python-kerasHow do Python Keras and TensorFlow compare in developing machine learning models?

Python Keras and TensorFlow are two popular open-source frameworks for developing machine learning models. Keras is a high-level API built on top of TensorFlow. It provides a simpler and more intuitive way to define and train deep learning models.

TensorFlow is a lower-level API that provides more flexibility and control over the model building process. It allows developers to define the architecture of the model in more detail, and also provides more advanced features such as distributed training and custom layers.

Here is an example of a basic neural network written in both Keras and TensorFlow:

Keras

model = Sequential()
model.add(Dense(32, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

TensorFlow

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(32, input_shape=(784,)))
model.add(tf.keras.layers.Activation('relu'))
model.add(tf.keras.layers.Dense(10))
model.add(tf.keras.layers.Activation('softmax'))
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

In summary, Keras provides a simpler and more intuitive way to define and train deep learning models, while TensorFlow provides more control and flexibility over the model building process.

Helpful links

• Keras Documentation
• TensorFlow Documentation

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