COLOR_RGB2GRAY ) # to reduce it to one channel to match the shape # this is where you preprocess the image # make sure to resize it to be self.output_size batch_size ) def _getitem_ (self, idx ) : # Initializing Batch # that one in the shape is just for a one channel images # if you want to use colored images you might want to set that to 3 indices ) def _len_ (self ) : return int ( len (self. on_epoch_end ( ) def on_epoch_end (self ) : The complete code class DataGenerator (Sequence ) : def _init_ (self, csv_file, base_dir, output_size, shuffle = False, batch_size = 10 ) : """ ) # note you could also make a validation generator and pass it here like normal datasets # back in the days you had to do this # model.fit_generator(train_gen. Shuffle = True ) # compile the model first of course # now let's train the model You can also easily make a validation generator and validate your model against that, all you need to do is make a new instance of the DataGenerator class, and pass in the validation csv and base directory and you're good to go. Now you are ready to fit the model to this generator. to_numpy ( ) # if you have any preprocessing for # the labels too do it here imread ( ) # this is where you preprocess the image # make sure to resize it to be self.output_size indices for i, data_index in enumerate (indices ) : batch_size, 4, 1 ) ) # get the indices of the requested batch This function should return a preprocessed batch of data def _getitem_ (self, idx ) : # Initializing Batch # that one in the shape is just for a one channel images # if you want to use colored images you might want to set that to 3 You might think splitting this into multiple functions would be a good idea. This will only be fired when keras trys to load a batch, which will save our memory. In this function we shall load and preprocess the images. This function gets called on indexing or slicing like data_generator or data_generator and the index is passed as a parameter to it. Now let's get serious, the fun part is in the next method which is _getitem_. def _len_ (self ) : return int ( len (self. Now we need to define the length of the data, which is not the number of entries as you might think, it's actually the number of batches, this needs to be accessible by the len function in python so we need to define the _len_ method. We call this method in the initializer because we need the indeces attribute to be set at the begining of the first epoch, otherwise we will get an error telling us that the class has no attribute "indecies" def on_epoch_end (self ) : Now let's define some special methods starting with the one called in the initializer on_epoch_end() that is called after each epoch as the name may suggest, duh! :param batch_size: The size of each batch returned by _getitem_ :param shuffle: shuffle the data after each epoch :param output_size: image output size after preprocessing :param base_dir: the directory in which all images are stored :param csv_file: file in which image names and numeric labels are stored utils import SequenceĬlass DataGenerator (Sequence ) : def _init_ (self, csv_file, base_dir, output_size, shuffle = False, batch_size = 10 ) : """ It will also take the output shape of the batchįrom tensorflow. The directory containing all of the images.Let's define an initializer, the initializer is going to take the information needed to get the data such as: So your features are images and labels are (x, y, h, w) for coordinate and dimensions of the containing box, and the labels and image names are stored in a csv file. Working with images is a good example for this, so let's say that you have pictures of objects that you need to localize, Tensorflow keras has a Sequence class that can be used for this purpose. Python generators are lazy which means they are iterables that give you the data upon request, unlike regular lists that just store the data in memory all the time. If you're dealing with a small dataset, that might work, but that is just a waste of resources, and worse if you're working on a huge dataset like the imageNet dataset, this won't work at all. Tf.TensorSpec(shape=(None,49), dtype=tf.Believe it or not, but loading the entire dataset in memory is NOT the best idea. Train_dataset = tf._generator(_generator, output_types=(, Yield dict(tokenizer(text, truncation=True, padding=True, return_tensors='tf')), label I have been trying to write a generator for DistillBertFast model # Generatorĭef _generator(text=train_texts, label=Y_oh_train, batch_size=1):
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