tf.keras.optimizers.Adam

Adam is an optimization algorithm used to update the weights and biases of a neural network during training. It belongs to the family of stochastic gradient descent (SGD) algorithms, but it incorporates adaptive learning rates and momentum to speed up convergence.

In TensorFlow's Keras API, the Adam optimizer is implemented as tf.keras.optimizers.Adam(). Its main parameters include the learning rate, beta1, beta2, and epsilon. The learning rate controls how large the step size is in the weight update. The beta1 and beta2 parameters control the exponential decay rates for the first and second moments of the gradients, respectively. The epsilon parameter is a small value added to the denominator to avoid numerical instability.

Here is an example of how to use Adam optimizer in Keras:

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Create a neural network model
model = Sequential()
model.add(Dense(32, input_dim=10, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compile the model with Adam optimizer
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

In this example, we create a simple neural network with two dense layers. We then compile the model with the Adam optimizer, setting the learning rate to 0.001, beta1 to 0.9, beta2 to 0.999, and epsilon to 1e-08. We also specify the loss function and evaluation metric for the model.