Keras Python

Keras is an open-source deep learning library written in Python. It provides a high-level neural networks API, making it easier to build and experiment with deep learning models. Keras can run on top of other deep learning frameworks such as TensorFlow and Theano.

However, as of my last update in September 2021, there have been some changes in the deep learning landscape, and Keras has been integrated more tightly with TensorFlow as part of the TensorFlow library. So, if you want to use Keras in Python, you can do so via TensorFlow.

Here’s how you can get started with Keras using TensorFlow as the backend:

Install TensorFlow: If you don’t have TensorFlow installed, you can do so using pip:

bash

pip install tensorflow

Import TensorFlow and Keras:

python

import tensorflow as tf

from tensorflow import keras

Build a simple neural network using Keras:

python

# Create a sequential model

model = keras.Sequential()
# Add layers to the model

model.add(keras.layers.Dense(units=64, activation='relu', input_shape=(input_size,)))

model.add(keras.layers.Dense(units=32, activation='relu'))

model.add(keras.layers.Dense(units=10, activation='softmax'))
# Compile the model

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

# Print a summary of the model architecture model.summary()

Train the model using your data:

python

# Assuming you have your training data (X_train and y_train) and test data (X_test and y_test)

model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

Evaluate the model:

python

loss, accuracy = model.evaluate(X_test, y_test)

print(f"Test accuracy: {accuracy}")

Keras provides a simple and intuitive interface for creating and training deep learning models. However, please note that new developments might have occurred since my last update, so I recommend checking the official documentation of TensorFlow and Keras for the most up-to-date information.

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