".
format((i+1), _EPOCH)) train(i) hours, rem = divmod(time() – train_start, 3600) minutes, seconds = divmod(rem, 60) mes = "Best accuracy pre session: {:.
2f}, time: {:0>2}:{:0>2}:{:05.
2f}" print(mes.
format(global_accuracy, int(hours), int(minutes), seconds))if __name__ == "__main__": main() sess.
close()Output:Epoch: 60/60Global step: 23070 – [>—————————–] 0% – acc: 0.
9531 – loss: 1.
5081 – 7045.
4 sample/secGlobal step: 23080 – [>—————————–] 3% – acc: 0.
9453 – loss: 1.
5159 – 7147.
6 sample/secGlobal step: 23090 – [=>—————————-] 5% – acc: 0.
9844 – loss: 1.
4764 – 7154.
6 sample/secGlobal step: 23100 – [==>—————————] 8% – acc: 0.
9297 – loss: 1.
5307 – 7104.
4 sample/secGlobal step: 23110 – [==>—————————] 10% – acc: 0.
9141 – loss: 1.
5462 – 7091.
4 sample/secGlobal step: 23120 – [===>————————–] 13% – acc: 0.
9297 – loss: 1.
5314 – 7162.
9 sample/secGlobal step: 23130 – [====>————————-] 15% – acc: 0.
9297 – loss: 1.
5307 – 7174.
8 sample/secGlobal step: 23140 – [=====>————————] 18% – acc: 0.
9375 – loss: 1.
5231 – 7140.
0 sample/secGlobal step: 23150 – [=====>————————] 20% – acc: 0.
9297 – loss: 1.
5301 – 7152.
8 sample/secGlobal step: 23160 – [======>———————–] 23% – acc: 0.
9531 – loss: 1.
5080 – 7112.
3 sample/secGlobal step: 23170 – [=======>———————-] 26% – acc: 0.
9609 – loss: 1.
5000 – 7154.
0 sample/secGlobal step: 23180 – [========>———————] 28% – acc: 0.
9531 – loss: 1.
5074 – 6862.
2 sample/secGlobal step: 23190 – [========>———————] 31% – acc: 0.
9609 – loss: 1.
4993 – 7134.
5 sample/secGlobal step: 23200 – [=========>——————–] 33% – acc: 0.
9609 – loss: 1.
4995 – 7166.
0 sample/secGlobal step: 23210 – [==========>——————-] 36% – acc: 0.
9375 – loss: 1.
5231 – 7116.
7 sample/secGlobal step: 23220 – [===========>——————] 38% – acc: 0.
9453 – loss: 1.
5153 – 7134.
1 sample/secGlobal step: 23230 – [===========>——————] 41% – acc: 0.
9375 – loss: 1.
5233 – 7074.
5 sample/secGlobal step: 23240 – [============>—————–] 43% – acc: 0.
9219 – loss: 1.
5387 – 7176.
9 sample/secGlobal step: 23250 – [=============>—————-] 46% – acc: 0.
8828 – loss: 1.
5769 – 7144.
1 sample/secGlobal step: 23260 – [==============>—————] 49% – acc: 0.
9219 – loss: 1.
5383 – 7059.
7 sample/secGlobal step: 23270 – [==============>—————] 51% – acc: 0.
8984 – loss: 1.
5618 – 6638.
6 sample/secGlobal step: 23280 – [===============>————–] 54% – acc: 0.
9453 – loss: 1.
5151 – 7035.
7 sample/secGlobal step: 23290 – [================>————-] 56% – acc: 0.
9609 – loss: 1.
4996 – 7129.
0 sample/secGlobal step: 23300 – [=================>————] 59% – acc: 0.
9609 – loss: 1.
4997 – 7075.
4 sample/secGlobal step: 23310 – [=================>————] 61% – acc: 0.
8750 – loss: 1.
5842 – 7117.
8 sample/secGlobal step: 23320 – [==================>———–] 64% – acc: 0.
9141 – loss: 1.
5463 – 7157.
2 sample/secGlobal step: 23330 – [===================>———-] 66% – acc: 0.
9062 – loss: 1.
5549 – 7169.
3 sample/secGlobal step: 23340 – [====================>———] 69% – acc: 0.
9219 – loss: 1.
5389 – 7164.
4 sample/secGlobal step: 23350 – [====================>———] 72% – acc: 0.
9609 – loss: 1.
5002 – 7135.
4 sample/secGlobal step: 23360 – [=====================>——–] 74% – acc: 0.
9766 – loss: 1.
4842 – 7124.
2 sample/secGlobal step: 23370 – [======================>——-] 77% – acc: 0.
9375 – loss: 1.
5231 – 7168.
5 sample/secGlobal step: 23380 – [======================>——-] 79% – acc: 0.
8906 – loss: 1.
5695 – 7175.
2 sample/secGlobal step: 23390 – [=======================>——] 82% – acc: 0.
9375 – loss: 1.
5225 – 7132.
1 sample/secGlobal step: 23400 – [========================>—–] 84% – acc: 0.
9844 – loss: 1.
4768 – 7100.
1 sample/secGlobal step: 23410 – [=========================>—-] 87% – acc: 0.
9766 – loss: 1.
4840 – 7172.
0 sample/secGlobal step: 23420 – [==========================>—] 90% – acc: 0.
9062 – loss: 1.
5542 – 7122.
1 sample/secGlobal step: 23430 – [==========================>—] 92% – acc: 0.
9297 – loss: 1.
5313 – 7145.
3 sample/secGlobal step: 23440 – [===========================>–] 95% – acc: 0.
9297 – loss: 1.
5301 – 7133.
3 sample/secGlobal step: 23450 – [============================>-] 97% – acc: 0.
9375 – loss: 1.
5231 – 7135.
7 sample/secGlobal step: 23460 – [=============================>] 100% – acc: 0.
9250 – loss: 1.
5362 – 10297.
5 sample/secEpoch 60 – accuracy: 78.
81% (7881/10000)This epoch receive better accuracy: 78.
81 > 78.
78.
Saving session.
###########################################################################################################Run Network on Test DataSet:import numpy as np import tensorflow as tf from include.
data import get_data_set from include.
model import model test_x, test_y = get_data_set("test") x, y, output, y_pred_cls, global_step, learning_rate = model() _BATCH_SIZE = 128 _CLASS_SIZE = 10 _SAVE_PATH = ".
/tensorboard/cifar-10-v1.
0.
0/" saver = tf.
train.
Saver() sess = tf.
Session() try: print(".Trying to restore last checkpoint .
") last_chk_path = tf.
train.
latest_checkpoint(checkpoint_dir=_SAVE_PATH) saver.
restore(sess, save_path=last_chk_path) print("Restored checkpoint from:", last_chk_path) except ValueError: print(".Failed to restore checkpoint.
Initializing variables instead.
") sess.
run(tf.
global_variables_initializer()) def main(): i = 0 predicted_class = np.
zeros(shape=len(test_x), dtype=np.
int) while i < len(test_x): j = min(i + _BATCH_SIZE, len(test_x)) batch_xs = test_x[i:j, :] batch_ys = test_y[i:j, :] predicted_class[i:j] = sess.
run(y_pred_cls, feed_dict={x: batch_xs, y: batch_ys}) i = j correct = (np.
argmax(test_y, axis=1) == predicted_class) acc = correct.
mean() * 100 correct_numbers = correct.
sum() print() print("Accuracy on Test-Set: {0:.
2f}% ({1} / {2})".
format(acc, correct_numbers, len(test_x))) if __name__ == "__main__": main() sess.
close()Simple Output:Trying to restore last checkpoint .
Restored checkpoint from: .
/tensorboard/cifar-10-v1.
0.
0/-23460Accuracy on Test-Set: 78.
81% (7881 / 10000)That was a very interesting use-case, wasn’t it.Thus, we saw how machine learning works and developed a basic program to implement it using the TensorFlow library in Python.
ConclusionThe Python projects discussed in this article should help you kickstart your learning about Python and it will indulge you and push you to learn more about Python practically.
This will be very handy when you are trying to consider a problem and providing a solution for that using Python.
Python will help you solve multiple real-life projects as well and these concepts will get you up to speed with how you can begin exploring and understanding the art of project design, development, and handling.
I hope you have enjoyed this post on Python Projects.
If you have any questions regarding this tutorial, please let me know in the comments.
If you wish to check out more articles on the market’s most trending technologies like Artificial Intelligence, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series which will explain the various other aspects of Python and Data Science.
1.
Python Tutorial2.
Python Programming Language3.
Python Functions4.
File Handling in Python5.
Python Numpy Tutorial6.
Scikit Learn Machine Learning7.
Python Pandas Tutorial8.
Matplotlib Tutorial9.
Tkinter Tutorial10.
Requests Tutorial11.
PyGame Tutorial12.
OpenCV Tutorial13.
Web Scraping With Python14.
PyCharm Tutorial15.
Machine Learning Tutorial16.
Linear Regression Algorithm from scratch in Python17.
Python for Data Science18.
Loops in Python19.
Python RegEx20.
Machine Learning Projects21.
Arrays in PythonOriginally published at www.
edureka.
co on January 11, 2019.
.