The flower dataset would have given a certain percentage of accuracy when a model is created. If it is required to configure the model for performance, a function is defined that performs the buffer prefetch for the second time, and then it is shuffled. This function is called on the training dataset to improve the performance of the model.
We will be using the flowers dataset, which contains images of several thousands of flowers. It contains 5 sub-directories, and there is one sub-directory for every class.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
print("A function is defined that configures the dataset for perfromance") def configure_for_performance(ds): ds = ds.cache() ds = ds.shuffle(buffer_size=1000) ds = ds.batch(batch_size) ds = ds.prefetch(buffer_size=AUTOTUNE) return ds print("The function is called on training dataset") train_ds = configure_for_performance(train_ds) print("The function is called on validation dataset") val_ds = configure_for_performance(val_ds)
Code credit: https://www.tensorflow.org/tutorials/load_data/images
A function is defined that configures the dataset for perfromance The function is called on training dataset The function is called on validation dataset