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November 14, 2019 09:26
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Quick and dirty tensorflow implementation of Optimistic Mirror Descent for the Adam Optimiser as in https://arxiv.org/abs/1711.00141
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| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| from tensorflow.python.ops import control_flow_ops | |
| from tensorflow.python.ops import math_ops | |
| from tensorflow.python.ops import state_ops | |
| from tensorflow.python.framework import ops | |
| from tensorflow.python.training import optimizer | |
| import tensorflow as tf | |
| # Adapted from https://raw.githubusercontent.com/openai/iaf/master/tf_utils/adamax.py | |
| class AdamirrorOptimizer(optimizer.Optimizer): | |
| def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, | |
| use_locking=False, name="Adamirror"): | |
| super(AdamirrorOptimizer, self).__init__(use_locking, name) | |
| self._lr = learning_rate | |
| self._beta1 = beta1 | |
| self._beta2 = beta2 | |
| # Tensor versions of the constructor arguments, created in _prepare(). | |
| self._lr_t = None | |
| self._beta1_t = None | |
| self._beta2_t = None | |
| self._t = None | |
| def _prepare(self): | |
| self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate") | |
| self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1") | |
| self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2") | |
| self._t = ops.convert_to_tensor(0, name="t") | |
| def _create_slots(self, var_list): | |
| # Create slots for the first and second moments. | |
| for v in var_list: | |
| self._zeros_slot(v, "m", self._name) | |
| self._zeros_slot(v, "v", self._name) | |
| self._zeros_slot(v, "g", self._name) | |
| def _apply_dense(self, grad, var): | |
| lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) | |
| beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) | |
| beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) | |
| if var.dtype.base_dtype == tf.float16: | |
| eps = 1e-7 # Can't use 1e-8 due to underflow -- not sure if it makes a big difference. | |
| else: | |
| eps = 1e-8 | |
| t = self._t | |
| t = t+1 | |
| v = self.get_slot(var, "v") | |
| v_t = v.assign(beta2_t * v + (1. - beta2_t) * tf.square(grad)) | |
| m = self.get_slot(var, "m") | |
| m_t = m.assign( beta1_t * m + (1. - beta1_t) * grad ) | |
| v_t_hat = tf.div(v_t, 1. - beta2_t.pow(t)) | |
| m_t_hat = tf.div(m_t, 1. - beta1_t.pow(t)) | |
| g_t = tf.div( m_t_hat, tf.sqrt(v_t_hat)+eps ) | |
| g_t_1 = self.get_slot(var, "g") | |
| g_t = g_t_1.assign( g_t ) | |
| t_t = self._t.assign(t) | |
| var_update = state_ops.assign_sub(var, 2. * lr_t * g_t - lr_t * g_t_1) #Adam would be lr_t * g_t | |
| return control_flow_ops.group(*[var_update, m_t, v_t, g_t, t_t]) | |
| def _apply_sparse(self, grad, var): | |
| raise NotImplementedError("Sparse gradient updates are not supported.") |
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Hi, I think you may have made a mistake at line 60, it shouldn't be
but it should rather be
with the conventions you have used in the previous script. Do let me know if I am mistaken,
Cheers.