How to use step_test method in autotest

Best Python code snippet using autotest_python

LengthTesting.py

Source:LengthTesting.py

1import random2import numpy as np3import matplotlib.pyplot as plt4import timeit5678from string import ascii_lowercase9101112def plotter(r_k_time, b_f_time, kmp_time, time, filename):13    '''14    plots using information based on time15    '''16    plt.title(f'Complexity Analysis when m = 500')17    plt.plot(time, b_f_time, label="Brute Force")18    plt.plot(time, r_k_time,19                   'tab:orange', label="Rabin Karp")20    plt.plot(time, kmp_time21                   , 'tab:green', label="KMP")22    plt.xlabel("n")23    plt.ylabel('time in seconds')24    plt.legend()2526    plt.plot()27    plt.savefig(f"{filename}.png")28    print("plotted")293031def get_random_string(length, letters):32    '''33    Generates random string of length length using letters34    '''35    result_str = ''.join(random.choice(letters) for i in range(length))36    return result_str373839def insert(source_str, insert_str, pos):40    '''41    Fits a string into another string and results in a new string42    '''43    return source_str[:pos]+insert_str+source_str[pos:]4445start_test,end_test,step_test = 5000,1000001,500046times = list(range(start_test,end_test,step_test))4748b_f_temp = list(range(start_test,end_test,step_test))49r_k_temp = list(range(start_test,end_test,step_test))50kmp_temp = list(range(start_test,end_test,step_test))5152for i,n in enumerate(range(start_test,end_test,step_test)):53    pattern = get_random_string(500,ascii_lowercase)54    string_to_search = get_random_string(n-len(pattern), ascii_lowercase)55    string_to_search = insert(56        string_to_search, pattern, random.randint(0, len(string_to_search)-1))57    a = timeit.timeit(stmt="NaiveAlgorithm(pattern,text)",58                        setup=f"from BruteForce import NaiveAlgorithm;text='{string_to_search}';pattern='{pattern}';", number=3)59    60    b = timeit.timeit(stmt=f"RabinKarp(pattern,text,524287)",61                        setup=f"from RabinKarp import RabinKarp;text='{string_to_search}';pattern='{pattern}';", number=3)62    63    c = timeit.timeit(stmt="KMP_search(pattern,text)",64                        setup=f"from Knuth import KMP_search;text='{string_to_search}';pattern='{pattern}';", number=3)65    b_f_temp[i] = a/366    r_k_temp[i] = b/367    kmp_temp[i] = c/368    import time69    if n % 100 == 0:70        print(n)71print("done")
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test_cnn.py

Source:test_cnn.py

1# file: test.py2#3# Restores a learned CNN model and tests it.4#5# Store your test images in a sub-folder for each6# class in <dataset_root>/validation/7# e.g.8#    <dataset_root>/validation/bikes9#    <dataset_root>/validation/cars10# 11# ---12# Prof. Dr. Juergen Brauer, www.juergenbrauer.org13from dataset_reader import dataset14dataset_root = "V:/01_job/12_datasets/imagenet/cars_vs_bikes_prepared/"15testing  = dataset(dataset_root + "validation", ".jpeg")16import tensorflow as tf17import numpy as np18# Parameters19batch_size = 120ckpt = tf.train.get_checkpoint_state("saved_model_exp03")21saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta')22pred = tf.get_collection("pred")[0]23x = tf.get_collection("x")[0]24keep_prob = tf.get_collection("keep_prob")[0]25sess = tf.Session()26saver.restore(sess, ckpt.model_checkpoint_path)27# test28step_test = 029correct=030while step_test * batch_size < len(testing):31    # testing_ys and testing_xs are tuples32    testing_ys, testing_xs = testing.nextBatch(batch_size)33    # get first image and first ground truth vector34    # from the tuples35    first_img             = testing_xs[0]36    first_groundtruth_vec = testing_ys[0]37    # at first iteration:38    # show shape of image and ground truth vector39    if step_test == 0:40        print("Shape of testing_xs is :", first_img.shape)41        print("Shape of testing_ys is :", first_groundtruth_vec.shape)42    # given the input image,43    # let the CNN predict the category!44    predict = sess.run(pred, feed_dict={x: testing_xs, keep_prob: 1.})45    # get ground truth label and46    # predicted label from output vector47    groundtruth_label = np.argmax(first_groundtruth_vec)48    predicted_label = np.argmax(predict, 1)[0]49    print("\nImage test: ", step_test)50    print("Ground truth label    :", testing.label2category[groundtruth_label])51    print("Label predicted by CNN:", testing.label2category[predicted_label])52    if predicted_label == groundtruth_label:53        correct+=154    step_test += 155print("\n---")56print("Classified", correct, "images correct of", step_test,"in total.")...

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