tf.keras.optimizers.SGD | TensorFlow Core v2.7.0
www.tensorflow.org › tf › kerasA Tensor, floating point value, or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to 0.01. momentum. float hyperparameter >= 0 that accelerates gradient descent in the relevant direction and dampens oscillations.
sklearn.linear_model.SGDClassifier — scikit-learn 1.0.2 ...
scikit-learn.org › stable › modulesThis estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning via the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance.
SGD - Keras
keras.io › api › optimizersname: Optional name prefix for the operations created when applying gradients. Defaults to "SGD". **kwargs: Keyword arguments. Allowed to be one of "clipnorm" or "clipvalue". "clipnorm" (float) clips gradients by norm; "clipvalue" (float) clips gradients by value. Usage: >>>
SGD - Keras
https://keras.io/api/optimizers/sgdArguments. learning_rate: A Tensor, floating point value, or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule, or a callable that takes no arguments and returns the actual value to use.The learning rate. Defaults to 0.01. momentum: float hyperparameter >= 0 that accelerates gradient descent in the relevant direction and dampens …