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triangular.html
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triangular.html
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<header>
<h1 class="title">Module <code>ktrain.lroptimize.triangular</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..imports import *
class CyclicLR(keras.callbacks.Callback):
"""
This callback implements a cyclical learning rate policy (CLR).
The method cycles the learning rate between two boundaries with
some constant frequency, as detailed in this paper (https://arxiv.org/abs/1506.01186).
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
```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])
```
# 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'.
reduce_on_plateau (int): LR will be reduced after this many
epochs with no improvement on validation loss.
If zero or None, no reduction will take place
reduce_factor(int): LR is reduced by this factor (e.g., 2 = 1/2 = 0.5)
monitor (str): Value to monitor when reducing LR
max_momentum(float): maximum momentum when momentum is cycled
If both max_momentum and min_momentum is None,
default momentum for Adam is used.
(only used if optimizer is Adam)
min_momentum(float): minimum momentum when momentum is cycled
If both max_momentum and min_momentum is None,
default momentum for Adam is used.
(only used if optimizer is Adam)
verbose (bool): If True, will print information on LR reduction
References:
Original Paper: https://arxiv.org/abs/1803.09820
Blog Post: https://sgugger.github.io/the-1cycle-policy.html
Code Reference: https://github.com/bckenstler/CLR
"""
def __init__(
self,
base_lr=0.001,
max_lr=0.006,
step_size=2000.0,
mode="triangular",
gamma=1.0,
scale_fn=None,
scale_mode="cycle",
reduce_on_plateau=0,
monitor="val_loss",
reduce_factor=2,
max_momentum=0.95,
min_momentum=0.85,
verbose=1,
):
super(keras.callbacks.Callback, self).__init__()
self.base_lr = base_lr
self.max_lr = max_lr
self.step_size = step_size
self.mode = mode
self.gamma = gamma
if scale_fn == None:
if self.mode == "triangular":
self.scale_fn = lambda x: 1.0
self.scale_mode = "cycle"
elif self.mode == "triangular2":
self.scale_fn = lambda x: 1 / (2.0 ** (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 = 0.0
self.trn_iterations = 0.0
self.history = {}
# restoring weights due to CRF bug
self.best_weights = None
# LR reduction
self.verbose = verbose
self.patience = reduce_on_plateau
self.factor = 1.0 / reduce_factor
self.monitor = monitor
if "acc" not in self.monitor:
self.monitor_op = lambda a, b: np.less(a, b)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b)
self.best = -np.Inf
# annihalting LR
self.overhump = False
# cyclical momentum
self.max_momentum = max_momentum
self.min_momentum = min_momentum
if self.min_momentum is None and self.max_momentum:
self.min_momentum = self.max_momentum
elif self.min_momentum and self.max_momentum is None:
self.max_momentum = self.min_momentum
self.cycle_momentum = True if self.max_momentum is not None else False
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 != None:
self.base_lr = new_base_lr
if new_max_lr != None:
self.max_lr = new_max_lr
if new_step_size != None:
self.step_size = new_step_size
self.clr_iterations = 0.0
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)
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())
self.orig_base_lr = self.base_lr
def on_batch_end(self, batch, logs=None):
logs = logs or {}
self.trn_iterations += 1
self.clr_iterations += 1
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)
K.set_value(self.model.optimizer.lr, self.clr())
# annihilate learning rate
prev_overhump = self.overhump
iterations = (self.clr_iterations + 1) % (self.step_size * 2)
if iterations / self.step_size > 1:
self.overhump = True
else:
self.overhump = False
if not prev_overhump and self.overhump:
self.base_lr = self.max_lr / 1000
elif prev_overhump and not self.overhump:
self.base_lr = self.orig_base_lr
# set momentum
if self.cycle_momentum:
if self.overhump:
current_percentage = 1.0 - (
(iterations - self.step_size) / float(self.step_size)
)
new_momentum = self.max_momentum - current_percentage * (
self.max_momentum - self.min_momentum
)
else:
current_percentage = iterations / float(self.step_size)
new_momentum = self.max_momentum - current_percentage * (
self.max_momentum - self.min_momentum
)
K.set_value(self.model.optimizer.beta_1, new_momentum)
self.history.setdefault("momentum", []).append(
K.get_value(self.model.optimizer.beta_1)
)
def on_epoch_end(self, epoch, logs=None):
# print(K.eval(self.model.optimizer.lr))
# Stop training if training loss becomes zero or negative
# to address bug in keras_contrib code for CRF.
# We restore the weights from previous best epoch
# rather than this epoch.
crf = U.is_crf(self.model)
if crf:
current_loss = logs.get("loss")
current_val_loss = logs.get("val_loss", None)
if (current_loss is not None and current_loss <= 0.0) or (
current_val_loss is not None and current_val_loss <= 0.0
):
self.model.stop_training = True
if crf and self.best_weights is not None:
if self.verbose > 0:
print(
"Restoring model weights from the end of " "the best epoch"
)
self.model.set_weights(self.best_weights)
return
if self.patience:
current = logs.get(self.monitor)
if current is None:
raise Exception("cannot monitor %s" % (self.monitor))
if self.monitor_op(current, self.best):
self.best = current
self.wait = 0
if crf:
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience:
min_lr = 1e-7
current_lr = float(K.get_value(self.model.optimizer.lr))
if self.max_lr > min_lr:
self.base_lr = self.base_lr * self.factor
self.max_lr = self.max_lr * self.factor
new_lr = current_lr * self.factor
new_lr = max(new_lr, min_lr)
K.set_value(self.model.optimizer.lr, new_lr)
if self.verbose:
print(
"\nEpoch %05d: Reducing Max LR on Plateau: "
"new max lr will be %s (if not early_stopping)."
% (epoch + 1, self.max_lr)
)
self.wait = 0</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.lroptimize.triangular.CyclicLR"><code class="flex name class">
<span>class <span class="ident">CyclicLR</span></span>
<span>(</span><span>base_lr=0.001, max_lr=0.006, step_size=2000.0, mode='triangular', gamma=1.0, scale_fn=None, scale_mode='cycle', reduce_on_plateau=0, monitor='val_loss', reduce_factor=2, max_momentum=0.95, min_momentum=0.85, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>This callback implements a cyclical learning rate policy (CLR).
The method cycles the learning rate between two boundaries with
some constant frequency, as detailed in this paper (<a href="https://arxiv.org/abs/1506.01186">https://arxiv.org/abs/1506.01186</a>).
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.</p>
<h1 id="example">Example</h1>
<pre><code>```python
clr = CyclicLR(base_lr=0.001, max_lr=0.006,
step_size=2000., mode='triangular')
model.fit(X_train, Y_train, callbacks=[clr])
```
</code></pre>
<p>Class also supports custom scaling functions:
<code>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])</code></p>
<h1 id="arguments">Arguments</h1>
<pre><code>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'.
reduce_on_plateau (int): LR will be reduced after this many
epochs with no improvement on validation loss.
If zero or None, no reduction will take place
reduce_factor(int): LR is reduced by this factor (e.g., 2 = 1/2 = 0.5)
monitor (str): Value to monitor when reducing LR
max_momentum(float): maximum momentum when momentum is cycled
If both max_momentum and min_momentum is None,
default momentum for Adam is used.
(only used if optimizer is Adam)
min_momentum(float): minimum momentum when momentum is cycled
If both max_momentum and min_momentum is None,
default momentum for Adam is used.
(only used if optimizer is Adam)
verbose (bool): If True, will print information on LR reduction
</code></pre>
<h2 id="references">References</h2>
<p>Original Paper: <a href="https://arxiv.org/abs/1803.09820">https://arxiv.org/abs/1803.09820</a>
Blog Post: <a href="https://sgugger.github.io/the-1cycle-policy.html">https://sgugger.github.io/the-1cycle-policy.html</a>
Code Reference: <a href="https://github.com/bckenstler/CLR">https://github.com/bckenstler/CLR</a></p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class CyclicLR(keras.callbacks.Callback):
"""
This callback implements a cyclical learning rate policy (CLR).
The method cycles the learning rate between two boundaries with
some constant frequency, as detailed in this paper (https://arxiv.org/abs/1506.01186).
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
```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])
```
# 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'.
reduce_on_plateau (int): LR will be reduced after this many
epochs with no improvement on validation loss.
If zero or None, no reduction will take place
reduce_factor(int): LR is reduced by this factor (e.g., 2 = 1/2 = 0.5)
monitor (str): Value to monitor when reducing LR
max_momentum(float): maximum momentum when momentum is cycled
If both max_momentum and min_momentum is None,
default momentum for Adam is used.
(only used if optimizer is Adam)
min_momentum(float): minimum momentum when momentum is cycled
If both max_momentum and min_momentum is None,
default momentum for Adam is used.
(only used if optimizer is Adam)
verbose (bool): If True, will print information on LR reduction
References:
Original Paper: https://arxiv.org/abs/1803.09820
Blog Post: https://sgugger.github.io/the-1cycle-policy.html
Code Reference: https://github.com/bckenstler/CLR
"""
def __init__(
self,
base_lr=0.001,
max_lr=0.006,
step_size=2000.0,
mode="triangular",
gamma=1.0,
scale_fn=None,
scale_mode="cycle",
reduce_on_plateau=0,
monitor="val_loss",
reduce_factor=2,
max_momentum=0.95,
min_momentum=0.85,
verbose=1,
):
super(keras.callbacks.Callback, self).__init__()
self.base_lr = base_lr
self.max_lr = max_lr
self.step_size = step_size
self.mode = mode
self.gamma = gamma
if scale_fn == None:
if self.mode == "triangular":
self.scale_fn = lambda x: 1.0
self.scale_mode = "cycle"
elif self.mode == "triangular2":
self.scale_fn = lambda x: 1 / (2.0 ** (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 = 0.0
self.trn_iterations = 0.0
self.history = {}
# restoring weights due to CRF bug
self.best_weights = None
# LR reduction
self.verbose = verbose
self.patience = reduce_on_plateau
self.factor = 1.0 / reduce_factor
self.monitor = monitor
if "acc" not in self.monitor:
self.monitor_op = lambda a, b: np.less(a, b)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b)
self.best = -np.Inf
# annihalting LR
self.overhump = False
# cyclical momentum
self.max_momentum = max_momentum
self.min_momentum = min_momentum
if self.min_momentum is None and self.max_momentum:
self.min_momentum = self.max_momentum
elif self.min_momentum and self.max_momentum is None:
self.max_momentum = self.min_momentum
self.cycle_momentum = True if self.max_momentum is not None else False
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 != None:
self.base_lr = new_base_lr
if new_max_lr != None:
self.max_lr = new_max_lr
if new_step_size != None:
self.step_size = new_step_size
self.clr_iterations = 0.0
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)
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())
self.orig_base_lr = self.base_lr
def on_batch_end(self, batch, logs=None):
logs = logs or {}
self.trn_iterations += 1
self.clr_iterations += 1
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)
K.set_value(self.model.optimizer.lr, self.clr())
# annihilate learning rate
prev_overhump = self.overhump
iterations = (self.clr_iterations + 1) % (self.step_size * 2)
if iterations / self.step_size > 1:
self.overhump = True
else:
self.overhump = False
if not prev_overhump and self.overhump:
self.base_lr = self.max_lr / 1000
elif prev_overhump and not self.overhump:
self.base_lr = self.orig_base_lr
# set momentum
if self.cycle_momentum:
if self.overhump:
current_percentage = 1.0 - (
(iterations - self.step_size) / float(self.step_size)
)
new_momentum = self.max_momentum - current_percentage * (
self.max_momentum - self.min_momentum
)
else:
current_percentage = iterations / float(self.step_size)
new_momentum = self.max_momentum - current_percentage * (
self.max_momentum - self.min_momentum
)
K.set_value(self.model.optimizer.beta_1, new_momentum)
self.history.setdefault("momentum", []).append(
K.get_value(self.model.optimizer.beta_1)
)
def on_epoch_end(self, epoch, logs=None):
# print(K.eval(self.model.optimizer.lr))
# Stop training if training loss becomes zero or negative
# to address bug in keras_contrib code for CRF.
# We restore the weights from previous best epoch
# rather than this epoch.
crf = U.is_crf(self.model)
if crf:
current_loss = logs.get("loss")
current_val_loss = logs.get("val_loss", None)
if (current_loss is not None and current_loss <= 0.0) or (
current_val_loss is not None and current_val_loss <= 0.0
):
self.model.stop_training = True
if crf and self.best_weights is not None:
if self.verbose > 0:
print(
"Restoring model weights from the end of " "the best epoch"
)
self.model.set_weights(self.best_weights)
return
if self.patience:
current = logs.get(self.monitor)
if current is None:
raise Exception("cannot monitor %s" % (self.monitor))
if self.monitor_op(current, self.best):
self.best = current
self.wait = 0
if crf:
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience:
min_lr = 1e-7
current_lr = float(K.get_value(self.model.optimizer.lr))
if self.max_lr > min_lr:
self.base_lr = self.base_lr * self.factor
self.max_lr = self.max_lr * self.factor
new_lr = current_lr * self.factor
new_lr = max(new_lr, min_lr)
K.set_value(self.model.optimizer.lr, new_lr)
if self.verbose:
print(
"\nEpoch %05d: Reducing Max LR on Plateau: "
"new max lr will be %s (if not early_stopping)."
% (epoch + 1, self.max_lr)
)
self.wait = 0</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>tensorflow.python.keras.callbacks.Callback</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.lroptimize.triangular.CyclicLR.clr"><code class="name flex">
<span>def <span class="ident">clr</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">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)</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.triangular.CyclicLR.on_batch_end"><code class="name flex">
<span>def <span class="ident">on_batch_end</span></span>(<span>self, batch, logs=None)</span>
</code></dt>
<dd>
<div class="desc"><p>A backwards compatibility alias for <code>on_train_batch_end</code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_batch_end(self, batch, logs=None):
logs = logs or {}
self.trn_iterations += 1
self.clr_iterations += 1
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)
K.set_value(self.model.optimizer.lr, self.clr())
# annihilate learning rate
prev_overhump = self.overhump
iterations = (self.clr_iterations + 1) % (self.step_size * 2)
if iterations / self.step_size > 1:
self.overhump = True
else:
self.overhump = False
if not prev_overhump and self.overhump:
self.base_lr = self.max_lr / 1000
elif prev_overhump and not self.overhump:
self.base_lr = self.orig_base_lr
# set momentum
if self.cycle_momentum:
if self.overhump:
current_percentage = 1.0 - (
(iterations - self.step_size) / float(self.step_size)
)
new_momentum = self.max_momentum - current_percentage * (
self.max_momentum - self.min_momentum
)
else:
current_percentage = iterations / float(self.step_size)
new_momentum = self.max_momentum - current_percentage * (
self.max_momentum - self.min_momentum
)
K.set_value(self.model.optimizer.beta_1, new_momentum)
self.history.setdefault("momentum", []).append(
K.get_value(self.model.optimizer.beta_1)
)</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.triangular.CyclicLR.on_epoch_end"><code class="name flex">
<span>def <span class="ident">on_epoch_end</span></span>(<span>self, epoch, logs=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Called at the end of an epoch.</p>
<p>Subclasses should override for any actions to run. This function should only
be called during TRAIN mode.</p>
<h2 id="arguments">Arguments</h2>
<p>epoch: Integer, index of epoch.
logs: Dict, metric results for this training epoch, and for the
validation epoch if validation is performed. Validation result keys
are prefixed with <code>val_</code>.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_epoch_end(self, epoch, logs=None):
# print(K.eval(self.model.optimizer.lr))
# Stop training if training loss becomes zero or negative
# to address bug in keras_contrib code for CRF.
# We restore the weights from previous best epoch
# rather than this epoch.
crf = U.is_crf(self.model)
if crf:
current_loss = logs.get("loss")
current_val_loss = logs.get("val_loss", None)
if (current_loss is not None and current_loss <= 0.0) or (
current_val_loss is not None and current_val_loss <= 0.0
):
self.model.stop_training = True
if crf and self.best_weights is not None:
if self.verbose > 0:
print(
"Restoring model weights from the end of " "the best epoch"
)
self.model.set_weights(self.best_weights)
return
if self.patience:
current = logs.get(self.monitor)
if current is None:
raise Exception("cannot monitor %s" % (self.monitor))
if self.monitor_op(current, self.best):
self.best = current
self.wait = 0
if crf:
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience:
min_lr = 1e-7
current_lr = float(K.get_value(self.model.optimizer.lr))
if self.max_lr > min_lr:
self.base_lr = self.base_lr * self.factor
self.max_lr = self.max_lr * self.factor
new_lr = current_lr * self.factor
new_lr = max(new_lr, min_lr)
K.set_value(self.model.optimizer.lr, new_lr)
if self.verbose:
print(
"\nEpoch %05d: Reducing Max LR on Plateau: "
"new max lr will be %s (if not early_stopping)."
% (epoch + 1, self.max_lr)
)
self.wait = 0</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.triangular.CyclicLR.on_train_begin"><code class="name flex">
<span>def <span class="ident">on_train_begin</span></span>(<span>self, logs={})</span>
</code></dt>
<dd>
<div class="desc"><p>Called at the beginning of training.</p>
<p>Subclasses should override for any actions to run.</p>
<h2 id="arguments">Arguments</h2>
<p>logs: Dict. Currently no data is passed to this argument for this method
but that may change in the future.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">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())
self.orig_base_lr = self.base_lr</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.lroptimize" href="index.html">ktrain.lroptimize</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.lroptimize.triangular.CyclicLR" href="#ktrain.lroptimize.triangular.CyclicLR">CyclicLR</a></code></h4>
<ul class="">
<li><code><a title="ktrain.lroptimize.triangular.CyclicLR.clr" href="#ktrain.lroptimize.triangular.CyclicLR.clr">clr</a></code></li>
<li><code><a title="ktrain.lroptimize.triangular.CyclicLR.on_batch_end" href="#ktrain.lroptimize.triangular.CyclicLR.on_batch_end">on_batch_end</a></code></li>
<li><code><a title="ktrain.lroptimize.triangular.CyclicLR.on_epoch_end" href="#ktrain.lroptimize.triangular.CyclicLR.on_epoch_end">on_epoch_end</a></code></li>
<li><code><a title="ktrain.lroptimize.triangular.CyclicLR.on_train_begin" href="#ktrain.lroptimize.triangular.CyclicLR.on_train_begin">on_train_begin</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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