#Tensorflow data generator how to
However, in this tutorial, we are doing to do something different, we will use RNNs as generative models, which means they can learn the sequences of a problem and then generate entirely a new sequence for the problem domain.Īfter reading this tutorial, you will learn how to build an LSTM model that can generate text (character by character) using TensorFlow and Keras in Python. Recurrent Neural Networks ( RNNs) are very powerful sequence models for classification problems.
#Tensorflow data generator generator
Let’s apply this to our generator and see how it goes.Disclosure: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission.
generator: must be a callable object that returns an object that supports the iter() protocol (e.g.from_generator( generator, output_types=None, output_shapes=None, args=None, output_signature=None ) This was the reason we examined our data types and shape above as soon as we built our generator. The output_shapes is really important because our dataset object will return the exact shape we mention inside the output_shapes.In the place of lambda use your data generator object.We will be looking at tf._generator()function which accepts 3 inputs and returns a dataset for us. We are all done creating our generator object with this you can proceed to model but let’s see the magic function that will help us to convert this into a Dataset. Remember don’t apply your augmentations here because these ImageDataGenerator computations are done on CPU hence it will make the training slower.Ĭool. Alright, enough of talking let’s jump into code!įirstly we need to create an ImageDataGenerator object, in this example, I will be parsing the Image ID from a data frame since getting images from a directory is much common than this. Let me state why I consider this cool, we can use ImageDataGenerator as a tool to load in images especially when your Image ID’s in a data frame and directory. I came over this handy method tf._generator() which helps us create a dataset object from the ImageDatagenerator object itself.
It was easy using ImageDataGenerator especially with directory and data frame method to load in images made things easier without any hassle. I wasn't sure how to construct complex pipelines. The tf.data API enables you to build complex input pipelines from simple, reusable pieces. Also, we can optimize the performance of our data pipelines by using prefetch, parallelizing data transformations, and other cool methods. Oh yeah, then I started to look into the tf.data API and it was a bit confusing at first but spending time on that made me realize this could help me writing descriptive and efficient input pipelines. There was no much fun either and the results were poor. It was easy building things with high-level API but as the days go by performance was an issue and I felt constricted on doing things. Since I am new to TensorFlow and tf.data API was something I wasn’t comfortable with. This is just for experiment purposes learning how to use tf._generators() and this dataset seems a suitable one to experiment with. For this experiment, we’ll use a dataset from the AI Crowd competition (live now)