Model = VGG_16(img_rows, img_cols, img_channels, nb_classes) Model='VGG_16', data_dir='/home/rcamachobarranco/datasets') (X_train, y_train), (X_test, y_test) = yelp_data(dtype=np.float32, grayscale=False, pixels=img_rows, batches=3, # the data, shuffled and split between train and test sets

Model.fit_generator(datagen.flow(x_train, y_train,batch_size=self.batch_size),steps_per_epoch=erations,epochs=self.epochs,callbacks=cbks,validation_data=(x_test, y_test)) Print('Using real-time data augmentation.')ĭatagen = ImageDataGenerator(horizontal_flip=True,width_shift_range=0.125,height_shift_range=0.125,fill_mode='constant',cval=0.) Tb_cb = TensorBoard(log_dir=self.log_filepath, histogram_freq=0)Ĭbks = # Save the best model during each training checkpointĬheckpoint = ModelCheckpoint(self.model_filename, X_train, x_test = lor_preprocessing(x_train, x_test) Y_test = _categorical(y_test, self.num_classes) Y_train = _categorical(y_train, self.num_classes) (x_train, y_train), (x_test, y_test) = cifar10.load_data() (rounds * augmentations, 1, 1, 1)),īatch_size=rounds * augmentations))įor array_aug, label_aug in zip(array_augs, label_augs): Vertical_flip = augmentations,Īrray_augs, label_augs = next(auggen.flow(np.tile(array, Zoom_range = augmentations,Ĭhannel_shift_range = augmentations, Samplewise_std_normalization = augmentations, Samplewise_center = augmentations,įeaturewise_std_normalization = augmentations, Print ('Final training weights saved to ' + save_name)ĭef gen_augment_arrays(array, label, augmentations, rounds = 1):Īuggen = ImageDataGenerator(featurewise_center = augmentations, Model.save_weights(direct + '/weights/' + save_name, overwrite=True) Print('Training completed in %f seconds' % tyme) #print ('Weights saved to ' + direct + '/weights/' + save_name + 'E-%d.hdf5' % (e+1)) #model.save_weights((direct + '/weights/' + save_name + 'E-%d.hdf5' ) % (e+1), overwrite=True) Y_pred = model.predict_classes(X_test, batch_size=20) Model.fit_generator(datagen.flow(X_batch, y_batch, batch_size=18, shuffle=True), Y_test = np_utils.to_categorical(y_test, classes)ĭatagen = ImageDataGenerator(rotation_range=30, width_shift_range=0.01, height_shift_range=0.01, horizontal_flip=True, vertical_flip=True) (X_test, y_test, y_conf) = load.load_test_data() Samples_per_epoch=len(X_train), nb_epoch=nb_epoch, validation_data=(X_val1, Y_val1), Model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), X_train = np.concatenate((X_train, X_val1), axis=0) _weights(self.weights_path,overwrite=True) History = _generator(datagen.flow(X_train, Y_train, batch_size=self.batch_size), # fit the model on the batches generated by datagen.flow() Height_shift_range=height_shift_range)# randomly shift images vertically (fraction of total height)) Width_shift_range=width_shift_range, # randomly shift images horizontally (fraction of total width) # this will do preprocessing and realtime data augmentationĭatagen = ImageDataGenerator(rotation_range=rotation_range, # randomly rotate images in the range (degrees, 0 to 180) Y_test = np_utils.to_categorical(y_test, self.nb_classes) Y_train = np_utils.to_categorical(y_train, self.nb_classes) # convert class vectors to binary class matrices X_test = X_test.reshape(X_test.shape, 1, self.img_rows, self.img_cols) X_train = X_train.reshape(X_train.shape, 1, self.img_rows, self.img_cols) X_train, X_test, y_train, y_test = train_test_split(ĭata, labels, test_size=0.33, random_state=42) # Get the label from the image path and then get the index from the letters list

Img = cv2.imread(self.letters_folder+'/'+imgName, cv2.IMREAD_GRAYSCALE) ''' Trains the model using the dataset in letters_folder '''įor imgName in listdir(self.letters_folder): Model.evaluate_generator(img_gen.flow(x_train, y_train, batch_size=16))ĭef train(self,save_model_to_file = True,rotation_range = 20,width_shift_range=0.5,height_shift_range=0.2): Validation_data=img_gen.flow(x_test, y_test,Īssert history.history > 0.75 History = model.fit_generator(img_gen.flow(x_train, y_train, batch_size=16), Layers.Dense(y_test.shape, activation='softmax') Layers.Conv2D(filters=4, kernel_size=(3, 3), (x_train, y_train), (x_test, y_test) = get_test_data(num_train=500, Img_gen = ImageDataGenerator(rescale=1.) # Dummy ImageDataGenerator

Def test_image_data_generator_training():

Datagen flow keras