Overview: This code, developed by Mohammad Hassan Heydari, implements a classifier for IMDB movie reviews. It uses the Keras library and TensorFlow Datasets to train a model that can classify movie reviews as positive or negative.
Features:
- Loads the IMDB reviews dataset from TensorFlow Datasets.
- Preprocesses the data by tokenizing and padding the sequences.
- Builds a sequential model using an embedding layer, followed by a flatten layer, and two dense layers.
- Compiles the model with binary cross-entropy loss and the Adam optimizer.
- Trains the model on the padded training data for 10 epochs, using the validation data for evaluation.
Usage:
- Install the required dependencies, including Keras, TensorFlow Datasets, and numpy.
- Load the IMDB reviews dataset using the tfds.load() function.
- Preprocess the data by converting it to sequences and padding them using the Tokenizer and pad_sequences functions.
- Build the sequential model with an embedding layer, flatten layer, and dense layers.
- Compile the model with binary cross-entropy loss and the Adam optimizer.
- Train the model using the fit() function, providing the padded training data and labels, as well as the validation data.
Contributions: Contributions to this project are welcome. If you would like to contribute, please follow the guidelines mentioned in the repository.
License: This code is released under an open-source license. Please refer to the license file in the repository for more information.
Contact: If you have any questions or feedback regarding this code, feel free to contact Mohammad Hassan Heydari at [heydari0081@gmail.com].
Please note that this is a general description, and you may need to customize it further based on your specific project details and preferences.