How to use the Embedding layer in Keras?

10 months ago

Liam

2 minutes

To use the Embedding layer in Keras, you can achieve it by following these steps:

Import the necessary libraries.

from keras.models import Sequential
from keras.layers import Embedding

Build a Sequential model:

model = Sequential()

Add an Embedding layer to the model:

model.add(Embedding(input_dim, output_dim, input_length))

In the code above:

Input_dim is the size of the vocabulary, which is the maximum index value of the input data plus one.
The output_dim is the dimension of the embedding vector, which is typically chosen to be a smaller value such as 50 or 100.
input_length refers to the length of the input sequence, which is the length of each input sample.

Compile the model and train it:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))

During the training process, the Embedding layer learns to map the input data into a representation in the embedding space. By utilizing the Embedding layer, it is possible to convert high-dimensional sparse input data into low-dimensional dense embedding representations, thereby enhancing the model’s performance and generalization capabilities.