tfa.optimizers.NovoGrad

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The NovoGrad Optimizer was first proposed in [Stochastic Gradient

Methods with Layerwise Adaptvie Moments for training of Deep Networks](https://arxiv.org/pdf/1905.11286.pdf)

NovoGrad is a first-order SGD-based algorithm, which computes second moments per layer instead of per weight as in Adam. Compared to Adam, NovoGrad takes less memory, and has been found to be more numerically stable. More specifically we compute (for more information on the computation please refer to this link:

Second order moment = exponential moving average of Layer-wise square of grads: v_t <-- beta2 * v{t-1} + (1-beta_2) * (g_t)^2 First order moment in one of four modes:

1. moment of grads normalized by v_t:
    m_t <- beta_1 * m_{t-1} + [ g_t / (sqrt(v_t)+epsilon)]
2. moment similar to Adam: exponential moving average of grads
normalized by v_t (set grad_averaging = True to use this):
    m_t <- beta_1 * m_{t-1} +
           [(1 - beta_1) * (g_t / (sqrt(v_t) + epsilon))]
3. weight decay adds a w_d term after grads are rescaled by
1/sqrt(v_t) (set weight_decay > 0 to use this0:
    m_t <- beta_1 * m_{t-1} +
           [(g_t / (sqrt(v_t) + epsilon)) + (w_d * w_{t-1})]
4. weight decay + exponential moving average from Adam:
    m_t <- beta_1 * m_{t-1} +
           [(1 - beta_1) * ((g_t / (sqrt(v_t + epsilon)) +
           (w_d * w_{t-1}))]

Weight update: wt <- w{t-1} - lr_t * m_t

Example of usage:

opt = tfa.optimizers.NovoGrad(
    lr=1e-3,
    beta_1=0.9,
    beta_2=0.999,
    weight_decay=0.001,
    grad_averaging=False
)

learning_rate A Tensor or a floating point value. or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule The learning rate.
beta_1 A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
beta_2 A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.
epsilon A small constant for numerical stability.
weight_decay A floating point value. Weight decay for each param.
grad_averaging determines whether to use Adam style exponential moving averaging for the first order moments.
**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.

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

Add a new slot variable for var.

add_weight

apply_gradients

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 experimental_aggregate_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),
    experimental_aggregate_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.
experimental_aggregate_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

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

View source

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

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

get_slot_names

A list of names for this optimizer's slots.

get_updates

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

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

View source

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

Returns variables of this Optimizer based on the order created.