Keras layers are the fundamental building blocks for constructing neural networks. They can be used to create both simple and complex models, and are designed to be highly modular and easy to use.

Overview

Keras provides a wide variety of layers that can be used for different purposes in your neural network. These layers include:

  • Dense: Fully connected layer.
  • Convolutional: Convolutional layer, used for image data.
  • Recurrent: Recurrent layer, used for sequence data.
  • Embedding: Embedding layer, used for converting categorical data into dense vectors.
  • Activation: Activation layer, used to introduce non-linearities into the network.

For more information on each type of layer, please refer to the following sections.

Dense Layer

The Dense layer is a fully connected neural network layer. It is typically used as the main layer in a neural network.

  • Input shape: (n_samples, n_features)
  • Output shape: (n_samples, n_units)

Example Usage

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(32,)))
model.add(Dense(10, activation='softmax'))

Convolutional Layer

The Conv2D layer is a convolutional layer, used for processing image data. It applies various filters to the input image and extracts features.

  • Input shape: (n_samples, height, width, channels)
  • Output shape: (n_samples, height, width, filters)

Example Usage

from keras.models import Sequential
from keras.layers import Conv2D

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

Recurrent Layer

The LSTM layer is a recurrent layer, used for processing sequence data. It is designed to remember information over time.

  • Input shape: (n_samples, timesteps, n_features)
  • Output shape: (n_samples, timesteps, n_units)

Example Usage

from keras.models import Sequential
from keras.layers import LSTM

model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(100, 1)))

Embedding Layer

The Embedding layer is used to convert categorical data into dense vectors. It is commonly used in natural language processing tasks.

  • Input shape: (n_samples, n_features)
  • Output shape: (n_samples, n_units)

Example Usage

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

model = Sequential()
model.add(Embedding(input_dim=10000, output_dim=32, input_length=10))

For more information on Keras layers, please visit the Keras Layers API Documentation page.