![]() Train_data = train_datagen.flow_from_directory(directory= r'/content/drive/MyDrive/cats_and_dogs_filtered/train',Ĭreate another DirectoryIterator object from the testing directory. Lastly, specify the type of label to be binary (i.e., cat or dog). Specify the number of images that each batch will use. Resize the images to a fixed size of 64圆4 pixels. Then specify the directory that stores the training data. It will generate batches of augmented images. They should reflect the original data distribution.Ĭreate a DirectoryIterator object from the training directory. This is because the model uses the test and validation data for evaluation purposes only. You do not need to apply the other augmentation techniques to the test and validation data. Validation_datagen = ImageDataGenerator(rescale= 1./ 255) # define the image data generator for validation Rescale the validation data the same way as the test data. Test_datagen = ImageDataGenerator(rescale= 1./ 255)Ĭreate a final instance of the ImageDataGenerator class for the validation data. # define the image data generator for testing It will normalize the pixel values of the test images to match the format used during training. Train_datagen = ImageDataGenerator(rescale= 1./ 255,Ĭreate another instance of the ImageDataGenerator class for the test data. # define the image data generator for training These techniques will generate new data which contains variations of the original data representing real-world scenarios. In the task of classifying whether an image is a cat or a dog, you can use the flipping, random width, random height, random brightness, and zooming data augmentation techniques. It will generate batches of augmented image data in real time during model training. You will use this object for preprocessing the training data. Creating Instances of the ImageDataGenerator ClassĬreate an instance of the ImageDataGenerator class for the train data. ![]()
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