def load_images_for_keras( image_dir: str = "../images/asl_alphabet_train/asl_alphabet_train", color_mode: str = "grayscale", image_size: Tuple[int, int] = (80, 80), validation_split: float = .2, seed: int = 1234321) -> Tuple[any, any]: """ Get the Keras dataset for the images. :param image_dir: The directory of the images :param color_mode: Whether or not to grayscale :param image_size: The size images should be scaled to :param validation_split: How to split the training and validation sets by :param seed: Random seed :return: A tuple of the training and validation datasets """ train = image_dataset_from_directory(image_dir, labels="inferred", color_mode=color_mode, image_size=image_size, validation_split=validation_split, subset="training", seed=seed) valid = image_dataset_from_directory(image_dir, labels="inferred", color_mode=color_mode, image_size=image_size, validation_split=validation_split, subset="validation", seed=seed) return train, valid
BASE_PATH = "./data" # define the batch size BATCH_SIZE = 64 OUTPUT_PATH = path.sep.join([BASE_PATH, "output"]) img_height = 128 img_width = 64 data_aug = keras.preprocessing.image.ImageDataGenerator(zoom_range=.1, horizontal_flip=True, rotation_range=8, width_shift_range=.2, height_shift_range=.2) train_ds = pc.image_dataset_from_directory(BASE_PATH + "/bbox_train", validation_split=0.2, label_mode="categorical", subset="training", seed=123, shuffle=False, image_size=(256, 256), batch_size=BATCH_SIZE) val_ds = pc.image_dataset_from_directory(BASE_PATH + "/bbox_test", validation_split=0.2, subset="validation", seed=123, image_size=(img_height, img_width), batch_size=BATCH_SIZE) class_names = train_ds.class_names print(class_names) normalization_layer = layers.experimental.preprocessing.Rescaling(1. / 255) normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y)) image_batch, labels_batch = next(iter(normalized_ds))
data_dir = keras.utils.get_file(origin=dataset_url, fname="BSR", untar=True) root_dir = os.path.join(data_dir, "BSDS500/data") """ We create training and validation datasets via `image_dataset_from_directory`. """ crop_size = 300 upscale_factor = 3 input_size = crop_size // upscale_factor batch_size = 4 train_ds = image_dataset_from_directory( root_dir, batch_size=batch_size, image_size=(crop_size, crop_size), validation_split=0.2, subset="training", seed=1337, label_mode=None, ) valid_ds = image_dataset_from_directory( root_dir, batch_size=batch_size, image_size=(crop_size, crop_size), validation_split=0.2, subset="validation", seed=1337, label_mode=None, ) """
from keras.preprocessing import image_dataset_from_directory image_dataset_from_directory( '/tf/notebooks/Keum/data/train/', labels="inferred", label_mode="int", class_names=None, color_mode="rgb", batch_size=32, image_size=(256, 256), shuffle=True, seed=None, validation_split=None, subset=None, interpolation="bilinear", follow_links=False, )
from keras.callbacks import EarlyStopping from sklearn.utils import class_weight from keras.optimizers import SGD from sklearn.svm import SVC from sklearn import metrics import numpy as np import tensorflow as tf #Path do dataset batch = 1 train_path = 'C:/Users/Labmint/Documents/Visao/base_padding/training_set' test_path = 'C:/Users/Labmint/Documents/Visao/base_padding/validation_set' train_set = image_dataset_from_directory(train_path, batch_size=batch, labels='inferred', label_mode='int', image_size=(355, 370), shuffle=False) test_set = image_dataset_from_directory(test_path, batch_size=batch, labels='inferred', label_mode='int', image_size=(355, 370), shuffle=False) #Carregando a rede #As outras redes podem ser importadas VGG16, MobileNetV2 model = EfficientNetB6(include_top=False, input_shape=(355, 370, 3), pooling='avg', weights='imagenet')
# %% from keras.preprocessing import image_dataset_from_directory from tensorflow import data AUTOTUNE = data.AUTOTUNE image_height = 64 image_width = 64 batch_size = 32 dataset = 'isochronous-dataset' X_raw_train = image_dataset_from_directory(dataset, validation_split=0.2, subset='training', seed=0, image_size=(image_height, image_width), color_mode='grayscale', batch_size=batch_size) X_raw_test = image_dataset_from_directory(dataset, validation_split=0.2, subset='validation', seed=0, image_size=(image_height, image_width), color_mode='grayscale', batch_size=batch_size) class_names = X_raw_train.class_names print(class_names) # %%