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sgdr.html
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sgdr.html
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.lroptimize.sgdr</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ..imports import *
class SGDRScheduler(keras.callbacks.Callback):
"""Cosine annealing learning rate scheduler with periodic restarts.
# Usage
```python
schedule = SGDRScheduler(min_lr=1e-7,
max_lr=1e-1,
steps_per_epoch=np.ceil(epoch_size/batch_size),
lr_decay=0.9,
cycle_length=1,
mult_factor=2)
model.fit(X_train, Y_train, epochs=100, callbacks=[schedule])
```
# Arguments
min_lr: The lower bound of the learning rate range for the experiment.
max_lr: The upper bound of the learning rate range for the experiment.
steps_per_epoch: Number of mini-batches in the dataset. Calculated as `np.ceil(epoch_size/batch_size)`.
lr_decay: Reduce the max_lr after the completion of each cycle.
Ex. To reduce the max_lr by 20% after each cycle, set this value to 0.8.
cycle_length: Initial number of epochs in a cycle.
mult_factor: Scale epochs_to_restart after each full cycle completion.
# References
Original paper: http://arxiv.org/abs/1608.03983
Blog Post: http://www.jeremyjordan.me/nn-learning-rate/
"""
def __init__(
self,
min_lr,
max_lr,
steps_per_epoch,
lr_decay=0.9,
cycle_length=10,
mult_factor=2,
):
super(keras.callbacks.Callback, self).__init__()
self.min_lr = min_lr
self.max_lr = max_lr
self.lr_decay = lr_decay
self.batch_since_restart = 0
self.next_restart = cycle_length
self.steps_per_epoch = steps_per_epoch
self.cycle_length = cycle_length
self.mult_factor = mult_factor
self.history = {}
def clr(self):
"""Calculate the learning rate."""
fraction_to_restart = self.batch_since_restart / (
self.steps_per_epoch * self.cycle_length
)
lr = self.min_lr + 0.5 * (self.max_lr - self.min_lr) * (
1 + np.cos(fraction_to_restart * np.pi)
)
return lr
def on_train_begin(self, logs={}):
"""Initialize the learning rate to the minimum value at the start of training."""
logs = logs or {}
K.set_value(self.model.optimizer.lr, self.max_lr)
def on_batch_end(self, batch, logs={}):
"""Record previous batch statistics and update the learning rate."""
logs = logs or {}
self.history.setdefault("lr", []).append(K.get_value(self.model.optimizer.lr))
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
self.batch_since_restart += 1
K.set_value(self.model.optimizer.lr, self.clr())
# print(K.eval(self.model.optimizer.lr))
def on_epoch_end(self, epoch, logs={}):
"""Check for end of current cycle, apply restarts when necessary."""
# print(K.eval(self.model.optimizer.lr))
if epoch + 1 == self.next_restart:
self.batch_since_restart = 0
self.cycle_length = np.ceil(self.cycle_length * self.mult_factor)
self.next_restart += self.cycle_length
self.max_lr *= self.lr_decay
# no longer needed as kauto completes cycles/epochs
# self.best_weights = self.model.get_weights()
def on_train_end(self, logs={}):
"""Set weights to the values from the end of the most recent cycle for best performance."""
# no longer needed as kauto completes cycles/epochs
# self.model.set_weights(self.best_weights)
pass
def on_epoch_begin(self, epoch, logs={}):
"""Initialize the learning rate to the minimum value at the start of training."""
logs = logs or {}</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.sgdr.SGDRScheduler"><code class="flex name class">
<span>class <span class="ident">SGDRScheduler</span></span>
<span>(</span><span>min_lr, max_lr, steps_per_epoch, lr_decay=0.9, cycle_length=10, mult_factor=2)</span>
</code></dt>
<dd>
<div class="desc"><p>Cosine annealing learning rate scheduler with periodic restarts.</p>
<h1 id="usage">Usage</h1>
<pre><code>```python
schedule = SGDRScheduler(min_lr=1e-7,
max_lr=1e-1,
steps_per_epoch=np.ceil(epoch_size/batch_size),
lr_decay=0.9,
cycle_length=1,
mult_factor=2)
model.fit(X_train, Y_train, epochs=100, callbacks=[schedule])
```
</code></pre>
<h1 id="arguments">Arguments</h1>
<pre><code>min_lr: The lower bound of the learning rate range for the experiment.
max_lr: The upper bound of the learning rate range for the experiment.
steps_per_epoch: Number of mini-batches in the dataset. Calculated as `np.ceil(epoch_size/batch_size)`.
lr_decay: Reduce the max_lr after the completion of each cycle.
Ex. To reduce the max_lr by 20% after each cycle, set this value to 0.8.
cycle_length: Initial number of epochs in a cycle.
mult_factor: Scale epochs_to_restart after each full cycle completion.
</code></pre>
<h1 id="references">References</h1>
<pre><code>Original paper: <http://arxiv.org/abs/1608.03983>
Blog Post: <http://www.jeremyjordan.me/nn-learning-rate/>
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SGDRScheduler(keras.callbacks.Callback):
"""Cosine annealing learning rate scheduler with periodic restarts.
# Usage
```python
schedule = SGDRScheduler(min_lr=1e-7,
max_lr=1e-1,
steps_per_epoch=np.ceil(epoch_size/batch_size),
lr_decay=0.9,
cycle_length=1,
mult_factor=2)
model.fit(X_train, Y_train, epochs=100, callbacks=[schedule])
```
# Arguments
min_lr: The lower bound of the learning rate range for the experiment.
max_lr: The upper bound of the learning rate range for the experiment.
steps_per_epoch: Number of mini-batches in the dataset. Calculated as `np.ceil(epoch_size/batch_size)`.
lr_decay: Reduce the max_lr after the completion of each cycle.
Ex. To reduce the max_lr by 20% after each cycle, set this value to 0.8.
cycle_length: Initial number of epochs in a cycle.
mult_factor: Scale epochs_to_restart after each full cycle completion.
# References
Original paper: http://arxiv.org/abs/1608.03983
Blog Post: http://www.jeremyjordan.me/nn-learning-rate/
"""
def __init__(
self,
min_lr,
max_lr,
steps_per_epoch,
lr_decay=0.9,
cycle_length=10,
mult_factor=2,
):
super(keras.callbacks.Callback, self).__init__()
self.min_lr = min_lr
self.max_lr = max_lr
self.lr_decay = lr_decay
self.batch_since_restart = 0
self.next_restart = cycle_length
self.steps_per_epoch = steps_per_epoch
self.cycle_length = cycle_length
self.mult_factor = mult_factor
self.history = {}
def clr(self):
"""Calculate the learning rate."""
fraction_to_restart = self.batch_since_restart / (
self.steps_per_epoch * self.cycle_length
)
lr = self.min_lr + 0.5 * (self.max_lr - self.min_lr) * (
1 + np.cos(fraction_to_restart * np.pi)
)
return lr
def on_train_begin(self, logs={}):
"""Initialize the learning rate to the minimum value at the start of training."""
logs = logs or {}
K.set_value(self.model.optimizer.lr, self.max_lr)
def on_batch_end(self, batch, logs={}):
"""Record previous batch statistics and update the learning rate."""
logs = logs or {}
self.history.setdefault("lr", []).append(K.get_value(self.model.optimizer.lr))
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
self.batch_since_restart += 1
K.set_value(self.model.optimizer.lr, self.clr())
# print(K.eval(self.model.optimizer.lr))
def on_epoch_end(self, epoch, logs={}):
"""Check for end of current cycle, apply restarts when necessary."""
# print(K.eval(self.model.optimizer.lr))
if epoch + 1 == self.next_restart:
self.batch_since_restart = 0
self.cycle_length = np.ceil(self.cycle_length * self.mult_factor)
self.next_restart += self.cycle_length
self.max_lr *= self.lr_decay
# no longer needed as kauto completes cycles/epochs
# self.best_weights = self.model.get_weights()
def on_train_end(self, logs={}):
"""Set weights to the values from the end of the most recent cycle for best performance."""
# no longer needed as kauto completes cycles/epochs
# self.model.set_weights(self.best_weights)
pass
def on_epoch_begin(self, epoch, logs={}):
"""Initialize the learning rate to the minimum value at the start of training."""
logs = logs or {}</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.sgdr.SGDRScheduler.clr"><code class="name flex">
<span>def <span class="ident">clr</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Calculate the learning rate.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def clr(self):
"""Calculate the learning rate."""
fraction_to_restart = self.batch_since_restart / (
self.steps_per_epoch * self.cycle_length
)
lr = self.min_lr + 0.5 * (self.max_lr - self.min_lr) * (
1 + np.cos(fraction_to_restart * np.pi)
)
return lr</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.sgdr.SGDRScheduler.on_batch_end"><code class="name flex">
<span>def <span class="ident">on_batch_end</span></span>(<span>self, batch, logs={})</span>
</code></dt>
<dd>
<div class="desc"><p>Record previous batch statistics and update the learning rate.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_batch_end(self, batch, logs={}):
"""Record previous batch statistics and update the learning rate."""
logs = logs or {}
self.history.setdefault("lr", []).append(K.get_value(self.model.optimizer.lr))
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
self.batch_since_restart += 1
K.set_value(self.model.optimizer.lr, self.clr())</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.sgdr.SGDRScheduler.on_epoch_begin"><code class="name flex">
<span>def <span class="ident">on_epoch_begin</span></span>(<span>self, epoch, logs={})</span>
</code></dt>
<dd>
<div class="desc"><p>Initialize the learning rate to the minimum value at the start of training.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_epoch_begin(self, epoch, logs={}):
"""Initialize the learning rate to the minimum value at the start of training."""
logs = logs or {}</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.sgdr.SGDRScheduler.on_epoch_end"><code class="name flex">
<span>def <span class="ident">on_epoch_end</span></span>(<span>self, epoch, logs={})</span>
</code></dt>
<dd>
<div class="desc"><p>Check for end of current cycle, apply restarts when necessary.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_epoch_end(self, epoch, logs={}):
"""Check for end of current cycle, apply restarts when necessary."""
# print(K.eval(self.model.optimizer.lr))
if epoch + 1 == self.next_restart:
self.batch_since_restart = 0
self.cycle_length = np.ceil(self.cycle_length * self.mult_factor)
self.next_restart += self.cycle_length
self.max_lr *= self.lr_decay</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.sgdr.SGDRScheduler.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>Initialize the learning rate to the minimum value at the start of training.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_train_begin(self, logs={}):
"""Initialize the learning rate to the minimum value at the start of training."""
logs = logs or {}
K.set_value(self.model.optimizer.lr, self.max_lr)</code></pre>
</details>
</dd>
<dt id="ktrain.lroptimize.sgdr.SGDRScheduler.on_train_end"><code class="name flex">
<span>def <span class="ident">on_train_end</span></span>(<span>self, logs={})</span>
</code></dt>
<dd>
<div class="desc"><p>Set weights to the values from the end of the most recent cycle for best performance.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_train_end(self, logs={}):
"""Set weights to the values from the end of the most recent cycle for best performance."""
# no longer needed as kauto completes cycles/epochs
# self.model.set_weights(self.best_weights)
pass</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.sgdr.SGDRScheduler" href="#ktrain.lroptimize.sgdr.SGDRScheduler">SGDRScheduler</a></code></h4>
<ul class="two-column">
<li><code><a title="ktrain.lroptimize.sgdr.SGDRScheduler.clr" href="#ktrain.lroptimize.sgdr.SGDRScheduler.clr">clr</a></code></li>
<li><code><a title="ktrain.lroptimize.sgdr.SGDRScheduler.on_batch_end" href="#ktrain.lroptimize.sgdr.SGDRScheduler.on_batch_end">on_batch_end</a></code></li>
<li><code><a title="ktrain.lroptimize.sgdr.SGDRScheduler.on_epoch_begin" href="#ktrain.lroptimize.sgdr.SGDRScheduler.on_epoch_begin">on_epoch_begin</a></code></li>
<li><code><a title="ktrain.lroptimize.sgdr.SGDRScheduler.on_epoch_end" href="#ktrain.lroptimize.sgdr.SGDRScheduler.on_epoch_end">on_epoch_end</a></code></li>
<li><code><a title="ktrain.lroptimize.sgdr.SGDRScheduler.on_train_begin" href="#ktrain.lroptimize.sgdr.SGDRScheduler.on_train_begin">on_train_begin</a></code></li>
<li><code><a title="ktrain.lroptimize.sgdr.SGDRScheduler.on_train_end" href="#ktrain.lroptimize.sgdr.SGDRScheduler.on_train_end">on_train_end</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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