python-kerasHow can I use the Python Keras library to build a deep learning model?

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. The core data structure of Keras is a model, a way to organize layers.

To use the Python Keras library to build a deep learning model, you can use the Sequential API. This allows you to build a model layer by layer. For example:

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

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

# Train the model with a batch size of 32
model.fit(x_train, y_train, epochs=5, batch_size=32)

This code creates a model with two layers, a fully connected layer with 64 units and a softmax layer with 10 units. It then compiles the model with the categorical crossentropy loss function, SGD optimizer, and accuracy metric. Finally, it trains the model with a batch size of 32.

To learn more about using Keras to build deep learning models, you can refer to the Keras documentation or the TensorFlow tutorial.

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