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<main>
<article id="content">
<header>
<h1 class="title">Package <code>ktrain</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from . import imports as I
from . import utils as U
from .core import (
ArrayLearner,
GenLearner,
get_predictor,
load_predictor,
release_gpu_memory,
)
from .graph.learner import LinkPredLearner, NodeClassLearner
from .text.learner import BERTTextClassLearner, TransformerTextClassLearner
from .text.ner.learner import NERLearner
from .version import __version__
from .vision.learner import ImageClassLearner
__all__ = ["get_learner", "get_predictor", "load_predictor", "release_gpu_memory"]
def get_learner(
model,
train_data=None,
val_data=None,
batch_size=U.DEFAULT_BS,
eval_batch_size=U.DEFAULT_BS,
workers=1,
use_multiprocessing=False,
):
"""
```
Returns a Learner instance that can be used to tune and train Keras models.
model (Model): A compiled instance of keras.engine.training.Model
train_data (tuple or generator): Either a:
1) tuple of (x_train, y_train), where x_train and
y_train are numpy.ndarrays or
2) Iterator
val_data (tuple or generator): Either a:
1) tuple of (x_test, y_test), where x_testand
y_test are numpy.ndarrays or
2) Iterator
Note: Should be same type as train_data.
batch_size (int): Batch size to use in training. default:32
eval_batch_size(int): batch size used by learner.predict
only applies to validaton data during training if
val_data is instance of utils.Sequence.
default:32
workers (int): number of cpu processes used to load data.
This is ignored unless train_data/val_data is an instance of
tf.keras.preprocessing.image.DirectoryIterator or tf.keras.preprocessing.image.DataFrameIterator.
use_multiprocessing(bool): whether or not to use multiprocessing for workers
This is ignored unless train_data/val_data is an instance of
tf.keras.preprocessing.image.DirectoryIterator or tf.keras.preprocessing.image.DataFrameIterator.
```
"""
# check arguments
if not isinstance(model, I.keras.Model):
raise ValueError("model must be of instance Model")
U.data_arg_check(train_data=train_data, val_data=val_data)
if type(workers) != type(1) or workers < 1:
workers = 1
# check for NumpyArrayIterator
if train_data and not U.ondisk(train_data):
if workers > 1 and not use_multiprocessing:
use_multiprocessing = True
wrn_msg = "Changed use_multiprocessing to True because NumpyArrayIterator with workers>1"
wrn_msg += " is slow when use_multiprocessing=False."
wrn_msg += " If you experience issues with this, please set workers=1 and use_multiprocessing=False."
I.warnings.warn(wrn_msg)
# verify BERT
is_bert = U.bert_data_tuple(train_data)
if is_bert:
maxlen = U.shape_from_data(train_data)[1]
msg = """For a GPU with 12GB of RAM, the following maxima apply:
sequence len=64, max_batch_size=64
sequence len=128, max_batch_size=32
sequence len=256, max_batch_size=16
sequence len=320, max_batch_size=14
sequence len=384, max_batch_size=12
sequence len=512, max_batch_size=6
You've exceeded these limits.
If using a GPU with <=12GB of memory, you may run out of memory during training.
If necessary, adjust sequence length or batch size based on above."""
wrn = False
if maxlen > 64 and batch_size > 64:
wrn = True
elif maxlen > 128 and batch_size > 32:
wrn = True
elif maxlen > 256 and batch_size > 16:
wrn = True
elif maxlen > 320 and batch_size > 14:
wrn = True
elif maxlen > 384 and batch_size > 12:
wrn = True
elif maxlen > 512 and batch_size > 6:
wrn = True
if wrn:
I.warnings.warn(msg)
# return the appropriate trainer
if U.is_iter(train_data):
if U.is_ner(model=model, data=train_data):
learner = NERLearner
elif U.is_imageclass_from_data(train_data):
learner = ImageClassLearner
elif U.is_nodeclass(data=train_data):
learner = NodeClassLearner
elif U.is_nodeclass(data=train_data):
learner = LinkPredLearner
elif U.is_huggingface(data=train_data):
learner = TransformerTextClassLearner
else:
learner = GenLearner
else:
if is_bert:
learner = BERTTextClassLearner
else: # vanilla text classifiers use standard ArrayLearners
learner = ArrayLearner
l = learner(
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,
)
import tensorflow as tf
from tensorflow.keras.optimizers import Optimizer
import warnings
from packaging import version
if (version.parse(tf.__version__) >= version.parse("2.11")) and (
isinstance(l.model.optimizer, Optimizer)
):
warnings.warn(
"ktrain currently only supports legacy optimizers in tensorflow>=2.11 - recompiling your model to use legacy Adam"
)
l._recompile(wd=0)
return l
# keys
# currently_unsupported: unsupported or disabled features (e.g., xai graph neural networks have not been implemented)
# dep_fix: a fix to address a problem in a dependency
# TODO: things to change
# NOTES: As of 0.30.x, TensorFlow is optional and no longer forced to allow for use of pretrained PyTorch or sklearn models.
# In core, lroptimize imports were localized to allow for optional TF
# References to ktrain.dataset (keras.utils) and anago (keras.Callback) were also localized (from module-level) for optional TF</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="ktrain.core" href="core.html">ktrain.core</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.dataset" href="dataset.html">ktrain.dataset</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.graph" href="graph/index.html">ktrain.graph</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.imports" href="imports.html">ktrain.imports</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.lroptimize" href="lroptimize/index.html">ktrain.lroptimize</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.models" href="models.html">ktrain.models</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.predictor" href="predictor.html">ktrain.predictor</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.preprocessor" href="preprocessor.html">ktrain.preprocessor</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.tabular" href="tabular/index.html">ktrain.tabular</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text" href="text/index.html">ktrain.text</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.torch_base" href="torch_base.html">ktrain.torch_base</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.utils" href="utils.html">ktrain.utils</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.version" href="version.html">ktrain.version</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision" href="vision/index.html">ktrain.vision</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.get_learner"><code class="name flex">
<span>def <span class="ident">get_learner</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>Returns a Learner instance that can be used to tune and train Keras models.
model (Model): A compiled instance of keras.engine.training.Model
train_data (tuple or generator): Either a:
1) tuple of (x_train, y_train), where x_train and
y_train are numpy.ndarrays or
2) Iterator
val_data (tuple or generator): Either a:
1) tuple of (x_test, y_test), where x_testand
y_test are numpy.ndarrays or
2) Iterator
Note: Should be same type as train_data.
batch_size (int): Batch size to use in training. default:32
eval_batch_size(int): batch size used by learner.predict
only applies to validaton data during training if
val_data is instance of utils.Sequence.
default:32
workers (int): number of cpu processes used to load data.
This is ignored unless train_data/val_data is an instance of
tf.keras.preprocessing.image.DirectoryIterator or tf.keras.preprocessing.image.DataFrameIterator.
use_multiprocessing(bool): whether or not to use multiprocessing for workers
This is ignored unless train_data/val_data is an instance of
tf.keras.preprocessing.image.DirectoryIterator or tf.keras.preprocessing.image.DataFrameIterator.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_learner(
model,
train_data=None,
val_data=None,
batch_size=U.DEFAULT_BS,
eval_batch_size=U.DEFAULT_BS,
workers=1,
use_multiprocessing=False,
):
"""
```
Returns a Learner instance that can be used to tune and train Keras models.
model (Model): A compiled instance of keras.engine.training.Model
train_data (tuple or generator): Either a:
1) tuple of (x_train, y_train), where x_train and
y_train are numpy.ndarrays or
2) Iterator
val_data (tuple or generator): Either a:
1) tuple of (x_test, y_test), where x_testand
y_test are numpy.ndarrays or
2) Iterator
Note: Should be same type as train_data.
batch_size (int): Batch size to use in training. default:32
eval_batch_size(int): batch size used by learner.predict
only applies to validaton data during training if
val_data is instance of utils.Sequence.
default:32
workers (int): number of cpu processes used to load data.
This is ignored unless train_data/val_data is an instance of
tf.keras.preprocessing.image.DirectoryIterator or tf.keras.preprocessing.image.DataFrameIterator.
use_multiprocessing(bool): whether or not to use multiprocessing for workers
This is ignored unless train_data/val_data is an instance of
tf.keras.preprocessing.image.DirectoryIterator or tf.keras.preprocessing.image.DataFrameIterator.
```
"""
# check arguments
if not isinstance(model, I.keras.Model):
raise ValueError("model must be of instance Model")
U.data_arg_check(train_data=train_data, val_data=val_data)
if type(workers) != type(1) or workers < 1:
workers = 1
# check for NumpyArrayIterator
if train_data and not U.ondisk(train_data):
if workers > 1 and not use_multiprocessing:
use_multiprocessing = True
wrn_msg = "Changed use_multiprocessing to True because NumpyArrayIterator with workers>1"
wrn_msg += " is slow when use_multiprocessing=False."
wrn_msg += " If you experience issues with this, please set workers=1 and use_multiprocessing=False."
I.warnings.warn(wrn_msg)
# verify BERT
is_bert = U.bert_data_tuple(train_data)
if is_bert:
maxlen = U.shape_from_data(train_data)[1]
msg = """For a GPU with 12GB of RAM, the following maxima apply:
sequence len=64, max_batch_size=64
sequence len=128, max_batch_size=32
sequence len=256, max_batch_size=16
sequence len=320, max_batch_size=14
sequence len=384, max_batch_size=12
sequence len=512, max_batch_size=6
You've exceeded these limits.
If using a GPU with <=12GB of memory, you may run out of memory during training.
If necessary, adjust sequence length or batch size based on above."""
wrn = False
if maxlen > 64 and batch_size > 64:
wrn = True
elif maxlen > 128 and batch_size > 32:
wrn = True
elif maxlen > 256 and batch_size > 16:
wrn = True
elif maxlen > 320 and batch_size > 14:
wrn = True
elif maxlen > 384 and batch_size > 12:
wrn = True
elif maxlen > 512 and batch_size > 6:
wrn = True
if wrn:
I.warnings.warn(msg)
# return the appropriate trainer
if U.is_iter(train_data):
if U.is_ner(model=model, data=train_data):
learner = NERLearner
elif U.is_imageclass_from_data(train_data):
learner = ImageClassLearner
elif U.is_nodeclass(data=train_data):
learner = NodeClassLearner
elif U.is_nodeclass(data=train_data):
learner = LinkPredLearner
elif U.is_huggingface(data=train_data):
learner = TransformerTextClassLearner
else:
learner = GenLearner
else:
if is_bert:
learner = BERTTextClassLearner
else: # vanilla text classifiers use standard ArrayLearners
learner = ArrayLearner
l = learner(
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,
)
import tensorflow as tf
from tensorflow.keras.optimizers import Optimizer
import warnings
from packaging import version
if (version.parse(tf.__version__) >= version.parse("2.11")) and (
isinstance(l.model.optimizer, Optimizer)
):
warnings.warn(
"ktrain currently only supports legacy optimizers in tensorflow>=2.11 - recompiling your model to use legacy Adam"
)
l._recompile(wd=0)
return l</code></pre>
</details>
</dd>
<dt id="ktrain.get_predictor"><code class="name flex">
<span>def <span class="ident">get_predictor</span></span>(<span>model, preproc, batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns a Predictor instance that can be used to make predictions on
unlabeled examples. Can be saved to disk and reloaded as part of a
larger application.
Args
model (Model): A compiled instance of keras.engine.training.Model
preproc(Preprocessor): An instance of TextPreprocessor,ImagePreprocessor,
or NERPreprocessor.
These instances are returned from the data loading
functions in the ktrain vision and text modules:
ktrain.vision.images_from_folder
ktrain.vision.images_from_csv
ktrain.vision.images_from_array
ktrain.text.texts_from_folder
ktrain.text.texts_from_csv
ktrain.text.ner.entities_from_csv
batch_size(int): batch size to use. default:32
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_predictor(model, preproc, batch_size=U.DEFAULT_BS):
"""
```
Returns a Predictor instance that can be used to make predictions on
unlabeled examples. Can be saved to disk and reloaded as part of a
larger application.
Args
model (Model): A compiled instance of keras.engine.training.Model
preproc(Preprocessor): An instance of TextPreprocessor,ImagePreprocessor,
or NERPreprocessor.
These instances are returned from the data loading
functions in the ktrain vision and text modules:
ktrain.vision.images_from_folder
ktrain.vision.images_from_csv
ktrain.vision.images_from_array
ktrain.text.texts_from_folder
ktrain.text.texts_from_csv
ktrain.text.ner.entities_from_csv
batch_size(int): batch size to use. default:32
```
"""
# check arguments
if not isinstance(model, keras.Model):
raise ValueError("model must be of instance keras.Model")
if not isinstance(
preproc,
(
ImagePreprocessor,
TextPreprocessor,
NERPreprocessor,
NodePreprocessor,
LinkPreprocessor,
TabularPreprocessor,
),
):
raise ValueError("preproc must be instance of ktrain.preprocessor.Preprocessor")
if isinstance(preproc, ImagePreprocessor):
return ImagePredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, TextPreprocessor):
# elif type(preproc).__name__ == 'TextPreprocessor':
return TextPredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, NERPreprocessor):
return NERPredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, NodePreprocessor):
return NodePredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, LinkPreprocessor):
return LinkPredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, TabularPreprocessor):
return TabularPredictor(model, preproc, batch_size=batch_size)
else:
raise Exception("preproc of type %s not currently supported" % (type(preproc)))</code></pre>
</details>
</dd>
<dt id="ktrain.load_predictor"><code class="name flex">
<span>def <span class="ident">load_predictor</span></span>(<span>fpath, batch_size=32, custom_objects=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Loads a previously saved Predictor instance
Args
fpath(str): predictor path name (value supplied to predictor.save)
From v0.16.x, this is always the path to a folder.
Pre-v0.16.x, this is the base name used to save model and .preproc instance.
batch_size(int): batch size to use for predictions. default:32
custom_objects(dict): custom objects required to load model.
This is useful if you compiled the model with a custom loss function, for example.
For models included with ktrain as is, this is populated automatically
and can be disregarded.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load_predictor(fpath, batch_size=U.DEFAULT_BS, custom_objects=None):
"""
```
Loads a previously saved Predictor instance
Args
fpath(str): predictor path name (value supplied to predictor.save)
From v0.16.x, this is always the path to a folder.
Pre-v0.16.x, this is the base name used to save model and .preproc instance.
batch_size(int): batch size to use for predictions. default:32
custom_objects(dict): custom objects required to load model.
This is useful if you compiled the model with a custom loss function, for example.
For models included with ktrain as is, this is populated automatically
and can be disregarded.
```
"""
# load the preprocessor
preproc = None
try:
preproc_name = os.path.join(fpath, U.PREPROC_NAME)
with open(preproc_name, "rb") as f:
preproc = pickle.load(f)
except:
try:
preproc_name = fpath + ".preproc"
# warnings.warn('could not load .preproc file as %s - attempting to load as %s' % (os.path.join(fpath, U.PREPROC_NAME), preproc_name))
with open(preproc_name, "rb") as f:
preproc = pickle.load(f)
except:
raise Exception(
"Failed to load .preproc file in either the post v0.16.x loction (%s) or pre v0.16.x location (%s)"
% (os.path.join(fpath, U.PREPROC_NAME), fpath + ".preproc")
)
# load the model
model = _load_model(fpath, preproc=preproc, custom_objects=custom_objects)
# preprocessing functions in ImageDataGenerators are not pickable
# so, we must reconstruct
if hasattr(preproc, "datagen") and hasattr(preproc.datagen, "ktrain_preproc"):
preproc_name = preproc.datagen.ktrain_preproc
if preproc_name == "resnet50":
preproc.datagen.preprocessing_function = (
keras.applications.resnet50.preprocess_input
)
elif preproc_name == "mobilenet":
preproc.datagen.preprocessing_function = (
keras.applications.mobilenet.preprocess_input
)
elif preproc_name == "mobilenetv3":
preproc.datagen.preprocessing_function = (
keras.applications.mobilenet_v3.preprocess_input
)
elif preproc_name == "inception":
preproc.datagen.preprocessing_function = (
keras.applications.inception_v3.preprocess_input
)
elif preproc_name == "efficientnet":
preproc.datagen.preprocessing_function = (
keras.applications.efficientnet.preprocess_input
)
else:
raise Exception("Uknown preprocessing_function name: %s" % (preproc_name))
# return the appropriate predictor
if not isinstance(model, keras.Model):
raise ValueError("model must be of instance keras.Model")
if not isinstance(
preproc,
(
ImagePreprocessor,
TextPreprocessor,
NERPreprocessor,
NodePreprocessor,
LinkPreprocessor,
TabularPreprocessor,
),
):
raise ValueError("preproc must be instance of ktrain.preprocessor.Preprocessor")
if isinstance(preproc, ImagePreprocessor):
return ImagePredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, TextPreprocessor):
return TextPredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, NERPreprocessor):
return NERPredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, NodePreprocessor):
return NodePredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, LinkPreprocessor):
return LinkPredictor(model, preproc, batch_size=batch_size)
elif isinstance(preproc, TabularPreprocessor):
return TabularPredictor(model, preproc, batch_size=batch_size)
else:
raise Exception("preprocessor not currently supported")</code></pre>
</details>
</dd>
<dt id="ktrain.release_gpu_memory"><code class="name flex">
<span>def <span class="ident">release_gpu_memory</span></span>(<span>device=0)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Relase GPU memory allocated by Tensorflow
Source:
https://stackoverflow.com/questions/51005147/keras-release-memory-after-finish-training-process
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def release_gpu_memory(device=0):
"""
```
Relase GPU memory allocated by Tensorflow
Source:
https://stackoverflow.com/questions/51005147/keras-release-memory-after-finish-training-process
```
"""
from numba import cuda
K.clear_session()
cuda.select_device(device)
cuda.close()
return</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="ktrain.core" href="core.html">ktrain.core</a></code></li>
<li><code><a title="ktrain.dataset" href="dataset.html">ktrain.dataset</a></code></li>
<li><code><a title="ktrain.graph" href="graph/index.html">ktrain.graph</a></code></li>
<li><code><a title="ktrain.imports" href="imports.html">ktrain.imports</a></code></li>
<li><code><a title="ktrain.lroptimize" href="lroptimize/index.html">ktrain.lroptimize</a></code></li>
<li><code><a title="ktrain.models" href="models.html">ktrain.models</a></code></li>
<li><code><a title="ktrain.predictor" href="predictor.html">ktrain.predictor</a></code></li>
<li><code><a title="ktrain.preprocessor" href="preprocessor.html">ktrain.preprocessor</a></code></li>
<li><code><a title="ktrain.tabular" href="tabular/index.html">ktrain.tabular</a></code></li>
<li><code><a title="ktrain.text" href="text/index.html">ktrain.text</a></code></li>
<li><code><a title="ktrain.torch_base" href="torch_base.html">ktrain.torch_base</a></code></li>
<li><code><a title="ktrain.utils" href="utils.html">ktrain.utils</a></code></li>
<li><code><a title="ktrain.version" href="version.html">ktrain.version</a></code></li>
<li><code><a title="ktrain.vision" href="vision/index.html">ktrain.vision</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.get_learner" href="#ktrain.get_learner">get_learner</a></code></li>
<li><code><a title="ktrain.get_predictor" href="#ktrain.get_predictor">get_predictor</a></code></li>
<li><code><a title="ktrain.load_predictor" href="#ktrain.load_predictor">load_predictor</a></code></li>
<li><code><a title="ktrain.release_gpu_memory" href="#ktrain.release_gpu_memory">release_gpu_memory</a></code></li>
</ul>
</li>
</ul>
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