<jupyter_start><jupyter_text>**Testing**<jupyter_code>val_datagen = ImageDataGenerator(rescale = 1./255)
validation_set = val_datagen.flow_from_directory('/content/output/val/',
                                            target_size = (224, 224),
                                            batch_size = batch_size,
                                            class_mode = 'categorical')
predict = model.predict_generator(validation_set,steps = len(validation_set))
print(predict.shape)
labels = np.full(predict.shape[0],1,np.float32)
test1Set = np.concatenate((predict,np.asarray([labels]).T), axis=1)
print(test1Set.shape)
#np.savetxt('test1Set.csv',test1Set, delimiter=',',fmt='%.15f')
validation_set = val_datagen.flow_from_directory('/content/NOA/val/',
                                            target_size = (224, 224),
                                            batch_size = batch_size)
predict = model.predict_generator(validation_set,steps = len(validation_set))
labels = np.full(predict.shape[0],0)
test0Set = np.concatenate((predict,np.asarray([labels]).T), axis=1)
print(test0Set.shape)
#np.savetxt('test0Set.csv',test0Set, delimiter=',',fmt='%.15f')
testData = np.concatenate((test1Set,test0Set), axis=0)
print(testData.shape)
#np.savetxt('testData.csv',testData, delimiter=',',fmt='%.15f')
testX = testData[:,:-1]
testY = testData[:,-1]
print(testX.shape)
print(testY.shape)
#clf = pickle.load(open('logRModel.sav', 'rb'))
acc = clf.score(testX,testY)
print(acc)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15817339510258108, 'fraction_numerical': 0.028457974851091992, 'mean_word_length': 3.3448275862068964, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 10, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '536796', 'license': 'MIT', 'n_tokens_mistral': 516, 'n_tokens_neox': 494, 'n_words': 60, 'path': 'Hybrid Model/logRegTraining.ipynb', 'repo_name': 'mkd1997/clothing-recognition', 'size': '1511'}