- Keras Tutorial
- Keras - Home
- Keras - Introduction
- Keras - Installation
- Keras - Backend Configuration
- Keras - Overview of Deep learning
- Keras - Deep learning
- Keras - Modules
- Keras - Layers
- Keras - Customized Layer
- Keras - Models
- Keras - Model Compilation
- Keras - Model Evaluation and Prediction
- Keras - Convolution Neural Network
- Keras - Regression Prediction using MPL
- Keras - Time Series Prediction using LSTM RNN
- Keras - Applications
- Keras - Real Time Prediction using ResNet Model
- Keras - Pre-Trained Models
- Keras Useful Resources
- Keras - Quick Guide
- Keras - Useful Resources
- Keras - Discussion
Keras - Embedding Layer
It performs embedding operations in input layer. It is used to convert positive into dense vectors of fixed size. Its main application is in text analysis. The signature of the Embedding layer function and its arguments with default value is as follows,
keras.layers.Embedding ( input_dim, output_dim, embeddings_initializer = 'uniform', embeddings_regularizer = None, activity_regularizer = None, embeddings_constraint = None, mask_zero = False, input_length = None )
Here,
input_dim refers the input dimension.
output_dim refers the dimension of the dense embedding.
embeddings_initializer refers the initializer for the embeddings matrix
embeddings_regularizer refers the regularizer function applied to the embeddings matrix.
activity_regularizer refers the regularizer function applied to the output of the layer.
embeddings_constraint refers the constraint function applied to the embeddings matrix
mask_zero refers the input value should be masked or not.
input_length refers the length of input sequence.
