deepscribe.models¶
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class
deepscribe.models.parametermodel.
ParameterModel
¶ generic class for a model that is initialized from a parameter dictionary. allows modularity and code reuse when building and training the model.
As of this writing, only implements the model building and training function - no model persistence is actually taken into account.
Must implement the functions _build_model and _train_model.
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class
deepscribe.models.cnn.
CNN2Conv
¶ Subclass of CNNAugment that implements a 2-layer CNN model.
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class
deepscribe.models.cnn.
CNNAugment
¶ Subclass of ParameterModel that trains a CNN image classification network with a data augmentation routine.
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class
deepscribe.models.cnn.
ResNet50
¶ Subclass of CNNAugment using architecture from ResNet50
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class
deepscribe.models.cnn.
ResNet50V2
¶ Subclass of CNNAugment using architecture from ResNet50
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class
deepscribe.models.cnn.
VGG16
¶ Subclass of CNNAugment that transfers learned weights from VGG16.
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class
deepscribe.models.cnn.
VGG19
¶ Subclass of CNNAugment that transfers learned weights from VGG19.
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deepscribe.models.baselines.
cnn_classifier_2conv
(x_train: numpy.array, y_train: numpy.array, x_val: numpy.array, y_val: numpy.array, params: Dict[KT, VT]) → Tuple[<sphinx.ext.autodoc.importer._MockObject object at 0x7fd514be2be0>, <sphinx.ext.autodoc.importer._MockObject object at 0x7fd514be2c18>]¶ Parameters: - x_train – training image data, with shape [n_images, x_dim, y_dim, n_channels]
- y_train – categorical variables with shape [n_images,]
- x_val – validation image data, with shape [n_images, x_dim, y_dim, n_channels]
- y_val – categorical variables with shape [n_images,]
- model – tf.Keras model.
- params – parameter dictionary.
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deepscribe.models.baselines.
cnn_classifier_4conv
(x_train: numpy.array, y_train: numpy.array, x_val: numpy.array, y_val: numpy.array, params: Dict[KT, VT]) → Tuple[<sphinx.ext.autodoc.importer._MockObject object at 0x7fd514be2a58>, <sphinx.ext.autodoc.importer._MockObject object at 0x7fd514be2a90>]¶ Parameters: - x_train – training image data, with shape [n_images, x_dim, y_dim, n_channels]
- y_train – categorical variables with shape [n_images,]
- x_val – validation image data, with shape [n_images, x_dim, y_dim, n_channels]
- y_val – categorical variables with shape [n_images,]
- model – tf.Keras model.
- params – parameter dictionary.