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. Once the flower dataset has been downloaded using the ‘get_file’ method, it will be loaded into the environment to work with it.The flower ... Read More

Let's say we have flower dataset. The flower dataset can be downloaded using a google API that basically links to the flower dataset. The ‘get_file’ method can be used to pass the API as a parameter. Once this is done, the data gets downloaded into the environment.It can be visualized ... Read More

The flower dataset can be configured for performance with the help of buffer prefetch, shuffle method, and cache method. Buffered prefetching can be used to ensure that the data can be taken from disk without having I/O become blocking. Dataset.cache() keeps the images in memory after they have been loaded ... Read More

The flower dataset can be pre-processed using the keras preprocessing API. It has a method named ‘image_dataset_from_directory’ that takes the validation set, the directory where data is stored, and other parameters to process the dataset.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will ... Read More

The flower dataset can be split into training and validation set, using the keras preprocessing API, with the help of the ‘image_dataset_from_directory’ which asks for the percentage split for the validation set.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?An image classifier is created ... Read More

The flower dataset can be explored using the keras sequential API with the help of the ‘PIL’ package and the ‘Image.open’ method. Different subdirectories have different types of images of flowers, which can be indexed and displayed on the console.Read More: What is TensorFlow and how Keras work with TensorFlow ... Read More

The flower dataset can be downloaded using the keras sequential API with the help of google API that stores the dataset. The ‘get_file’ method is used with the API (URL) to fetch the dataset, and store it in memory.Read More: What is TensorFlow and how Keras work with TensorFlow to ... Read More

A convolutional neural network can be evaluated using the ‘evaluate’ method. This method takes the test data as its parameters. Before this, the data is plotted on the console using ‘matplotlib’ library and ‘imshow’ methods.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?Convolutional neural ... Read More

A convolutional neural network can be trained and compiled using the ‘train’ method and the ‘fit’ method respectively. The ‘epoch’ value is provided in the ‘fit’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful ... Read More

A dense layer can be added to the sequential model using the ‘add’ method, and specifying the type of layer as ‘Dense’. The layers are first flattened, and then a layer is added. This new layer will be applied to the entire training dataset.Read More: What is TensorFlow and how ... Read More