Found 178 Articles for Tensorflow

A sequential model can be created using the ‘Sequential’ API that uses the ‘ layers.experimental.preprocessing.Rescaling’ method. The other layers are specified while created the model.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 in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.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 ... Read More

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 data can be standardized by introducing a normalization layer in the model. This layer is called the ‘Rescaling’ layer, which is applied to the entire dataset using the ‘map’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We are using the Google Colaboratory to ... 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 off disk during the first epoch. Dataset.prefetch() will overlap the data preprocessing and model execution while training.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?The Keras Sequential API is used, which is helpful in building a sequential model that is used to work with ... 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 using the ‘matplotlib’ library. The ‘imshow’ method is used to display the image on the console. 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 in building a sequential model that is used to work with ... 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 use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor. An image classifier is created using a keras.Sequential model, and data is loaded using ... 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 using a keras.Sequential model, and data is loaded using preprocessing.image_dataset_from_directory. Data is efficiently loaded off disk. Overfitting is identified and techniques are applied to mitigate it. These techniques include data augmentation, and dropout. There are images of 3700 flowers. This dataset contaisn 5 sub directories, and there is one sub ... 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 to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.An image classifier is created using a keras.Sequential model, and ... 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 create Neural Networks?A neural network that contains at least one layer is known as a convolutional layer. Convolutional neural networks have been used to produce great results for a specific kind of problems, such as image recognition.  An image classifier is created using a keras.Sequential model, and data is loaded ... 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 networks have been used to produce great results for a specific kind of problems, such as image recognition.  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 ... 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 in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. Convolutional neural networks have been used to produce ... Read More