python-kerasHow do I use the Keras layers.dense function in Python?

The Keras layers.dense function is a powerful tool for building neural networks in Python. It creates a densely connected layer of neurons, where each neuron is connected to all of the neurons in the previous layer. Here is an example of how to use the layers.dense function in Python:

from keras.layers import Dense

# Create a densely connected layer with 10 neurons
layer = Dense(10)

The code above creates a densely connected layer with 10 neurons. The layer can be used as part of a neural network model, as shown below:

from keras.models import Sequential

# Create a model
model = Sequential()

# Add the layer to the model
model.add(layer)

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])

The code above creates a model and adds the layer to it. The model is then compiled using an optimizer, a loss function, and a list of metrics.

Code explanation

1. from keras.layers import Dense: This imports the Dense class from the Keras layers module.
2. layer = Dense(10): This creates a densely connected layer with 10 neurons.
3. from keras.models import Sequential: This imports the Sequential class from the Keras models module.
4. model = Sequential(): This creates a model.
5. model.add(layer): This adds the layer to the model.
6. model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy']): This compiles the model with an optimizer, a loss function, and a list of metrics.

Helpful links

• Keras Documentation - Dense Layer
• Keras Documentation - Sequential Model

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