python-kerasHow can I use the Adam optimizer in TensorFlow?

The Adam optimizer can be used in TensorFlow to optimize the weights of a model during training. It is a popular choice in deep learning applications and is often preferred over other optimizers such as gradient descent. To use the Adam optimizer in TensorFlow, the following code can be used:

# Create model
model = tf.keras.Sequential([
    ...
])

# Compile model
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train model
model.fit(x_train, y_train, epochs=5)

The code above creates a model, compiles it with the Adam optimizer and a learning rate of 0.001, and then trains the model for 5 epochs.

The code can be broken down into the following parts:

1. Create model: This creates the model architecture, such as a neural network, using the TensorFlow Keras API.
2. Compile model: This compiles the model with an optimizer, loss function, and any metrics used to evaluate the model. The Adam optimizer is specified here with a learning rate of 0.001.
3. Train model: This trains the model on the training data for a specified number of epochs.

For more information on using the Adam optimizer in TensorFlow, see the TensorFlow documentation.

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