How to setup a Multi-Layer Perceptron model for review classification in Keras
Review classification is the process of determining the sentiment of a piece of text, such as a product review, and classifying it as positive, negative or neutral. A Multi-Layer Perceptron (MLP) is a type of neural network that can be used for review classification, and Keras is a high-level neural networks API that allows for easy and fast prototyping, and it is written in Python.
The first step in setting up a Multi-Layer Perceptron (MLP) model for review classification using Keras is to prepare the dataset. For review classification, we typically use a dataset of text samples, such as product reviews, along with their corresponding labels indicating whether the review is positive, negative or neutral. The dataset should be divided into a training set and a test set.
Next, we need to preprocess the text data. This involves cleaning the text by removing special characters, stop words, and punctuations. It also involves tokenizing the text into individual words and creating a vocabulary of all the unique words in the dataset. We can also convert the words to numerical values using one-hot encoding or word embeddings like word2vec or GloVe.
Once the text data is preprocessed, we can build the Multi-Layer Perceptron (MLP) model. Keras provides a built-in function for creating a sequential model, which is the type of model used for MLPs. We can add layers to this model, such as an input layer, hidden layers, and an output layer. The input layer receives the preprocessed text data, and the hidden layers process the data and extract features. The output layer produces the review classification.
After building the model, we need to compile it. This involves specifying the optimizer and loss function that will be used during training. The optimizer is responsible for updating the model’s weights during training, and the loss function is used to measure the model’s performance. Additionally, we also need to specify the metrics we want to use to evaluate the model’s performance, such as accuracy or F1-score.
Once the model is compiled, we can train the model on our dataset. This is done by feeding the model text samples from the training set and adjusting the weights based on the performance of the model. This process is repeated for a set number of iterations, known as epochs, until the model reaches a satisfactory level of performance.
After training the model, we can evaluate its performance on the test set using the metrics we specified earlier. This will give us an idea of how well the model will perform on unseen data.
Finally, we can use the trained model to make predictions on new text samples. This can be done by calling the predict function on the model and passing in the text we want to classify.
In summary, setting up a Multi-Layer Perceptron model for review classification using Keras involves preparing a dataset of text samples, preprocessing the text data, building the Multi-Layer Perceptron model, compiling the model, training it on the dataset, evaluating its performance on the test set, and making predictions with new text samples. The model is composed by an input layer, hidden layers and an output layer, which are the key elements of the model. The hidden layers process the data and extract features, and the output layer produces the review classification.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to setup a Multi-Layer Perceptron model for review classification in Keras.
What should I learn from this recipe?
You will learn:
- How to code a keras and tensorflow model in Python.
- How to setup a sequential deep learning model in Python.
- How to setup Early Stopping in a Deep Learning Model in Keras.
- How to split train and test datasets in a Deep Leaning Model in Keras.
- How to incorporate Multiple Layers in a Deep Learning model.
- How to reduce overfitting in a Deep Learning model.
- How to test different OPTIMIZERs and Epoch Sizes in a Deep Learning model.
- How to setup an experiment in a Deep Learning model.
- How to setup CNN layers in Keras for image classification.
- How to classify images using CNN layers in Keras: An application of MNIST Dataset
- How to create simulated data using scikit-learn.
- How to create training and testing dataset using scikit-learn.
- How to train a tensorflow and keras model.
- How to report confusion matrix.
- How to setup a Multi-Layer Perceptron model for review classification in Keras.
How to setup a Multi-Layer Perceptron model for review classification in Keras:
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