![]() ![]() The preview of the image generator output Test_datagen = ImageDataGenerator (rescale = 1. 255 ,rotation_range = 3 ,shear_range = 0.01 ,fill_mode = 'nearest' ,zoom_range = 0.1, width_shift_range = 0.05, height_shift_range = 0.05, horizontal_flip = False ) Train_datagen = ImageDataGenerator (rescale = 1. flow (image, batch_size = 1, save_to_dir =PREVIEW_DATA_DIR, save_prefix = 'preview_', save_format = 'jpg' ) :Ĭ += 1 if c > 15 : break # otherwise the generator would loop indefinitely shape )Ĭ = 0 # Images will be saved to PREVIEW_DATA_DIR for batch in datagen. It's important for preventing overfitting of the model. The class labels have been converted to numbers for training purposes, so 2 is the class indexįirst we initialize the data augmentator and test it. We define the import modules and some constants import numpy as npĪnd open the dataset file to take a look into a random sample of the dataset, with its label # Visualize dataset train sample Define the neural network model and train using the generators.Load the dataset and instantiate the train and test generators.Define the generator method for our HDF5 dataset, utilizing data augmentation.The training part can be summarized in a few steps: There is also an option to compress the data, though. Unfortunately it is much bigger than the dataset images folder, because jpg compression is very efficient, and hdf5 stores the information in raw format. There should now be a file named dataset.h5 on the folder where the source file is. resize (img, (IMG_SIZE, IMG_SIZE ), interpolation =cv2. This saves the data to the hard drive in real time, so there is no risk of overflowing the RAM # Train images for i in range ( len (train_addrs ) ) : if i % 200 = 0 and i > 1 : print ( f"Train: Done " ) uint8 )Īt last, we read the images from the folders and add them to the dataset. create_dataset ( 'y_test', ( len (test_addrs ), ), np. create_dataset ( 'y_train', ( len (train_addrs ), ), np. create_dataset ( 'x_test', test_shape, np. create_dataset ( 'x_train', train_shape, np. Test_shape = ( len (test_addrs ), IMG_SIZE, IMG_SIZE, IMG_CHANNELS ) # Create hdf5 file Train_shape = ( len (train_addrs ), IMG_SIZE, IMG_SIZE, IMG_CHANNELS ) It allows us to store any matrix size (dim1_size, dim2_size, dim3_size. Here we specify beforehand the final dataset shape. append ( (img_addr, index ) ) # Shuffle listĭivision = int ( len (addrs ) * TRAIN_TEST_RATIO ) Img_addrs = glob (path + "*" ) for img_addr in img_addrs :Īddrs. Labels = glob (DATADIR + '*/' ) # Create list with (address, class_index)Īddrs = for (index, path ) in enumerate (labels ) : I'm using the following libraries (on python 3) from glob import globįirst read all the images paths using glob and then we save them with a specified numeric index TRAIN_TEST_RATIO = 0.2 # Grab folder names Let's say we're working with a standard image dataset, where each class is a folder and each folder holds the images for that class dataset/ If we want to build more complex neural networks, for tasks such as regression for instance, or even working with data other than standard rgb images, we need flexible ways of storing data. You could also use datagen.flow_from_directory, but still, it's only really useful for very basic data classification. This is really not ideal because datagen.flow loads the whole dataset into memory. Steps_per_epoch = len (x_train ) / 32, epochs =epochs ) flow (x_train, y_train, batch_size = 32 ) , fit (x_train ) # fits the model on batches with real-time data augmentation: Horizontal_flip = True ) # compute quantities required for featurewise normalization # (std, mean, and principal components if ZCA whitening is applied)ĭatagen. Shown bellow the steps 2 and 3, taken from their webpage: (x_train, y_train ), (x_test, y_test ) = cifar10. Finally, use the fit_generator to train the model.Create a generator to stream the data (could be keras' ImageDataGenerator or some other).Let's take the image classification model training for example. The code referred here can he found on github. Also, we'll take a look at a technique for augmenting the data on the fly while training - which is the industry standard. I brefly explain here h5py (the HDF5 library for python), a useful tool that allows us to store a large amount of generic data on the hard drive, while only loading smaller chuncks of it at a time. If the data is larger than your RAM (often the case when dealing with image data), you'll need to load only parts of the dataset from the hard drive at a time. Dealing with large datasets can be tricky in neural networks. ![]()
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