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predictor.html
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predictor.html
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.vision.predictor</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 *
from ..predictor import Predictor
from .preprocessor import ImagePreprocessor
class ImagePredictor(Predictor):
"""
```
predicts image classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, keras.Model):
raise ValueError("model must be of instance keras.Model")
if not isinstance(preproc, ImagePreprocessor):
raise ValueError("preproc must be instance of ImagePreprocessor")
self.model = model
self.preproc = preproc
self.datagen = self.preproc.get_preprocessor()
self.c = self.preproc.get_classes()
self.batch_size = batch_size
def get_classes(self):
return self.c
def explain(self, img_fpath):
"""
```
Highlights image to explain prediction
```
"""
try:
import eli5
except:
msg = (
"ktrain requires a forked version of eli5 to support tf.keras. "
+ "Install with: pip install https://github.com/amaiya/eli5-tf/archive/refs/heads/master.zip"
)
warnings.warn(msg)
return
if not DISABLE_V2_BEHAVIOR:
warnings.warn(
"Please add os.environ['DISABLE_V2_BEHAVIOR'] = '1' at top of your script or notebook."
)
msg = (
"\nFor image classification, the explain method currently requires disabling V2 behavior in TensorFlow 2.\n"
+ "Please add the following to the top of your script or notebook BEFORE you import ktrain and restart Colab runtime or Jupyter kernel:\n\n"
+ "import os\n"
+ "os.environ['DISABLE_V2_BEHAVIOR'] = '1'\n"
)
print(msg)
return
img = keras.preprocessing.image.load_img(
img_fpath,
target_size=self.preproc.target_size,
color_mode=self.preproc.color_mode,
)
x = keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
return eli5.show_prediction(self.model, x)
def predict(self, data, return_proba=False, verbose=0):
"""
```
Predicts class from image in array format.
If return_proba is True, returns probabilities of each class.
```
"""
if not isinstance(data, np.ndarray):
raise ValueError("data must be numpy.ndarray")
(generator, steps) = self.preproc.preprocess(data, batch_size=self.batch_size)
return self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)
def predict_filename(self, img_path, return_proba=False, verbose=0):
"""
```
Predicts class from filepath to single image file.
If return_proba is True, returns probabilities of each class.
```
"""
if not os.path.isfile(img_path):
raise ValueError("img_path must be valid file")
(generator, steps) = self.preproc.preprocess(
img_path, batch_size=self.batch_size
)
return self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)
def predict_folder(self, folder, return_proba=False, verbose=0):
"""
```
Predicts the classes of all images in a folder.
If return_proba is True, returns probabilities of each class.
```
"""
if not os.path.isdir(folder):
raise ValueError("folder must be valid directory")
(generator, steps) = self.preproc.preprocess(folder, batch_size=self.batch_size)
result = self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)
if len(result) != len(generator.filenames):
raise Exception("number of results does not equal number of filenames")
return list(zip(generator.filenames, result))
def predict_generator(self, generator, steps=None, return_proba=False, verbose=0):
# loss = self.model.loss
# if callable(loss): loss = loss.__name__
# treat_multilabel = False
# if loss != 'categorical_crossentropy' and not return_proba:
# return_proba=True
# treat_multilabel = True
classification, multilabel = U.is_classifier(self.model)
if not classification:
return_proba = True
# *_generator methods are deprecated from TF 2.1.0
# preds = self.model.predict_generator(generator, steps=steps)
preds = self.model.predict(generator, steps=steps, verbose=verbose)
result = (
preds
if return_proba or multilabel
else [self.c[np.argmax(pred)] for pred in preds]
)
if multilabel and not return_proba:
return [list(zip(self.c, r)) for r in result]
if not classification:
return np.squeeze(result, axis=1)
else:
return result
def predict_proba(self, data, verbose=0):
return self.predict(data, return_proba=True, verbose=verbose)
def predict_proba_folder(self, folder, verbose=0):
return self.predict_folder(folder, return_proba=True, verbose=verbose)
def predict_proba_filename(self, img_path, verbose=0):
return self.predict_filename(img_path, return_proba=True, verbose=verbose)
def predict_proba_generator(self, generator, steps=None, verbose=0):
return self.predict_proba_generator(
generator, steps=steps, return_proba=True, verbose=verbose
)
def analyze_valid(self, generator, print_report=True, multilabel=None):
"""
```
Makes predictions on validation set and returns the confusion matrix.
Accepts as input a genrator (e.g., DirectoryIterator, DataframeIterator)
representing the validation set.
Optionally prints a classification report.
Currently, this method is only supported for binary and multiclass
problems, not multilabel classification problems.
```
"""
if multilabel is None:
multilabel = U.is_multilabel(generator)
if multilabel:
warnings.warn("multilabel_confusion_matrix not yet supported - skipping")
return
y_true = generator.classes
# *_generator methods are deprecated from TF 2.1.0
# y_pred = self.model.predict_generator(generator)
y_pred = self.model.predict(generator)
y_pred = np.argmax(y_pred, axis=1)
if print_report:
print(classification_report(y_true, y_pred, target_names=self.c))
if not multilabel:
cm_func = confusion_matrix
cm = cm_func(y_true, y_pred)
else:
cm = None
return cm
def _save_preproc(self, fpath):
preproc_name = "tf_model.preproc"
with open(os.path.join(fpath, preproc_name), "wb") as f:
datagen = self.preproc.get_preprocessor()
pfunc = datagen.preprocessing_function
datagen.preprocessing_function = None
pickle.dump(self.preproc, f)
datagen.preprocessing_function = pfunc
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.predictor.ImagePredictor"><code class="flex name class">
<span>class <span class="ident">ImagePredictor</span></span>
<span>(</span><span>model, preproc, batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>predicts image classes
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ImagePredictor(Predictor):
"""
```
predicts image classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, keras.Model):
raise ValueError("model must be of instance keras.Model")
if not isinstance(preproc, ImagePreprocessor):
raise ValueError("preproc must be instance of ImagePreprocessor")
self.model = model
self.preproc = preproc
self.datagen = self.preproc.get_preprocessor()
self.c = self.preproc.get_classes()
self.batch_size = batch_size
def get_classes(self):
return self.c
def explain(self, img_fpath):
"""
```
Highlights image to explain prediction
```
"""
try:
import eli5
except:
msg = (
"ktrain requires a forked version of eli5 to support tf.keras. "
+ "Install with: pip install https://github.com/amaiya/eli5-tf/archive/refs/heads/master.zip"
)
warnings.warn(msg)
return
if not DISABLE_V2_BEHAVIOR:
warnings.warn(
"Please add os.environ['DISABLE_V2_BEHAVIOR'] = '1' at top of your script or notebook."
)
msg = (
"\nFor image classification, the explain method currently requires disabling V2 behavior in TensorFlow 2.\n"
+ "Please add the following to the top of your script or notebook BEFORE you import ktrain and restart Colab runtime or Jupyter kernel:\n\n"
+ "import os\n"
+ "os.environ['DISABLE_V2_BEHAVIOR'] = '1'\n"
)
print(msg)
return
img = keras.preprocessing.image.load_img(
img_fpath,
target_size=self.preproc.target_size,
color_mode=self.preproc.color_mode,
)
x = keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
return eli5.show_prediction(self.model, x)
def predict(self, data, return_proba=False, verbose=0):
"""
```
Predicts class from image in array format.
If return_proba is True, returns probabilities of each class.
```
"""
if not isinstance(data, np.ndarray):
raise ValueError("data must be numpy.ndarray")
(generator, steps) = self.preproc.preprocess(data, batch_size=self.batch_size)
return self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)
def predict_filename(self, img_path, return_proba=False, verbose=0):
"""
```
Predicts class from filepath to single image file.
If return_proba is True, returns probabilities of each class.
```
"""
if not os.path.isfile(img_path):
raise ValueError("img_path must be valid file")
(generator, steps) = self.preproc.preprocess(
img_path, batch_size=self.batch_size
)
return self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)
def predict_folder(self, folder, return_proba=False, verbose=0):
"""
```
Predicts the classes of all images in a folder.
If return_proba is True, returns probabilities of each class.
```
"""
if not os.path.isdir(folder):
raise ValueError("folder must be valid directory")
(generator, steps) = self.preproc.preprocess(folder, batch_size=self.batch_size)
result = self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)
if len(result) != len(generator.filenames):
raise Exception("number of results does not equal number of filenames")
return list(zip(generator.filenames, result))
def predict_generator(self, generator, steps=None, return_proba=False, verbose=0):
# loss = self.model.loss
# if callable(loss): loss = loss.__name__
# treat_multilabel = False
# if loss != 'categorical_crossentropy' and not return_proba:
# return_proba=True
# treat_multilabel = True
classification, multilabel = U.is_classifier(self.model)
if not classification:
return_proba = True
# *_generator methods are deprecated from TF 2.1.0
# preds = self.model.predict_generator(generator, steps=steps)
preds = self.model.predict(generator, steps=steps, verbose=verbose)
result = (
preds
if return_proba or multilabel
else [self.c[np.argmax(pred)] for pred in preds]
)
if multilabel and not return_proba:
return [list(zip(self.c, r)) for r in result]
if not classification:
return np.squeeze(result, axis=1)
else:
return result
def predict_proba(self, data, verbose=0):
return self.predict(data, return_proba=True, verbose=verbose)
def predict_proba_folder(self, folder, verbose=0):
return self.predict_folder(folder, return_proba=True, verbose=verbose)
def predict_proba_filename(self, img_path, verbose=0):
return self.predict_filename(img_path, return_proba=True, verbose=verbose)
def predict_proba_generator(self, generator, steps=None, verbose=0):
return self.predict_proba_generator(
generator, steps=steps, return_proba=True, verbose=verbose
)
def analyze_valid(self, generator, print_report=True, multilabel=None):
"""
```
Makes predictions on validation set and returns the confusion matrix.
Accepts as input a genrator (e.g., DirectoryIterator, DataframeIterator)
representing the validation set.
Optionally prints a classification report.
Currently, this method is only supported for binary and multiclass
problems, not multilabel classification problems.
```
"""
if multilabel is None:
multilabel = U.is_multilabel(generator)
if multilabel:
warnings.warn("multilabel_confusion_matrix not yet supported - skipping")
return
y_true = generator.classes
# *_generator methods are deprecated from TF 2.1.0
# y_pred = self.model.predict_generator(generator)
y_pred = self.model.predict(generator)
y_pred = np.argmax(y_pred, axis=1)
if print_report:
print(classification_report(y_true, y_pred, target_names=self.c))
if not multilabel:
cm_func = confusion_matrix
cm = cm_func(y_true, y_pred)
else:
cm = None
return cm
def _save_preproc(self, fpath):
preproc_name = "tf_model.preproc"
with open(os.path.join(fpath, preproc_name), "wb") as f:
datagen = self.preproc.get_preprocessor()
pfunc = datagen.preprocessing_function
datagen.preprocessing_function = None
pickle.dump(self.preproc, f)
datagen.preprocessing_function = pfunc
return</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.predictor.Predictor" href="../predictor.html#ktrain.predictor.Predictor">Predictor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.vision.predictor.ImagePredictor.analyze_valid"><code class="name flex">
<span>def <span class="ident">analyze_valid</span></span>(<span>self, generator, print_report=True, multilabel=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Makes predictions on validation set and returns the confusion matrix.
Accepts as input a genrator (e.g., DirectoryIterator, DataframeIterator)
representing the validation set.
Optionally prints a classification report.
Currently, this method is only supported for binary and multiclass
problems, not multilabel classification problems.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def analyze_valid(self, generator, print_report=True, multilabel=None):
"""
```
Makes predictions on validation set and returns the confusion matrix.
Accepts as input a genrator (e.g., DirectoryIterator, DataframeIterator)
representing the validation set.
Optionally prints a classification report.
Currently, this method is only supported for binary and multiclass
problems, not multilabel classification problems.
```
"""
if multilabel is None:
multilabel = U.is_multilabel(generator)
if multilabel:
warnings.warn("multilabel_confusion_matrix not yet supported - skipping")
return
y_true = generator.classes
# *_generator methods are deprecated from TF 2.1.0
# y_pred = self.model.predict_generator(generator)
y_pred = self.model.predict(generator)
y_pred = np.argmax(y_pred, axis=1)
if print_report:
print(classification_report(y_true, y_pred, target_names=self.c))
if not multilabel:
cm_func = confusion_matrix
cm = cm_func(y_true, y_pred)
else:
cm = None
return cm</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.explain"><code class="name flex">
<span>def <span class="ident">explain</span></span>(<span>self, img_fpath)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Highlights image to explain prediction
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def explain(self, img_fpath):
"""
```
Highlights image to explain prediction
```
"""
try:
import eli5
except:
msg = (
"ktrain requires a forked version of eli5 to support tf.keras. "
+ "Install with: pip install https://github.com/amaiya/eli5-tf/archive/refs/heads/master.zip"
)
warnings.warn(msg)
return
if not DISABLE_V2_BEHAVIOR:
warnings.warn(
"Please add os.environ['DISABLE_V2_BEHAVIOR'] = '1' at top of your script or notebook."
)
msg = (
"\nFor image classification, the explain method currently requires disabling V2 behavior in TensorFlow 2.\n"
+ "Please add the following to the top of your script or notebook BEFORE you import ktrain and restart Colab runtime or Jupyter kernel:\n\n"
+ "import os\n"
+ "os.environ['DISABLE_V2_BEHAVIOR'] = '1'\n"
)
print(msg)
return
img = keras.preprocessing.image.load_img(
img_fpath,
target_size=self.preproc.target_size,
color_mode=self.preproc.color_mode,
)
x = keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
return eli5.show_prediction(self.model, x)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.get_classes"><code class="name flex">
<span>def <span class="ident">get_classes</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 get_classes(self):
return self.c</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, data, return_proba=False, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Predicts class from image in array format.
If return_proba is True, returns probabilities of each class.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, data, return_proba=False, verbose=0):
"""
```
Predicts class from image in array format.
If return_proba is True, returns probabilities of each class.
```
"""
if not isinstance(data, np.ndarray):
raise ValueError("data must be numpy.ndarray")
(generator, steps) = self.preproc.preprocess(data, batch_size=self.batch_size)
return self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict_filename"><code class="name flex">
<span>def <span class="ident">predict_filename</span></span>(<span>self, img_path, return_proba=False, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Predicts class from filepath to single image file.
If return_proba is True, returns probabilities of each class.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_filename(self, img_path, return_proba=False, verbose=0):
"""
```
Predicts class from filepath to single image file.
If return_proba is True, returns probabilities of each class.
```
"""
if not os.path.isfile(img_path):
raise ValueError("img_path must be valid file")
(generator, steps) = self.preproc.preprocess(
img_path, batch_size=self.batch_size
)
return self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict_folder"><code class="name flex">
<span>def <span class="ident">predict_folder</span></span>(<span>self, folder, return_proba=False, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Predicts the classes of all images in a folder.
If return_proba is True, returns probabilities of each class.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_folder(self, folder, return_proba=False, verbose=0):
"""
```
Predicts the classes of all images in a folder.
If return_proba is True, returns probabilities of each class.
```
"""
if not os.path.isdir(folder):
raise ValueError("folder must be valid directory")
(generator, steps) = self.preproc.preprocess(folder, batch_size=self.batch_size)
result = self.predict_generator(
generator, steps=steps, return_proba=return_proba, verbose=verbose
)
if len(result) != len(generator.filenames):
raise Exception("number of results does not equal number of filenames")
return list(zip(generator.filenames, result))</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict_generator"><code class="name flex">
<span>def <span class="ident">predict_generator</span></span>(<span>self, generator, steps=None, return_proba=False, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_generator(self, generator, steps=None, return_proba=False, verbose=0):
# loss = self.model.loss
# if callable(loss): loss = loss.__name__
# treat_multilabel = False
# if loss != 'categorical_crossentropy' and not return_proba:
# return_proba=True
# treat_multilabel = True
classification, multilabel = U.is_classifier(self.model)
if not classification:
return_proba = True
# *_generator methods are deprecated from TF 2.1.0
# preds = self.model.predict_generator(generator, steps=steps)
preds = self.model.predict(generator, steps=steps, verbose=verbose)
result = (
preds
if return_proba or multilabel
else [self.c[np.argmax(pred)] for pred in preds]
)
if multilabel and not return_proba:
return [list(zip(self.c, r)) for r in result]
if not classification:
return np.squeeze(result, axis=1)
else:
return result</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict_proba"><code class="name flex">
<span>def <span class="ident">predict_proba</span></span>(<span>self, data, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba(self, data, verbose=0):
return self.predict(data, return_proba=True, verbose=verbose)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict_proba_filename"><code class="name flex">
<span>def <span class="ident">predict_proba_filename</span></span>(<span>self, img_path, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba_filename(self, img_path, verbose=0):
return self.predict_filename(img_path, return_proba=True, verbose=verbose)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict_proba_folder"><code class="name flex">
<span>def <span class="ident">predict_proba_folder</span></span>(<span>self, folder, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba_folder(self, folder, verbose=0):
return self.predict_folder(folder, return_proba=True, verbose=verbose)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.predictor.ImagePredictor.predict_proba_generator"><code class="name flex">
<span>def <span class="ident">predict_proba_generator</span></span>(<span>self, generator, steps=None, verbose=0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba_generator(self, generator, steps=None, verbose=0):
return self.predict_proba_generator(
generator, steps=steps, return_proba=True, verbose=verbose
)</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.predictor.Predictor" href="../predictor.html#ktrain.predictor.Predictor">Predictor</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.predictor.Predictor.create_onnx_session" href="../predictor.html#ktrain.predictor.Predictor.create_onnx_session">create_onnx_session</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_onnx" href="../predictor.html#ktrain.predictor.Predictor.export_model_to_onnx">export_model_to_onnx</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_tflite" href="../predictor.html#ktrain.predictor.Predictor.export_model_to_tflite">export_model_to_tflite</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.save" href="../predictor.html#ktrain.predictor.Predictor.save">save</a></code></li>
</ul>
</li>
</ul>
</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.vision" href="index.html">ktrain.vision</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.vision.predictor.ImagePredictor" href="#ktrain.vision.predictor.ImagePredictor">ImagePredictor</a></code></h4>
<ul class="">
<li><code><a title="ktrain.vision.predictor.ImagePredictor.analyze_valid" href="#ktrain.vision.predictor.ImagePredictor.analyze_valid">analyze_valid</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.explain" href="#ktrain.vision.predictor.ImagePredictor.explain">explain</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.get_classes" href="#ktrain.vision.predictor.ImagePredictor.get_classes">get_classes</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict" href="#ktrain.vision.predictor.ImagePredictor.predict">predict</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict_filename" href="#ktrain.vision.predictor.ImagePredictor.predict_filename">predict_filename</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict_folder" href="#ktrain.vision.predictor.ImagePredictor.predict_folder">predict_folder</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict_generator" href="#ktrain.vision.predictor.ImagePredictor.predict_generator">predict_generator</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict_proba" href="#ktrain.vision.predictor.ImagePredictor.predict_proba">predict_proba</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict_proba_filename" href="#ktrain.vision.predictor.ImagePredictor.predict_proba_filename">predict_proba_filename</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict_proba_folder" href="#ktrain.vision.predictor.ImagePredictor.predict_proba_folder">predict_proba_folder</a></code></li>
<li><code><a title="ktrain.vision.predictor.ImagePredictor.predict_proba_generator" href="#ktrain.vision.predictor.ImagePredictor.predict_proba_generator">predict_proba_generator</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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