tf.keras.optimizers.Adadelta

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Optimizer that implements the Adadelta algorithm.

Inherits From: Optimizer

tf.keras.optimizers.Adadelta(
    learning_rate=0.001, rho=0.95, epsilon=1e-07, name='Adadelta', **kwargs
)

Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: 1) the continual decay of learning rates throughout training 2) the need for a manually selected global learning rate

Two accumulation steps are required: 1) the accumulation of gradients squared, 2) the accumulation of updates squared.

Initialization:

$$E[g^2]_0 := 0 \text{(Initialize gradient 2nd order moment vector)}$$
$$E[\Delta x^2]_0 := 0 \text{(Initialize 2nd order variable update)}$$
$$t := t + 1$$
$$E[g^2]_t := \rho * E[g^2]_{t-1} + (1 - \rho) * g^2$$
$$\Delta x_t = -RMS[\Delta x]_{t-1} * g_t / RMS[g]_t$$
$$E[\Delta x^2]_t := \rho * E[\Delta x^2]_{t-1} + (1 - \rho) * \Delta x_t^2$$
$$x_t := x_{t-1} + \Delta x_{t}$$

References See M. D. Zeiler (pdf)

Args:

  • learning_rate: A Tensor, floating point value, or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule. The learning rate. To match the exact form in the original paper use 1.0.
  • rho: A Tensor or a floating point value. The decay rate.
  • epsilon: A Tensor or a floating point value. A constant epsilon used to better conditioning the grad update.
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "Adadelta".
  • **kwargs: keyword arguments. Allowed to be {clipnorm, clipvalue, lr, decay}. clipnorm is clip gradients by norm; clipvalue is clip gradients by value, decay is included for backward compatibility to allow time inverse decay of learning rate. lr is included for backward compatibility, recommended to use learning_rate instead.

Attributes:

  • iterations: Variable. The number of training steps this Optimizer has run.
  • weights: Returns variables of this Optimizer based on the order created.

Methods

add_slot

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add_slot(
    var, slot_name, initializer='zeros'
)

Add a new slot variable for var.

add_weight

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add_weight(
    name, shape, dtype=None, initializer='zeros', trainable=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.compat.v1.VariableAggregation.NONE
)

apply_gradients

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apply_gradients(
    grads_and_vars, name=None, all_reduce_sum_gradients=True
)

Apply gradients to variables.

This is the second part of minimize(). It returns an Operation that applies gradients.

The method sums gradients from all replicas in the presence of tf.distribute.Strategy by default. You can aggregate gradients yourself by passing all_reduce_sum_gradients=False.

Example:

grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars), all_reduce_sum_gradients=False)

Args:

  • grads_and_vars: List of (gradient, variable) pairs.
  • name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.
  • all_reduce_sum_gradients: Whether to sum gradients from different replicas in the presense of tf.distribute.Strategy. If False, it's user responsibility to aggregate the gradients. Default to True.

Returns:

An Operation that applies the specified gradients. The iterations will be automatically increased by 1.

Raises:

  • TypeError: If grads_and_vars is malformed.
  • ValueError: If none of the variables have gradients.

from_config

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@classmethod
from_config(
    config, custom_objects=None
)

Creates an optimizer from its config.

This method is the reverse of get_config, capable of instantiating the same optimizer from the config dictionary.

Arguments:

  • config: A Python dictionary, typically the output of get_config.
  • custom_objects: A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter.

Returns:

An optimizer instance.

get_config

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get_config()

Returns the config of the optimizer.

An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration.

Returns:

Python dictionary.

get_gradients

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get_gradients(
    loss, params
)

Returns gradients of loss with respect to params.

Arguments:

  • loss: Loss tensor.
  • params: List of variables.

Returns:

List of gradient tensors.

Raises:

  • ValueError: In case any gradient cannot be computed (e.g. if gradient function not implemented).

get_slot

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get_slot(
    var, slot_name
)

get_slot_names

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get_slot_names()

A list of names for this optimizer's slots.

get_updates

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get_updates(
    loss, params
)

get_weights

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get_weights()

Returns the current weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.

For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

opt = tf.keras.optimizers.RMSprop() 
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) 
m.compile(opt, loss='mse') 
data = np.arange(100).reshape(5, 20) 
labels = np.zeros(5) 
print('Training'); results = m.fit(data, labels) 
Training ... 
len(opt.get_weights()) 
3 

Returns:

Weights values as a list of numpy arrays.

minimize

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minimize(
    loss, var_list, grad_loss=None, name=None
)

Minimize loss by updating var_list.

This method simply computes gradient using tf.GradientTape and calls apply_gradients(). If you want to process the gradient before applying then call tf.GradientTape and apply_gradients() explicitly instead of using this function.

Args:

  • loss: A callable taking no arguments which returns the value to minimize.
  • var_list: list or tuple of Variable objects to update to minimize loss, or a callable returning the list or tuple of Variable objects. Use callable when the variable list would otherwise be incomplete before minimize since the variables are created at the first time loss is called.
  • grad_loss: Optional. A Tensor holding the gradient computed for loss.
  • name: Optional name for the returned operation.

Returns:

An Operation that updates the variables in var_list. The iterations will be automatically increased by 1.

Raises:

  • ValueError: If some of the variables are not Variable objects.

set_weights

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set_weights(
    weights
)

Set the weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.

For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

opt = tf.keras.optimizers.RMSprop() 
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) 
m.compile(opt, loss='mse') 
data = np.arange(100).reshape(5, 20) 
labels = np.zeros(5) 
print('Training'); results = m.fit(data, labels) 
Training ... 
new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])] 
opt.set_weights(new_weights) 
opt.iterations 
<tf.Variable 'RMSprop/iter:0' shape=() dtype=int64, numpy=10> 

Arguments:

  • weights: weight values as a list of numpy arrays.

variables

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variables()

Returns variables of this Optimizer based on the order created.