python-kerasHow do I create a neural network using Python and Keras?

Creating a neural network using Python and Keras is a relatively straightforward process.

First, we need to import the necessary libraries:

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

Next, we need to define the model. In this example, we will use a sequential model with two layers:

model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))

The first line creates a sequential model, which is a linear stack of layers. The second line adds a densely connected layer with 12 neurons and an input dimension of 8. The third line adds a densely connected layer with 8 neurons.

Finally, we need to compile and fit the model:

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, Y, epochs=150, batch_size=10)

The first line compiles the model using the categorical crossentropy loss function, the adam optimizer, and accuracy as a metric. The second line fits the model to the data (X and Y) using 150 epochs and a batch size of 10.

Helpful links

• Keras Documentation
• Keras Sequential Model Guide
• Keras Layers Guide
• Keras Compilation Guide
• Keras Fitting Guide

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