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How to use test_gendata method in avocado

Best Python code snippet using avocado_python

ResNet152.py

Source:ResNet152.py

1import tensorflow as tf2from keras.applications.densenet import DenseNet1213from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D,GlobalAveragePooling2D4from keras.models import Sequential,Model,load_model5from tensorflow.python.client import device_lib6import numpy as np7from keras.models import Model8from keras.layers import Dense9from keras.applications import ResNet15210from sklearn.metrics import classification_report11import itertools12from keras_preprocessing.image import ImageDataGenerator13from sklearn.metrics import roc_curve, auc, roc_auc_score14import matplotlib.pyplot as plt15from keras import models16from keras import layers17from keras import optimizers18from keras.models import Model,load_model,Sequential19from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D,GlobalAveragePooling2D,BatchNormalization,Activation20##### Pre Processing21from keras_preprocessing.image import ImageDataGenerator22train_gendata = []23valid_gendata = []24test_gendata = []25train_datagen2 = ImageDataGenerator(26    rescale=1. / 255,27    zoom_range=0.2,28    validation_split = 0.2)29model_path = "E://CN240//model//model5-{epoch:02d}-{val_accuracy:.4f}.h5"30train_gen = train_datagen2.flow_from_directory(31directory='C://Users//Admin//Desktop//Newtrain',32target_size=(224, 224),33shuffle = True,34color_mode="rgb",35batch_size=4,36class_mode="categorical",37subset = 'training')38train_gendata.append(train_gen)39    40valid_gen = train_datagen2.flow_from_directory(41directory='C://Users//Admin//Desktop//Newtrain',42target_size=(224, 224),43color_mode="rgb",44batch_size=4,45class_mode="categorical",46subset='validation')47valid_gendata.append(valid_gen)48    49test_gen = train_datagen2.flow_from_directory(50directory='C://Users//Admin//Desktop//testdata',51target_size=(224, 224),52color_mode="rgb",53batch_size=1,54class_mode=None)55test_gendata.append(test_gen)56##### Create ResNet152 Model57model = ResNet152(weights='imagenet',58                    include_top=False,59                    input_shape=(224, 224, 3))60x = model.output61x = GlobalAveragePooling2D()(x)62x = Dense(1024, activation='relu')(x)63x = Dropout(0.55)(x)64x = Dense(512, activation='relu')(x)65x = Dropout(0.55)(x)66predictions = Dense(3, activation= 'softmax')(x)67model_2 = Model(inputs = model.input, outputs = predictions)68model_2.compile(optimizer= 'adam', loss='categorical_crossentropy', metrics=['accuracy'])69##### Train Model70batch_size = 1671    72generator = train_gendata[0]73    74valid = valid_gendata[0]75test = test_gendata[0]76filepath="E://CN240//model_from_fold//model5-{epoch:02d}-{val_accuracy:.4f}.h5"77checkpoint = ModelCheckpoint(filepath, monitor= 'val_accuracy', verbose=1, save_best_only=False, mode='max')78callbacks_list = [checkpoint]79history = model_2.fit(80                generator,81                steps_per_epoch=generator.n/batch_size,82                epochs=30,83                validation_data=valid,84                validation_steps=valid.n/batch_size,85                shuffle=True,86                verbose=1,87                callbacks = callbacks_list)88    89model_2.evaluate_generator(generator=valid,steps=valid.n)90test_gendata[0].reset()91    92import json93from keras.models import model_from_json, load_model94with open('E://CN240//model//model5_architecture.json', 'w') as f:95    f.write(model_2.to_json())96print("Saved model to disk")97##### Ploting Model-Loss Graph98# list all data in history99print(history.history.keys())100# summarize history for accuracy101plt.plot(history.history['accuracy'])102plt.plot(history.history['val_accuracy'])103plt.title('Model Accuracy')104plt.ylabel('Accuracy')105plt.xlabel('Epoch')106plt.legend(['Train', 'Test'], loc='upper left')107plt.show()108# summarize history for loss109plt.plot(history.history['loss'])110plt.plot(history.history['val_loss'])111plt.title('Model Loss')112plt.ylabel('Loss')113plt.xlabel('Epoch')114plt.legend(['Train', 'Test'], loc='upper left')115plt.show()116##### Printing Report117x_labels = test.classes118y_labels = predicted_class_indices119print(classification_report(y_labels, x_labels))120##### Ploting Confusion Matrix121def plot_confusion_matrix(cm, classes, 122                          normalize=False,123                         title = 'Confusion Matrix',124                         cmap=plt.cm.Blues):125  126  plt.imshow(cm, interpolation = 'nearest', cmap=cmap)127  plt.title(title)128  plt.colorbar()129  tick_marks = np.arange(len(classes))130  plt.xticks(tick_marks, classes, rotation = 45)131  plt.yticks(tick_marks, classes)132  133  if normalize:134    cm = cm.astype('float') / cm.sum(axis=1) [:, np.newaxis]135    print("Normalized Confusion Matrix")136  else:137    print("Confusion Matrix without normalization")138  139  print(cm)140  141  thresh = cm.max() / 2.142  143  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):144    plt.text(j,i, cm[i,j],145            horizontalalignment ="center",146            color = "white" if cm[i,j] > thresh else "black")147    plt.tight_layout()148    plt.ylabel('True label')149    plt.xlabel('Predicted label')150cm = confusion_matrix(x_labels, y_labels)151cm_plot_labels = ['Glaucoma','Normal','Others']...

test.py

Source:test.py

1import hashlib2import certifi3import urllib4import os5from pymongo import MongoClient6from pymongo.errors import ConnectionFailure7from PIL import Image8import unittest9from main import decode10from main import validate_password11from main import genData12class Testing(unittest.TestCase):13    def test_validate_password(self):14        self.assertEqual(validate_password(''), 0)15        self.assertEqual(validate_password('Abc123'), 0)16        self.assertEqual(validate_password('Abc123Abc123Abc123Abc123'), 0)17        self.assertEqual(validate_password('AAAAAAAA'), 1)18        self.assertEqual(validate_password('AAAAbbbbc'), 1)19        self.assertEqual(validate_password('123456789'), 2)20        self.assertEqual(validate_password('AAAA12345'), 2)21        self.assertEqual(validate_password('aaaa12345'), 3)22        self.assertEqual(validate_password('5we13aaaa12345'), 3)23        self.assertEqual(validate_password('AAAaaaa12345'), -1)24        self.assertEqual(validate_password('kjs36adfjIS230Rcv5D'), -1)25    26    def test_decode(self):27        script_dir = os.path.dirname(__file__)28        rel_path = '../../test.png'29        abs_img_path = os.path.join(script_dir, rel_path)30        image_to_decode = Image.open(abs_img_path)31        self.assertEqual(decode(image_to_decode, ''), False)32        self.assertEqual(decode(image_to_decode, 'osdfjlj32'), False)33        self.assertEqual(decode(image_to_decode, 'Abcd1234'), False)34        35        password = hashlib.sha256('Abcd1234'.encode()).hexdigest()36        self.assertEqual(decode(image_to_decode, password), 'xyz')37    38    def test_genData(self):39        self.assertEqual(genData("1234"), ['00110001', '00110010', '00110011', '00110100'])40        self.assertEqual(genData("abcd"), ['01100001', '01100010', '01100011', '01100100'])41        self.assertEqual(genData("QWER"), ['01010001', '01010111', '01000101', '01010010'])42        self.assertEqual(genData("!@#$"), ['00100001', '01000000', '00100011', '00100100'])43        self.assertEqual(genData("[];',./"), ['01011011', '01011101', '00111011', '00100111', '00101100', '00101110', '00101111'])44    def test_isConnected(self):45        conn = MongoClient(46            "mongodb+srv://LijuanZhuge:" + urllib.parse.quote(47                "US-65&sR@P5A#@F") + "@cluster0.botulzy.mongodb.net/?retryWrites=true&w=majority", tlsCAFile=certifi.where())48        try:49            conn.finalproject.command('ismaster')50        except ConnectionFailure:51            print("Server not available")52if __name__ == '__main__':...

test_gendata.py

Source:test_gendata.py

2from pydantic import parse_file_as3from validate_cloud_optimized_geotiff import validate4from loopy.run_image import run_image5from loopy.sample import Sample6def test_gendata():7    out = Path("testout")8    run_image(9        Path("sample.tif"),10        out,11        scale=0.497e-6,12        channels=",".join(["Lipofuscin", "DAPI", "GFAP", "NeuN", "OLIG2", "TMEM119"]),13    )14    out = out / "sample"15    assert (out / "sample_1.tif").exists()16    assert (out / "sample_2.tif").exists()17    assert not validate((out / "sample_1.tif").as_posix(), full_check=True)[0]  # No errors18    assert not validate((out / "sample_2.tif").as_posix(), full_check=True)[0]...

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