from tensorflow.keras.callbacks import Callback
from tensorflow.keras import backend as K
import numpy as np
[docs]def clr_check_args(args):
req_keys = ['clr_mode', 'clr_base_lr', 'clr_max_lr', 'clr_gamma']
keys_present = True
for key in req_keys:
if key not in args.keys():
keys_present = False
return keys_present
[docs]def clr_set_args(args):
req_keys = ['clr_mode', 'clr_base_lr', 'clr_max_lr', 'clr_gamma']
exclusive_keys = ['warmup_lr', 'reduce_lr']
keys_present = True
for key in req_keys:
if key not in args.keys():
keys_present = False
if keys_present and args['clr_mode'] is not None:
clr_keras_kwargs = {'mode': args['clr_mode'], 'base_lr': args['clr_base_lr'],
'max_lr': args['clr_max_lr'], 'gamma': args['clr_gamma']}
for ex_key in exclusive_keys:
if ex_key in args.keys():
if args[ex_key] is True:
print("Key ", ex_key, " conflicts, setting to False")
args[ex_key] = False
else:
print("Incomplete CLR specification: will run without")
clr_keras_kwargs = {'mode': None, 'base_lr': 0.1,
'max_lr': 0.1, 'gamma': 0.1}
return clr_keras_kwargs
[docs]def clr_callback(mode=None, base_lr=1e-4, max_lr=1e-3, gamma=0.999994):
""" Creates keras callback for cyclical learning rate. """
if mode == 'trng1':
clr = CyclicLR(base_lr=base_lr, max_lr=max_lr, mode='triangular')
elif mode == 'trng2':
clr = CyclicLR(base_lr=base_lr, max_lr=max_lr, mode='triangular2')
elif mode == 'exp':
clr = CyclicLR(base_lr=base_lr, max_lr=max_lr, mode='exp_range', gamma=gamma) # 0.99994; 0.99999994; 0.999994
return clr
[docs]class CyclicLR(Callback):
"""This callback implements a cyclical learning rate policy (CLR).
The method cycles the learning rate between two boundaries with
some constant frequency.
# Arguments
base_lr: initial learning rate which is the
lower boundary in the cycle.
max_lr: upper boundary in the cycle. Functionally,
it defines the cycle amplitude (max_lr - base_lr).
The lr at any cycle is the sum of base_lr
and some scaling of the amplitude; therefore
max_lr may not actually be reached depending on
scaling function.
step_size: number of training iterations per
half cycle. Authors suggest setting step_size
2-8 x training iterations in epoch.
mode: one of {triangular, triangular2, exp_range}.
Default 'triangular'.
Values correspond to policies detailed above.
If scale_fn is not None, this argument is ignored.
gamma: constant in 'exp_range' scaling function:
gamma**(cycle iterations)
scale_fn: Custom scaling policy defined by a single
argument lambda function, where
0 <= scale_fn(x) <= 1 for all x >= 0.
mode paramater is ignored
scale_mode: {'cycle', 'iterations'}.
Defines whether scale_fn is evaluated on
cycle number or cycle iterations (training
iterations since start of cycle). Default is 'cycle'.
The amplitude of the cycle can be scaled on a per-iteration or
per-cycle basis.
This class has three built-in policies, as put forth in the paper.
"triangular":
A basic triangular cycle w/ no amplitude scaling.
"triangular2":
A basic triangular cycle that scales initial amplitude by half each cycle.
"exp_range":
A cycle that scales initial amplitude by gamma**(cycle iterations) at each
cycle iteration.
For more detail, please see paper.
# Example for CIFAR-10 w/ batch size 100:
```python
clr = CyclicLR(base_lr=0.001, max_lr=0.006,
step_size=2000., mode='triangular')
model.fit(X_train, Y_train, callbacks=[clr])
```
Class also supports custom scaling functions:
```python
clr_fn = lambda x: 0.5*(1+np.sin(x*np.pi/2.))
clr = CyclicLR(base_lr=0.001, max_lr=0.006,
step_size=2000., scale_fn=clr_fn,
scale_mode='cycle')
model.fit(X_train, Y_train, callbacks=[clr])
```
# References
- [Cyclical Learning Rates for Training Neural Networks](
https://arxiv.org/abs/1506.01186)
"""
def __init__(
self,
base_lr=0.001,
max_lr=0.006,
step_size=2000.,
mode='triangular',
gamma=1.,
scale_fn=None,
scale_mode='cycle'):
super(CyclicLR, self).__init__()
if mode not in ['triangular', 'triangular2',
'exp_range']:
raise KeyError("mode must be one of 'triangular', "
"'triangular2', or 'exp_range'")
self.base_lr = base_lr
self.max_lr = max_lr
self.step_size = step_size
self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = lambda x: 1.
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = lambda x: 1 / (2.**(x - 1))
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = lambda x: gamma ** x
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.clr_iterations = 0.
self.trn_iterations = 0.
self.history = {}
self._reset()
def _reset(self, new_base_lr=None, new_max_lr=None,
new_step_size=None):
"""Resets cycle iterations.
Optional boundary/step size adjustment.
"""
if new_base_lr is not None:
self.base_lr = new_base_lr
if new_max_lr is not None:
self.max_lr = new_max_lr
if new_step_size is not None:
self.step_size = new_step_size
self.clr_iterations = 0.
[docs] def clr(self):
cycle = np.floor(1 + self.clr_iterations / (2 * self.step_size))
x = np.abs(self.clr_iterations / self.step_size - 2 * cycle + 1)
if self.scale_mode == 'cycle':
return self.base_lr + (self.max_lr - self.base_lr) * \
np.maximum(0, (1 - x)) * self.scale_fn(cycle)
else:
return self.base_lr + (self.max_lr - self.base_lr) * \
np.maximum(0, (1 - x)) * self.scale_fn(self.clr_iterations)
[docs] def on_train_begin(self, logs={}):
logs = logs or {}
if self.clr_iterations == 0:
K.set_value(self.model.optimizer.lr, self.base_lr)
else:
K.set_value(self.model.optimizer.lr, self.clr())
[docs] def on_batch_end(self, epoch, logs=None):
logs = logs or {}
self.trn_iterations += 1
self.clr_iterations += 1
K.set_value(self.model.optimizer.lr, self.clr())
self.history.setdefault(
'lr', []).append(
K.get_value(
self.model.optimizer.lr))
self.history.setdefault('iterations', []).append(self.trn_iterations)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
[docs] def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
logs['lr'] = K.get_value(self.model.optimizer.lr)