#TENSORFLOW DATA GENERATOR HOW TO#

def _getitem_(self, index): # Generate one batch of data # Generate indices of the batch index = self.index # Find list of IDs batch = for k in index] # Generate data X, y = self._get_data(batch) return X, yīasically, we just obtained the shuffled indices and called the dataset from a different method and returned it to the caller. Next is the _getitem_ method which is called with the batch number as an argument to obtain a given batch of data. def _len_(self): # Denotes the number of batches per epoch return len(self.indices) // self.batch_size It essentially returns the number of steps in an epoch, using the samples and the batch size.

def on_epoch_end(self): self.index = np.arange(len(self.indices)) if self.shuffle = True: np.random.shuffle(self.index)īasically, we shuffled the order of the dataframe rows in this snippet.Īnother utility method we have is _len_. The on_epoch_end method is a method that is called after every epoch. This could also be a directory name from where you can load the data. Hence, we define the x_col and y_col parameters. Note that here, we assume the path to the data is in a dataframe column. def _init_( self, df, x_col, y_col=None, batch_size=32, num_classes=None, shuffle=True ): self.batch_size = batch_size self.df = dataframe self.indices = self.df.index.tolist() self.num_classes = num_classes self.shuffle = shuffle self.x_col = x_col self.y_col = y_col self.on_epoch_end()įirst, we define the constructor to initialize the configuration of the generator. You just have to fill the blanks/replace certain variables with your own logic.Īs mentioned earlier, we will subclass the tf. API. This will be a very generic implementation and hence can be directly copied. We finally start with the implementation. Hence, we tackle this issue by implementing a custom data generator. And, loading all the data at once isn’t affordable. Here, you obviously cannot use ImageDataGenerator. For example, say you are working on a multi-modal classification problem where you need to process text and image data simultaneously. There are times when a model is fed with multiple types of inputs at once. Model training is not limited to a single type of input and target. One may include preprocessing steps like scaling, augmentation on images that would be directly applied to the images in real-time. The ImageDataGenerator API provides features for pipelining of image data from directories as well as from paths mentioned in a dataframe. datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True )

#TENSORFLOW DATA GENERATOR HOW TO#

ImageDataGeneratorįirst things first, we will now see how to use the ImageDataGenerator API for dynamic image pipelining and hence, address the need for implementing custom ones. In this article, we will see how to subclass the tf. class to implement custom data generators. Another advantage of this pipeline is, one can easily apply preprocessing routines on these mini-batches of data as they are prepared to feed the model. They can generate the model input dynamically thus forming a pipeline from the storage to the RAM to load the data as and when it is required. Well, the solution to this can be loading the mini-batches fed to the model dynamically.

Wyd? Purchasing more RAM is obviously isn’t an option. Consider a scenario where you have lots of data, so much that you cannot have all of it at once in the RAM. Data Generators is one of the most useful features of the Keras API.

Tensorflow data generator