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<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.vision.learner</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 ..core import GenLearner
from ..imports import *
from .data import show_image
class ImageClassLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for image classification.
Main parameters are:
model (Model): A compiled instance of keras.engine.training.Model
train_data (Iterator): a Iterator instance for training set
val_data (Iterator): A Iterator instance for validation set
```
"""
def __init__(
self,
model,
train_data=None,
val_data=None,
batch_size=U.DEFAULT_BS,
eval_batch_size=U.DEFAULT_BS,
workers=1,
use_multiprocessing=False,
):
super().__init__(
model,
train_data=train_data,
val_data=val_data,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
)
return
def view_top_losses(self, n=4, preproc=None, val_data=None):
"""
```
Views observations with top losses in validation set.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
```
"""
val = self._check_val(val_data)
# check validation data and arguments
if val_data is not None:
val = val_data
else:
val = self.val_data
if val is None:
raise Exception(
"val_data must be supplied to get_learner or view_top_losses"
)
# get top losses and associated data
tups = self.top_losses(n=n, val_data=val, preproc=preproc)
# get multilabel status and class names
classes = preproc.get_classes() if preproc is not None else None
# iterate through losses
for tup in tups:
# get data
idx = tup[0]
loss = tup[1]
truth = tup[2]
pred = tup[3]
# Image Classification
if type(val).__name__ in ["DirectoryIterator", "DataFrameIterator"]:
fpath = val.filepaths[tup[0]]
fp = os.path.join(
os.path.basename(os.path.dirname(fpath)), os.path.basename(fpath)
)
plt.figure()
plt.title(
"%s | loss:%s | true:%s | pred:%s)"
% (fp, round(loss, 2), truth, pred)
)
show_image(fpath)
elif type(val).__name__ in ["NumpyArrayIterator"]:
obs = val.x[idx]
# if preproc is not None: obs = preproc.undo(obs)
plt.figure()
plt.title(
"id:%s | loss:%s | true:%s | pred:%s)"
% (idx, round(loss, 2), truth, pred)
)
plt.imshow(np.squeeze(obs))
# everything else including text classification
else:
raise Exception(
"ImageClassLearner.view_top_losses only supports "
+ "DirectoryIterators, DataFrameIterators, and NumpyArrayIterators"
)
return</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.vision.learner.ImageClassLearner"><code class="flex name class">
<span>class <span class="ident">ImageClassLearner</span></span>
<span>(</span><span>model, train_data=None, val_data=None, batch_size=32, eval_batch_size=32, workers=1, use_multiprocessing=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Main class used to tune and train Keras models for image classification.
Main parameters are:
model (Model): A compiled instance of keras.engine.training.Model
train_data (Iterator): a Iterator instance for training set
val_data (Iterator): A Iterator instance for validation set
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ImageClassLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for image classification.
Main parameters are:
model (Model): A compiled instance of keras.engine.training.Model
train_data (Iterator): a Iterator instance for training set
val_data (Iterator): A Iterator instance for validation set
```
"""
def __init__(
self,
model,
train_data=None,
val_data=None,
batch_size=U.DEFAULT_BS,
eval_batch_size=U.DEFAULT_BS,
workers=1,
use_multiprocessing=False,
):
super().__init__(
model,
train_data=train_data,
val_data=val_data,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
)
return
def view_top_losses(self, n=4, preproc=None, val_data=None):
"""
```
Views observations with top losses in validation set.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
```
"""
val = self._check_val(val_data)
# check validation data and arguments
if val_data is not None:
val = val_data
else:
val = self.val_data
if val is None:
raise Exception(
"val_data must be supplied to get_learner or view_top_losses"
)
# get top losses and associated data
tups = self.top_losses(n=n, val_data=val, preproc=preproc)
# get multilabel status and class names
classes = preproc.get_classes() if preproc is not None else None
# iterate through losses
for tup in tups:
# get data
idx = tup[0]
loss = tup[1]
truth = tup[2]
pred = tup[3]
# Image Classification
if type(val).__name__ in ["DirectoryIterator", "DataFrameIterator"]:
fpath = val.filepaths[tup[0]]
fp = os.path.join(
os.path.basename(os.path.dirname(fpath)), os.path.basename(fpath)
)
plt.figure()
plt.title(
"%s | loss:%s | true:%s | pred:%s)"
% (fp, round(loss, 2), truth, pred)
)
show_image(fpath)
elif type(val).__name__ in ["NumpyArrayIterator"]:
obs = val.x[idx]
# if preproc is not None: obs = preproc.undo(obs)
plt.figure()
plt.title(
"id:%s | loss:%s | true:%s | pred:%s)"
% (idx, round(loss, 2), truth, pred)
)
plt.imshow(np.squeeze(obs))
# everything else including text classification
else:
raise Exception(
"ImageClassLearner.view_top_losses only supports "
+ "DirectoryIterators, DataFrameIterators, and NumpyArrayIterators"
)
return</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></li>
<li><a title="ktrain.core.Learner" href="../core.html#ktrain.core.Learner">Learner</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.vision.learner.ImageClassLearner.view_top_losses"><code class="name flex">
<span>def <span class="ident">view_top_losses</span></span>(<span>self, n=4, preproc=None, val_data=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Views observations with top losses in validation set.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def view_top_losses(self, n=4, preproc=None, val_data=None):
"""
```
Views observations with top losses in validation set.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
```
"""
val = self._check_val(val_data)
# check validation data and arguments
if val_data is not None:
val = val_data
else:
val = self.val_data
if val is None:
raise Exception(
"val_data must be supplied to get_learner or view_top_losses"
)
# get top losses and associated data
tups = self.top_losses(n=n, val_data=val, preproc=preproc)
# get multilabel status and class names
classes = preproc.get_classes() if preproc is not None else None
# iterate through losses
for tup in tups:
# get data
idx = tup[0]
loss = tup[1]
truth = tup[2]
pred = tup[3]
# Image Classification
if type(val).__name__ in ["DirectoryIterator", "DataFrameIterator"]:
fpath = val.filepaths[tup[0]]
fp = os.path.join(
os.path.basename(os.path.dirname(fpath)), os.path.basename(fpath)
)
plt.figure()
plt.title(
"%s | loss:%s | true:%s | pred:%s)"
% (fp, round(loss, 2), truth, pred)
)
show_image(fpath)
elif type(val).__name__ in ["NumpyArrayIterator"]:
obs = val.x[idx]
# if preproc is not None: obs = preproc.undo(obs)
plt.figure()
plt.title(
"id:%s | loss:%s | true:%s | pred:%s)"
% (idx, round(loss, 2), truth, pred)
)
plt.imshow(np.squeeze(obs))
# everything else including text classification
else:
raise Exception(
"ImageClassLearner.view_top_losses only supports "
+ "DirectoryIterators, DataFrameIterators, and NumpyArrayIterators"
)
return</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.core.GenLearner.autofit" href="../core.html#ktrain.core.Learner.autofit">autofit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.evaluate" href="../core.html#ktrain.core.Learner.evaluate">evaluate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit" href="../core.html#ktrain.core.GenLearner.fit">fit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit_onecycle" href="../core.html#ktrain.core.Learner.fit_onecycle">fit_onecycle</a></code></li>
<li><code><a title="ktrain.core.GenLearner.freeze" href="../core.html#ktrain.core.Learner.freeze">freeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.get_weight_decay" href="../core.html#ktrain.core.Learner.get_weight_decay">get_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.layer_output" href="../core.html#ktrain.core.GenLearner.layer_output">layer_output</a></code></li>
<li><code><a title="ktrain.core.GenLearner.load_model" href="../core.html#ktrain.core.Learner.load_model">load_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_estimate" href="../core.html#ktrain.core.Learner.lr_estimate">lr_estimate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_find" href="../core.html#ktrain.core.Learner.lr_find">lr_find</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_plot" href="../core.html#ktrain.core.Learner.lr_plot">lr_plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.plot" href="../core.html#ktrain.core.Learner.plot">plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.predict" href="../core.html#ktrain.core.Learner.predict">predict</a></code></li>
<li><code><a title="ktrain.core.GenLearner.print_layers" href="../core.html#ktrain.core.Learner.print_layers">print_layers</a></code></li>
<li><code><a title="ktrain.core.GenLearner.reset_weights" href="../core.html#ktrain.core.Learner.reset_weights">reset_weights</a></code></li>
<li><code><a title="ktrain.core.GenLearner.save_model" href="../core.html#ktrain.core.Learner.save_model">save_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_model" href="../core.html#ktrain.core.Learner.set_model">set_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_weight_decay" href="../core.html#ktrain.core.Learner.set_weight_decay">set_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.top_losses" href="../core.html#ktrain.core.Learner.top_losses">top_losses</a></code></li>
<li><code><a title="ktrain.core.GenLearner.unfreeze" href="../core.html#ktrain.core.Learner.unfreeze">unfreeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.validate" href="../core.html#ktrain.core.Learner.validate">validate</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
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<h1>Index</h1>
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<li><h3>Super-module</h3>
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<li><code><a title="ktrain.vision" href="index.html">ktrain.vision</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<h4><code><a title="ktrain.vision.learner.ImageClassLearner" href="#ktrain.vision.learner.ImageClassLearner">ImageClassLearner</a></code></h4>
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<li><code><a title="ktrain.vision.learner.ImageClassLearner.view_top_losses" href="#ktrain.vision.learner.ImageClassLearner.view_top_losses">view_top_losses</a></code></li>
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