tfsnippet.applications¶
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class
tfsnippet.applications.
InceptionV3
(cache_dir=None)¶ Bases:
object
Inception V3 model from TensorFlow tutorial.
This class directly loads the persisted TensorFlow graph, instead of assembling the model using TensorFlow or Keras APIs. If you do need a freshly assembled model, you should turn to Keras instead.
The major purpose of this class is to compute the same Inception score as “Improved techniques for training gans”, Salimans, T. et al. 2016.
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N_CLASSES
= 1008¶
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__init__
(cache_dir=None)¶ Construct a new
InceptionV3
instance.Parameters: cache_dir (CacheDir) – The cache directory instance. If not specified, use a default one.
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get_labels
(classes)¶ Get the labels for specified classes.
Parameters: classes (Iterable[int]) – The IDs of the classes. Returns: The class labels. Return type: list[str]
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inception_score
(images)¶ Compute the Inception score (“Improved techniques for training gans”, Salimans, T. et al. 2016.) for specified images, using InceptionV3.
Parameters: images (list[bytes] or np.ndarray) – List of JPEG image data (each image as bytes), or numpy array of shape (?, ?, ?, 3)
, the pixels of images. Note the pixels should be 256-colors.Returns: The Inception score for images. Return type: float
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predict
(images)¶ Predict the class classes for images.
Parameters: images (list[bytes] or np.ndarray) – List of JPEG image data (each image as bytes), or numpy array of shape (?, ?, ?, 3)
, the pixels of images. Note the pixels should be 256-colors.Returns: The predicted classes. Return type: np.ndarray
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predict_log_proba
(images)¶ Predict the class log-probabilities for images.
Parameters: images (list[bytes] or np.ndarray) – List of JPEG image data (each image as bytes), or numpy array of shape (?, ?, ?, 3)
, the pixels of images. Note the pixels should be 256-colors.Returns: The predicted class log-probabilities. Return type: np.ndarray
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predict_logits
(images)¶ Predict the softmax logits (un-normalized log-proba) for images.
Parameters: images (list[bytes] or np.ndarray) – List of JPEG image data (each image as bytes), or numpy array of shape (?, ?, ?, 3)
, the pixels of images. Note the pixels should be 256-colors.Returns: The predicted softmax logits probabilities. Return type: np.ndarray
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predict_proba
(images)¶ Predict the class probabilities for images.
Parameters: images (list[bytes] or np.ndarray) – List of JPEG image data (each image as bytes), or numpy array of shape (?, ?, ?, 3)
, the pixels of images. Note the pixels should be 256-colors.Returns: The predicted class probabilities. Return type: np.ndarray
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