python coding examples

1app = 'YOUR TEXT'
2replit = 'https://replit.com'
3fruit = 'Banana'
4discord = 'https://discord.gg/bkuzr9ZabV'
5
6print(discord)
7print(app)
8print(replit)
9print(fruit)

1import numpy as np
2 
3import tensorflow as tf
4 
5 
6 
7from include.data import get_data_set
8 
9from include.model import model
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11 
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13 
14 
15test_x, test_y = get_data_set("test")
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17x, y, output, y_pred_cls, global_step, learning_rate = model()
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19 
20 
21 
22 
23_BATCH_SIZE = 128
24 
25_CLASS_SIZE = 10
26 
27_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
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29 
30 
31 
32 
33saver = tf.train.Saver()
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35sess = tf.Session()
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37 
38 
39 
40 
41try:
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43    print("
44Trying to restore last checkpoint ...")
45 
46    last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
47 
48    saver.restore(sess, save_path=last_chk_path)
49 
50    print("Restored checkpoint from:", last_chk_path)
51 
52except ValueError:
53 
54    print("
55Failed to restore checkpoint. Initializing variables instead.")
56 
57    sess.run(tf.global_variables_initializer())
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59 
60 
61 
62 
63def main():
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65    i = 0
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67    predicted_class = np.zeros(shape=len(test_x), dtype=np.int)
68 
69    while i < len(test_x):
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71        j = min(i + _BATCH_SIZE, len(test_x))
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73        batch_xs = test_x[i:j, :]
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75        batch_ys = test_y[i:j, :]
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77        predicted_class[i:j] = sess.run(y_pred_cls, feed_dict={x: batch_xs, y: batch_ys})
78 
79        i = j
80 
81 
82 
83    correct = (np.argmax(test_y, axis=1) == predicted_class)
84 
85    acc = correct.mean() * 100
86 
87    correct_numbers = correct.sum()
88 
89    print()
90 
91    print("Accuracy on Test-Set: {0:.2f}% ({1} / {2})".format(acc, correct_numbers, len(test_x)))
92 
93if __name__ == "__main__":
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95    main()
96 
97sess.close()
98