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callbacks.html
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callbacks.html
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<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.ner.anago.callbacks</code></h1>
</header>
<section id="section-intro">
<p>Custom callbacks.</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">"""
Custom callbacks.
"""
from ....imports import *
from .. import metrics
class F1score(keras.callbacks.Callback):
def __init__(self, seq, preprocessor=None):
super(F1score, self).__init__()
self.seq = seq
self.p = preprocessor
def get_lengths(self, y_true):
lengths = []
for y in np.argmax(y_true, -1):
try:
i = list(y).index(0)
except ValueError:
i = len(y)
lengths.append(i)
return lengths
def on_epoch_end(self, epoch, logs={}):
label_true = []
label_pred = []
for i in range(len(self.seq)):
x_true, y_true = self.seq[i]
lengths = self.get_lengths(y_true)
y_pred = self.model.predict_on_batch(x_true)
y_true = self.p.inverse_transform(y_true, lengths)
y_pred = self.p.inverse_transform(y_pred, lengths)
label_true.extend(y_true)
label_pred.extend(y_pred)
score = metrics.f1_score(label_true, label_pred)
print(" - f1: {:04.2f}".format(score * 100))
print(metrics.classification_report(label_true, label_pred))
logs["f1"] = score</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.ner.anago.callbacks.F1score"><code class="flex name class">
<span>class <span class="ident">F1score</span></span>
<span>(</span><span>seq, preprocessor=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Abstract base class used to build new callbacks.</p>
<p>Callbacks can be passed to keras methods such as <code>fit</code>, <code>evaluate</code>, and
<code>predict</code> in order to hook into the various stages of the model training and
inference lifecycle.</p>
<p>To create a custom callback, subclass <code>keras.callbacks.Callback</code> and
override the method associated with the stage of interest. See
<a href="https://www.tensorflow.org/guide/keras/custom_callback">https://www.tensorflow.org/guide/keras/custom_callback</a> for more information.</p>
<p>Example:</p>
<pre><code class="language-python-repl">>>> training_finished = False
>>> class MyCallback(tf.keras.callbacks.Callback):
... def on_train_end(self, logs=None):
... global training_finished
... training_finished = True
>>> model = tf.keras.Sequential([
... tf.keras.layers.Dense(1, input_shape=(1,))])
>>> model.compile(loss='mean_squared_error')
>>> model.fit(tf.constant([[1.0]]), tf.constant([[1.0]]),
... callbacks=[MyCallback()])
>>> assert training_finished == True
</code></pre>
<p>If you want to use <code>Callback</code> objects in a custom training loop:</p>
<ol>
<li>You should pack all your callbacks into a single <code>callbacks.CallbackList</code>
so they can all be called together.</li>
<li>You will need to manually call all the <code>on_*</code> methods at the appropriate
locations in your loop. Like this:</li>
</ol>
<p>Example:</p>
<pre><code class="language-python"> callbacks = tf.keras.callbacks.CallbackList([...])
callbacks.append(...)
callbacks.on_train_begin(...)
for epoch in range(EPOCHS):
callbacks.on_epoch_begin(epoch)
for i, data in dataset.enumerate():
callbacks.on_train_batch_begin(i)
batch_logs = model.train_step(data)
callbacks.on_train_batch_end(i, batch_logs)
epoch_logs = ...
callbacks.on_epoch_end(epoch, epoch_logs)
final_logs=...
callbacks.on_train_end(final_logs)
</code></pre>
<h2 id="attributes">Attributes</h2>
<dl>
<dt><strong><code>params</code></strong></dt>
<dd>Dict. Training parameters
(eg. verbosity, batch size, number of epochs…).</dd>
<dt><strong><code>model</code></strong></dt>
<dd>Instance of <code>keras.models.Model</code>.
Reference of the model being trained.</dd>
</dl>
<p>The <code>logs</code> dictionary that callback methods
take as argument will contain keys for quantities relevant to
the current batch or epoch (see method-specific docstrings).</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class F1score(keras.callbacks.Callback):
def __init__(self, seq, preprocessor=None):
super(F1score, self).__init__()
self.seq = seq
self.p = preprocessor
def get_lengths(self, y_true):
lengths = []
for y in np.argmax(y_true, -1):
try:
i = list(y).index(0)
except ValueError:
i = len(y)
lengths.append(i)
return lengths
def on_epoch_end(self, epoch, logs={}):
label_true = []
label_pred = []
for i in range(len(self.seq)):
x_true, y_true = self.seq[i]
lengths = self.get_lengths(y_true)
y_pred = self.model.predict_on_batch(x_true)
y_true = self.p.inverse_transform(y_true, lengths)
y_pred = self.p.inverse_transform(y_pred, lengths)
label_true.extend(y_true)
label_pred.extend(y_pred)
score = metrics.f1_score(label_true, label_pred)
print(" - f1: {:04.2f}".format(score * 100))
print(metrics.classification_report(label_true, label_pred))
logs["f1"] = score</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>keras.callbacks.Callback</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.ner.anago.callbacks.F1score.get_lengths"><code class="name flex">
<span>def <span class="ident">get_lengths</span></span>(<span>self, y_true)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_lengths(self, y_true):
lengths = []
for y in np.argmax(y_true, -1):
try:
i = list(y).index(0)
except ValueError:
i = len(y)
lengths.append(i)
return lengths</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.anago.callbacks.F1score.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>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="args">Args</h2>
<dl>
<dt><strong><code>epoch</code></strong></dt>
<dd>Integer, index of epoch.</dd>
<dt><strong><code>logs</code></strong></dt>
<dd>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>. For training epoch, the values of
the <code>Model</code>'s metrics are returned. Example:
<code>{'loss': 0.2, 'accuracy': 0.7}</code>.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def on_epoch_end(self, epoch, logs={}):
label_true = []
label_pred = []
for i in range(len(self.seq)):
x_true, y_true = self.seq[i]
lengths = self.get_lengths(y_true)
y_pred = self.model.predict_on_batch(x_true)
y_true = self.p.inverse_transform(y_true, lengths)
y_pred = self.p.inverse_transform(y_pred, lengths)
label_true.extend(y_true)
label_pred.extend(y_pred)
score = metrics.f1_score(label_true, label_pred)
print(" - f1: {:04.2f}".format(score * 100))
print(metrics.classification_report(label_true, label_pred))
logs["f1"] = score</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.text.ner.anago" href="index.html">ktrain.text.ner.anago</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.ner.anago.callbacks.F1score" href="#ktrain.text.ner.anago.callbacks.F1score">F1score</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.ner.anago.callbacks.F1score.get_lengths" href="#ktrain.text.ner.anago.callbacks.F1score.get_lengths">get_lengths</a></code></li>
<li><code><a title="ktrain.text.ner.anago.callbacks.F1score.on_epoch_end" href="#ktrain.text.ner.anago.callbacks.F1score.on_epoch_end">on_epoch_end</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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