How to use test_generator method in pyresttest

Best Python code snippet using pyresttest_python

test - Copy.py

Source:test - Copy.py

1<<<<<<< HEAD2# Imports3import cv24import keras5import time6import numpy as np7from keras import optimizers8from models.vgg16_model import vgg16_finetuned9from datagenerator.datagenerator import test_datagenerator10from keras.preprocessing.image import load_img11from IPython.display import display12from PIL import Image13from keras.models import load_model14test_dir = 'dataset/test'15# Load the trained model16print('loading trained model...')17new_model = load_model('trained_models/model1.h5')18print('loading complete')19# Print the summary of the loaded model20print('summary of loaded model')21new_model.summary()22# Load test images23print('reading test images...')24test_generator = test_datagenerator()25# Get the filenames from the generator26fnames = test_generator.filenames27# Get the ground truth from generator28ground_truth = test_generator.classes29# Get the label to class mapping from the generator30label2index = test_generator.class_indices31# Getting the mapping from class index to class label32idx2label = dict((v, k) for k, v in label2index.items())33# Get the predictions from the model using the generator34print('predicting on the test images...')35prediction_start = time.clock()36predictions = new_model.predict_generator(test_generator,37                                          steps=test_generator.samples / test_generator.batch_size,38                                          verbose=0)39prediction_finish = time.clock()40prediction_time = prediction_finish - prediction_start41predicted_classes = np.argmax(predictions, axis=1)42errors = np.where(predicted_classes != ground_truth)[0]43print("No. of errors = {}/{}".format(len(errors), test_generator.samples))44correct_predictions = np.where(predicted_classes == ground_truth)[0]45print("No. of correct predictions = {}/{}".format(len(correct_predictions), test_generator.samples))46print("Test Accuracy = {0:.2f}%".format(len(correct_predictions)*100/test_generator.samples))47print("Predicted in {0:.3f} minutes!".format(prediction_time/60))48# original = load_img('{}/{}'.format(test_dir, fnames[correct_predictions[2]]))49# print(original)50# # Show the errors51# for i in range(len(errors)):52#     pred_class = np.argmax(predictions[errors[i]])53#     pred_label = idx2label[pred_class]54#55#     title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(56#         fnames[errors[i]].split('/')[0],57#         pred_label,58#         predictions[errors[i]][pred_class])59#60#     original = load_img('{}/{}'.format(test_dir, fnames[errors[i]]))61#     plt.figure(figsize=[7, 7])62#     plt.axis('off')63#     plt.title(title)64#     plt.imshow(original)65#     plt.show()66#67# # Show the correct predictions68# for i in range(len(correct_predictions)):69#     pred_class = np.argmax(predictions[correct_predictions[i]])70#     pred_label = idx2label[pred_class]71#72#     title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(73#         fnames[correct_predictions[i]].split('/')[0],74#         pred_label,75#         predictions[correct_predictions[i]][pred_class])76#77#     original = load_img('{}/{}'.format(test_dir, fnames[correct_predictions[i]]))78#     plt.figure(figsize=[7, 7])79#     plt.axis('off')80#     plt.title(title)81#     plt.imshow(original)82=======83# Imports84import cv285import keras86import time87import numpy as np88from keras import optimizers89from models.vgg16_model import vgg16_finetuned90from datagenerator.datagenerator import test_datagenerator91from keras.preprocessing.image import load_img92from IPython.display import display93from PIL import Image94from keras.models import load_model95test_dir = 'dataset/test'96# Load the trained model97print('loading trained model...')98new_model = load_model('trained_models/model1.h5')99print('loading complete')100# Print the summary of the loaded model101print('summary of loaded model')102new_model.summary()103# Load test images104print('reading test images...')105test_generator = test_datagenerator()106# Get the filenames from the generator107fnames = test_generator.filenames108# Get the ground truth from generator109ground_truth = test_generator.classes110# Get the label to class mapping from the generator111label2index = test_generator.class_indices112# Getting the mapping from class index to class label113idx2label = dict((v, k) for k, v in label2index.items())114# Get the predictions from the model using the generator115print('predicting on the test images...')116prediction_start = time.clock()117predictions = new_model.predict_generator(test_generator,118                                          steps=test_generator.samples / test_generator.batch_size,119                                          verbose=0)120prediction_finish = time.clock()121prediction_time = prediction_finish - prediction_start122predicted_classes = np.argmax(predictions, axis=1)123errors = np.where(predicted_classes != ground_truth)[0]124print("No. of errors = {}/{}".format(len(errors), test_generator.samples))125correct_predictions = np.where(predicted_classes == ground_truth)[0]126print("No. of correct predictions = {}/{}".format(len(correct_predictions), test_generator.samples))127print("Test Accuracy = {0:.2f}%".format(len(correct_predictions)*100/test_generator.samples))128print("Predicted in {0:.3f} minutes!".format(prediction_time/60))129# original = load_img('{}/{}'.format(test_dir, fnames[correct_predictions[2]]))130# print(original)131# # Show the errors132# for i in range(len(errors)):133#     pred_class = np.argmax(predictions[errors[i]])134#     pred_label = idx2label[pred_class]135#136#     title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(137#         fnames[errors[i]].split('/')[0],138#         pred_label,139#         predictions[errors[i]][pred_class])140#141#     original = load_img('{}/{}'.format(test_dir, fnames[errors[i]]))142#     plt.figure(figsize=[7, 7])143#     plt.axis('off')144#     plt.title(title)145#     plt.imshow(original)146#     plt.show()147#148# # Show the correct predictions149# for i in range(len(correct_predictions)):150#     pred_class = np.argmax(predictions[correct_predictions[i]])151#     pred_label = idx2label[pred_class]152#153#     title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(154#         fnames[correct_predictions[i]].split('/')[0],155#         pred_label,156#         predictions[correct_predictions[i]][pred_class])157#158#     original = load_img('{}/{}'.format(test_dir, fnames[correct_predictions[i]]))159#     plt.figure(figsize=[7, 7])160#     plt.axis('off')161#     plt.title(title)162#     plt.imshow(original)163>>>>>>> 2ca6a6a72573981894106df4e30dd0bd62f05584...

random.py

Source:random.py

...166	print 'TWOPI         =', TWOPI167	print 'LOG4          =', LOG4168	print 'NV_MAGICCONST =', NV_MAGICCONST169	print 'SG_MAGICCONST =', SG_MAGICCONST170	test_generator(N, 'random()')171	test_generator(N, 'normalvariate(0.0, 1.0)')172	test_generator(N, 'lognormvariate(0.0, 1.0)')173	test_generator(N, 'cunifvariate(0.0, 1.0)')174	test_generator(N, 'expovariate(1.0)')175	test_generator(N, 'vonmisesvariate(0.0, 1.0)')176	test_generator(N, 'gammavariate(0.5, 1.0)')177	test_generator(N, 'gammavariate(0.9, 1.0)')178	test_generator(N, 'gammavariate(1.0, 1.0)')179	test_generator(N, 'gammavariate(2.0, 1.0)')180	test_generator(N, 'gammavariate(20.0, 1.0)')181	test_generator(N, 'gammavariate(200.0, 1.0)')182	test_generator(N, 'gauss(0.0, 1.0)')183	test_generator(N, 'betavariate(3.0, 3.0)')184def test_generator(n, funccall):185	import time186	print n, 'times', funccall187	code = compile(funccall, funccall, 'eval')188	sum = 0.0189	sqsum = 0.0190	smallest = 1e10191	largest = -1e10192	t0 = time.time()193	for i in range(n):194		x = eval(code)195		sum = sum + x196		sqsum = sqsum + x*x197		smallest = min(x, smallest)198		largest = max(x, largest)...

problem5test.py

Source:problem5test.py

1#Euler Problem 5 Test23import unittest4import problem556class TestProblem5(unittest.TestCase):7 8    def test_count_generator(self):9        test_generator = problem5.count_generator(2520,20)10        self.assertEqual(next(test_generator), 2520)11        self.assertEqual(next(test_generator), 2540)12        self.assertEqual(next(test_generator), 2560)13        14        test_generator = problem5.count_generator(2,3)15        self.assertEqual(next(test_generator), 2)16        self.assertEqual(next(test_generator), 5)17        self.assertEqual(next(test_generator), 8)18        19    def test_is_multiple_of_integers_under_19(self):20        self.assertEqual(problem5.is_multiple_of_integers_under_19(2520), False)21        self.assertEqual(problem5.is_multiple_of_integers_under_19(670442572800), True)22    23    def test_smallest_multiple(self):24        self.assertEqual(problem5.smallest_multiple(), 232792560)25        26if __name__ == '__main__':
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