesmatplotlibbackendsbackend_qt5agg.py”, line 15, in <module> from .backend_qt5 import ( File “C:UserstymotAnaconda1libsite-packagesmatplotlibbackendsbackend_qt5.py”, line 19, in <module> import matplotlib.backends.qt_editor.figureoptions as figureoptions File “C:UserstymotAnaconda1libsite-packagesmatplotlibbackendsqt_editorfigureoptions.py”, line 20, in <module> import matplotlib.backends.qt_editor.formlayout as formlayout File “C:UserstymotAnaconda1libsite-packagesmatplotlibbackendsqt_editorformlayout.py”, line 54, in <module> from matplotlib.backends.qt_compat import QtGui, QtWidgets, QtCore File “C:UserstymotAnaconda1libsite-packagesmatplotlibbackendsqt_compat.py”, line 158, in <module> raise ImportError(“Failed to import any qt binding”) ImportError: Failed to import any qt binding

My code which I am trying to run:

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt num_features = 2 num_iter = 10000 display_step = int(num_iter / 10) learning_rate = 0.01 num_input = 2 # units in the input layer 28×28 images num_hidden1 = 2 # units in the first hidden layer num_output = 1 # units in the output, only one output 0 or 1 #%% mlp function def multi_layer_perceptron_xor(x, weights, biases): hidden_layer1 = tf.add(tf.matmul(x, weights[‘w_h1’]), biases[‘b_h1’]) hidden_layer1 = tf.nn.sigmoid(hidden_layer1) out_layer = tf.add(tf.matmul(hidden_layer1, weights[‘w_out’]), biases[‘b_out’]) return out_layer #%% x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], np.float32) # 4×2, input y = np.array([0, 1, 1, 0], np.float32) # 4, correct output, AND operation y = np.reshape(y, [4,1]) # convert to 4×1 # trainum_inputg data and labels X = tf.placeholder(‘float’, [None, num_input]) # training data Y = tf.placeholder(‘float’, [None, num_output]) # labels # weights and biases weights = { ‘w_h1’ : tf.Variable(tf.random_normal([num_input, num_hidden1])), # w1, from input layer to hidden layer 1 ‘w_out’: tf.Variable(tf.random_normal([num_hidden1, num_output])) # w2, from hidden layer 1 to output layer } biases = { ‘b_h1’ : tf.Variable(tf.zeros([num_hidden1])), ‘b_out’: tf.Variable(tf.zeros([num_output])) } model = multi_layer_perceptron_xor(X, weights, biases) ”’ – cost function and optimization – sigmoid cross entropy – single output – softmax cross entropy – multiple output, normalized ”’ loss_func = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=model, labels=Y)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss_func) sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) for k in range(num_iter): tmp_cost, _ = sess.run([loss_func, optimizer], feed_dict={X: x, Y: y}) if k % display_step == 0: #print(‘output: ‘, sess.run(model, feed_dict={X:x})) print(‘loss= ‘ + “{:.5f}”.format(tmp_cost)) # separates the input space W = np.squeeze(sess.run(weights[‘w_h1’])) # 2×2 b = np.squeeze(sess.run(biases[‘b_h1′])) # 2, sess.close() #%% # Now plot the fitted line. We need only two points to plot the line plot_x = np.array([np.min(x[:, 0] – 0.2), np.max(x[:, 1]+0.2)]) plot_y = -1 / W[1, 0] * (W[0, 0] * plot_x + b[0]) plot_y = np.reshape(plot_y, [2, -1]) plot_y = np.squeeze(plot_y) plot_y2 = -1 / W[1, 1] * (W[0, 1] * plot_x + b[1]) plot_y2 = np.reshape(plot_y2, [2, -1]) plot_y2 = np.squeeze(plot_y2) plt.scatter(x[:, 0], x[:, 1], c=y, s=100, cmap=’viridis’) plt.plot(plot_x, plot_y, color=’k’, linewidth=2) # line 1 plt.plot(plot_x, plot_y2, color=’k’, linewidth=2) # line 2 plt.xlim([-0.2, 1.2]); plt.ylim([-0.2, 1.25]); #plt.text(0.425, 1.05, ‘XOR’, fontsize=14) plt.xticks([0.0, 0.5, 1.0]); plt.yticks([0.0, 0.5, 1.0]) plt.show() #%%

I think it follows another version of python. How can I run the code without error. I installed qt-binding and added tensorflow to my PyCharm.

Any help will be appreciated.

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Tags : python,tensorflow

make sure you have PyQt5 installed. you may open a python shell and try:

import PyQt5

if it fails then you can install it via:

pip install PyQt5

If you are on macOS or Linux be careful that you might need to run

pip3 install PyQt5

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It solved my problem.

pip uninstall matplotlib python -m pip install -upgrade pip pip install matplotlib

I met the same ImportError when using %matplotlib qt. Following Foad’s answer solved my problem.

I use Archlinux so I tried

sudo pacman -S python-pyqt5

and it worked.

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This is also useful:

https://docs.github.com/en/repositories/working-with-files/managing-large-files/about-large-files-on-github

If the file was added with your most recent commit:

I had that error on windows-VScode after the following magic command:

%matplotlib qt

Solved this error by completing the following steps:

• Uninstall Anaconda
• Reinstall Anaconda
• Restart, open Anaconda, start VScode from there.