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

To build a neural network using Python and Keras, you will need to install Keras and its dependencies. After installation, you can use the following code to create a simple neural network:

# import necessary packages
from keras.models import Sequential
from keras.layers import Dense

# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

# fit model
model.fit(X, Y, epochs=150, batch_size=10)

# evaluate model
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

This code creates a neural network with three layers; an input layer with 8 nodes, a hidden layer with 12 nodes, and an output layer with 1 node. The model is then compiled with a binary cross entropy loss function and an Adam optimizer, and is fit on the data with 150 epochs and a batch size of 10. Finally, the model is evaluated on the data and the accuracy is printed.

Parts of the code:

• from keras.models import Sequential: This imports the Sequential model from the Keras library, which is used to create a linear stack of layers.
• model.add(Dense(12, input_dim=8, activation='relu')): This adds a dense layer with 12 nodes and an input dimension of 8 to the model. The activation function used is the Rectified Linear Unit (ReLU).
• model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']): This compiles the model with a binary cross entropy loss function and an Adam optimizer.
• model.fit(X, Y, epochs=150, batch_size=10): This fits the model on the data with 150 epochs and a batch size of 10.
• scores = model.evaluate(X, Y): This evaluates the model on the data.
• print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)): This prints the accuracy of the model.

Helpful links

• Keras Documentation
• Neural Networks with Keras Tutorial

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