# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """One-line documentation for rmsprop module. rmsprop algorithm [tieleman2012rmsprop] A detailed description of rmsprop. - maintain a moving (discounted) average of the square of gradients - divide gradient by the root of this average mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2 mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(mean_square + epsilon) delta = - mom This implementation of RMSProp uses plain momentum, not Nesterov momentum. The centered version additionally maintains a moving (discounted) average of the gradients, and uses that average to estimate the variance: mean_grad = decay * mean_grad{t-1} + (1-decay) * gradient mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2 mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(mean_square - mean_grad**2 + epsilon) delta = - mom """ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops from tensorflow.python.util.tf_export import tf_export @tf_export(v1=["train.RMSPropOptimizer"]) class RMSPropOptimizer(optimizer.Optimizer): """Optimizer that implements the RMSProp algorithm (Tielemans et al. 2012). References: Coursera slide 29: Hinton, 2012 ([pdf](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)) @compatibility(TF2) tf.compat.v1.train.RMSPropOptimizer is compatible with eager mode and `tf.function`. When eager execution is enabled, `learning_rate`, `decay`, `momentum`, and `epsilon` can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions. To switch to native TF2 style, use [`tf.keras.optimizers.RMSprop`] (https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/RMSprop) instead. Please notice that due to the implementation differences, `tf.keras.optimizers.RMSprop` and `tf.compat.v1.train.RMSPropOptimizer` may have slight differences in floating point numerics even though the formula used for the variable updates still matches. #### Structural mapping to native TF2 Before: ```python optimizer = tf.compat.v1.train.RMSPropOptimizer( learning_rate=learning_rate, decay=decay, momentum=momentum, epsilon=epsilon) ``` After: ```python optimizer = tf.keras.optimizers.RMSprop( learning_rate=learning_rate, rho=decay, momentum=momentum, epsilon=epsilon) ``` #### How to map arguments | TF1 Arg Name | TF2 Arg Name | Note | | ------------------ | ------------- | ------------------------------- | | `learning_rate` | `learning_rate`| Be careful of setting | : : : learning_rate tensor value computed from the global step. : : : : In TF1 this was usually meant to imply a dynamic learning rate and : : : : would recompute in each step. In TF2 (eager + function) it will : : : : treat it as a scalar value that only gets computed once instead of : : : : a symbolic placeholder to be computed each time. : | `decay` | `rho` | - | | `momentum` | `momentum` | - | | `epsilon` | `epsilon` | Default value is 1e-10 in TF1, | : : : but 1e-07 in TF2. : | `use_locking` | - | Not applicable in TF2. | #### Before & after usage example Before: ```python x = tf.Variable([1,2,3], dtype=tf.float32) grad = tf.constant([0.1, 0.2, 0.3]) optimizer = tf.compat.v1.train.RMSPropOptimizer(learning_rate=0.001) optimizer.apply_gradients(zip([grad], [x])) ``` After: ```python x = tf.Variable([1,2,3], dtype=tf.float32) grad = tf.constant([0.1, 0.2, 0.3]) optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001) optimizer.apply_gradients(zip([grad], [x])) ``` @end_compatibility """ def __init__(self, learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, centered=False, name="RMSProp"): """Construct a new RMSProp optimizer. Note that in the dense implementation of this algorithm, variables and their corresponding accumulators (momentum, gradient moving average, square gradient moving average) will be updated even if the gradient is zero (i.e. accumulators will decay, momentum will be applied). The sparse implementation (used when the gradient is an `IndexedSlices` object, typically because of `tf.gather` or an embedding lookup in the forward pass) will not update variable slices or their accumulators unless those slices were used in the forward pass (nor is there an "eventual" correction to account for these omitted updates). This leads to more efficient updates for large embedding lookup tables (where most of the slices are not accessed in a particular graph execution), but differs from the published algorithm. Args: learning_rate: A Tensor or a floating point value. The learning rate. decay: Discounting factor for the history/coming gradient momentum: A scalar tensor. epsilon: Small value to avoid zero denominator. use_locking: If True use locks for update operation. centered: If True, gradients are normalized by the estimated variance of the gradient; if False, by the uncentered second moment. Setting this to True may help with training, but is slightly more expensive in terms of computation and memory. Defaults to False. name: Optional name prefix for the operations created when applying gradients. Defaults to "RMSProp". """ super(RMSPropOptimizer, self).__init__(use_locking, name) self._learning_rate = learning_rate self._decay = decay self._momentum = momentum self._epsilon = epsilon self._centered = centered # Tensors for learning rate and momentum. Created in _prepare. self._learning_rate_tensor = None self._decay_tensor = None self._momentum_tensor = None self._epsilon_tensor = None def _create_slots(self, var_list): for v in var_list: if v.get_shape().is_fully_defined(): init_rms = init_ops.ones_initializer(dtype=v.dtype.base_dtype) else: init_rms = array_ops.ones_like(v) self._get_or_make_slot_with_initializer(v, init_rms, v.get_shape(), v.dtype.base_dtype, "rms", self._name) if self._centered: self._zeros_slot(v, "mg", self._name) self._zeros_slot(v, "momentum", self._name) def _prepare(self): lr = self._call_if_callable(self._learning_rate) decay = self._call_if_callable(self._decay) momentum = self._call_if_callable(self._momentum) epsilon = self._call_if_callable(self._epsilon) self._learning_rate_tensor = ops.convert_to_tensor(lr, name="learning_rate") self._decay_tensor = ops.convert_to_tensor(decay, name="decay") self._momentum_tensor = ops.convert_to_tensor(momentum, name="momentum") self._epsilon_tensor = ops.convert_to_tensor(epsilon, name="epsilon") def _apply_dense(self, grad, var): rms = self.get_slot(var, "rms") mom = self.get_slot(var, "momentum") if self._centered: mg = self.get_slot(var, "mg") return training_ops.apply_centered_rms_prop( var, mg, rms, mom, math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), math_ops.cast(self._decay_tensor, var.dtype.base_dtype), math_ops.cast(self._momentum_tensor, var.dtype.base_dtype), math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype), grad, use_locking=self._use_locking).op else: return training_ops.apply_rms_prop( var, rms, mom, math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), math_ops.cast(self._decay_tensor, var.dtype.base_dtype), math_ops.cast(self._momentum_tensor, var.dtype.base_dtype), math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype), grad, use_locking=self._use_locking).op def _resource_apply_dense(self, grad, var): rms = self.get_slot(var, "rms") mom = self.get_slot(var, "momentum") if self._centered: mg = self.get_slot(var, "mg") return training_ops.resource_apply_centered_rms_prop( var.handle, mg.handle, rms.handle, mom.handle, math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype), math_ops.cast(self._decay_tensor, grad.dtype.base_dtype), math_ops.cast(self._momentum_tensor, grad.dtype.base_dtype), math_ops.cast(self._epsilon_tensor, grad.dtype.base_dtype), grad, use_locking=self._use_locking) else: return training_ops.resource_apply_rms_prop( var.handle, rms.handle, mom.handle, math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype), math_ops.cast(self._decay_tensor, grad.dtype.base_dtype), math_ops.cast(self._momentum_tensor, grad.dtype.base_dtype), math_ops.cast(self._epsilon_tensor, grad.dtype.base_dtype), grad, use_locking=self._use_locking) def _apply_sparse(self, grad, var): rms = self.get_slot(var, "rms") mom = self.get_slot(var, "momentum") if self._centered: mg = self.get_slot(var, "mg") return training_ops.sparse_apply_centered_rms_prop( var, mg, rms, mom, math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), math_ops.cast(self._decay_tensor, var.dtype.base_dtype), math_ops.cast(self._momentum_tensor, var.dtype.base_dtype), math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype), grad.values, grad.indices, use_locking=self._use_locking) else: return training_ops.sparse_apply_rms_prop( var, rms, mom, math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), math_ops.cast(self._decay_tensor, var.dtype.base_dtype), math_ops.cast(self._momentum_tensor, var.dtype.base_dtype), math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype), grad.values, grad.indices, use_locking=self._use_locking) def _resource_apply_sparse(self, grad, var, indices): rms = self.get_slot(var, "rms") mom = self.get_slot(var, "momentum") if self._centered: mg = self.get_slot(var, "mg") return training_ops.resource_sparse_apply_centered_rms_prop( var.handle, mg.handle, rms.handle, mom.handle, math_ops.cast(self._learning_rate_tensor, grad.dtype), math_ops.cast(self._decay_tensor, grad.dtype), math_ops.cast(self._momentum_tensor, grad.dtype), math_ops.cast(self._epsilon_tensor, grad.dtype), grad, indices, use_locking=self._use_locking) else: return training_ops.resource_sparse_apply_rms_prop( var.handle, rms.handle, mom.handle, math_ops.cast(self._learning_rate_tensor, grad.dtype), math_ops.cast(self._decay_tensor, grad.dtype), math_ops.cast(self._momentum_tensor, grad.dtype), math_ops.cast(self._epsilon_tensor, grad.dtype), grad, indices, use_locking=self._use_locking)