Export Keras image preprocessing layers into the experimental namespace.
PiperOrigin-RevId: 299956491 Change-Id: I2f5a5ea74cbd2ffcfbb254d99c9d54a4bbd81f34
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
185dd2376b
commit
8f6e6c541f
@ -57,6 +57,7 @@ keras_packages = [
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"tensorflow.python.keras.layers.noise",
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"tensorflow.python.keras.layers.normalization",
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"tensorflow.python.keras.layers.normalization_v2",
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"tensorflow.python.keras.layers.preprocessing",
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"tensorflow.python.keras.layers.pooling",
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"tensorflow.python.keras.layers.recurrent",
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"tensorflow.python.keras.layers.recurrent_v2",
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@ -29,6 +29,19 @@ from tensorflow.python.keras.engine.input_spec import InputSpec
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from tensorflow.python.keras.engine.base_layer import Layer
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from tensorflow.python.keras.engine.base_preprocessing_layer import PreprocessingLayer
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# Image preprocessing layers.
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import CenterCrop
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomCrop
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomFlip
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomContrast
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomHeight
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomRotation
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomTranslation
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomWidth
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import RandomZoom
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import Resizing
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import Rescaling
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# Preprocessing layers.
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if tf2.enabled():
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from tensorflow.python.keras.layers.preprocessing.normalization import Normalization
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@ -44,7 +57,6 @@ else:
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from tensorflow.python.keras.layers.preprocessing.text_vectorization_v1 import TextVectorization
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from tensorflow.python.keras.layers.preprocessing.text_vectorization import TextVectorization as TextVectorizationV2
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TextVectorizationV1 = TextVectorization
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from tensorflow.python.keras.layers.preprocessing.image_preprocessing import Rescaling
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# Advanced activations.
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from tensorflow.python.keras.layers.advanced_activations import LeakyReLU
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@ -36,6 +36,7 @@ from tensorflow.python.ops import image_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import stateful_random_ops
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from tensorflow.python.ops import stateless_random_ops
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from tensorflow.python.util.tf_export import keras_export
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ResizeMethod = image_ops.ResizeMethod
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@ -51,6 +52,7 @@ _RESIZE_METHODS = {
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}
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@keras_export('keras.layers.experimental.preprocessing.Resizing')
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class Resizing(Layer):
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"""Image resizing layer.
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@ -63,15 +65,21 @@ class Resizing(Layer):
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interpolation: String, the interpolation method. Defaults to `bilinear`.
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Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`,
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`gaussian`, `mitchellcubic`
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name: A string, the name of the layer.
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"""
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def __init__(self, height, width, interpolation='bilinear', **kwargs):
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def __init__(self,
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height,
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width,
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interpolation='bilinear',
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name=None,
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**kwargs):
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self.target_height = height
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self.target_width = width
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self.interpolation = interpolation
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self._interpolation_method = get_interpolation(interpolation)
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self.input_spec = InputSpec(ndim=4)
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super(Resizing, self).__init__(**kwargs)
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super(Resizing, self).__init__(name=name, **kwargs)
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def build(self, input_shape):
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channel_axis = 3
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@ -101,6 +109,7 @@ class Resizing(Layer):
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export('keras.layers.experimental.preprocessing.CenterCrop')
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class CenterCrop(Layer):
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"""Crop the central portion of the images to target height and width.
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@ -118,13 +127,14 @@ class CenterCrop(Layer):
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Arguments:
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height: Integer, the height of the output shape.
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width: Integer, the width of the output shape.
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name: A string, the name of the layer.
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"""
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def __init__(self, height, width, **kwargs):
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def __init__(self, height, width, name=None, **kwargs):
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self.target_height = height
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self.target_width = width
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self.input_spec = InputSpec(ndim=4)
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super(CenterCrop, self).__init__(**kwargs)
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super(CenterCrop, self).__init__(name=name, **kwargs)
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def build(self, input_shape):
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channel_axis = 3
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@ -173,6 +183,7 @@ class CenterCrop(Layer):
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export('keras.layers.experimental.preprocessing.RandomCrop')
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class RandomCrop(Layer):
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"""Randomly crop the images to target height and width.
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@ -195,15 +206,16 @@ class RandomCrop(Layer):
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height: Integer, the height of the output shape.
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width: Integer, the width of the output shape.
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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"""
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def __init__(self, height, width, seed=None, **kwargs):
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def __init__(self, height, width, seed=None, name=None, **kwargs):
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self.height = height
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self.width = width
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self.seed = seed
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self._rng = make_generator(self.seed)
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self.input_spec = InputSpec(ndim=4)
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super(RandomCrop, self).__init__(**kwargs)
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super(RandomCrop, self).__init__(name=name, **kwargs)
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def call(self, inputs, training=None):
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if training is None:
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@ -278,6 +290,7 @@ class RandomCrop(Layer):
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export('keras.layers.experimental.preprocessing.Rescaling')
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class Rescaling(Layer):
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"""Multiply inputs by `scale`.
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@ -294,11 +307,12 @@ class Rescaling(Layer):
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Arguments:
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scale: Float, the scale to apply to the inputs.
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name: A string, the name of the layer.
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"""
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def __init__(self, scale, **kwargs):
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def __init__(self, scale, name=None, **kwargs):
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self.scale = scale
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super(Rescaling, self).__init__(**kwargs)
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super(Rescaling, self).__init__(name=name, **kwargs)
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def call(self, inputs):
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dtype = self._compute_dtype
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@ -315,13 +329,16 @@ class Rescaling(Layer):
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return dict(list(base_config.items()) + list(config.items()))
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HORIZONTAL = 'horizontal'
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VERTICAL = 'vertical'
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HORIZONTAL_AND_VERTICAL = 'horizontal_and_vertical'
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@keras_export('keras.layers.experimental.preprocessing.RandomFlip')
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class RandomFlip(Layer):
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"""Randomly flip each image horizontally and vertically.
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This layer will by default flip the images horizontally and then vertically
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during training time.
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`RandomFlip(horizontal=True)` will only flip the input horizontally.
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`RandomFlip(vertical=True)` will only flip the input vertically.
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This layer will flip the images based on the `mode` attribute.
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During inference time, the output will be identical to input. Call the layer
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with `training=True` to flip the input.
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@ -334,23 +351,35 @@ class RandomFlip(Layer):
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`(samples, height, width, channels)`, data_format='channels_last'.
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Attributes:
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horizontal: Bool, whether to randomly flip horizontally.
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width: Bool, whether to randomly flip vertically.
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mode: String indicating which flip mode to use. Can be "horizontal",
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"vertical", or "horizontal_and_vertical". Defaults to
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"horizontal_and_vertical".
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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"""
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def __init__(self, horizontal=None, vertical=None, seed=None, **kwargs):
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# If both arguments are None, set both to True.
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if horizontal is None and vertical is None:
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def __init__(self,
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mode=HORIZONTAL_AND_VERTICAL,
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seed=None,
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name=None,
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**kwargs):
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super(RandomFlip, self).__init__(name=name, **kwargs)
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self.mode = mode
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if mode == HORIZONTAL:
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self.horizontal = True
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self.vertical = False
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elif mode == VERTICAL:
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self.horizontal = False
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self.vertical = True
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elif mode == HORIZONTAL_AND_VERTICAL:
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self.horizontal = True
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self.vertical = True
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else:
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self.horizontal = horizontal or False
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self.vertical = vertical or False
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raise ValueError('RandomFlip layer {name} received an unknown mode '
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'argument {arg}'.format(name=name, arg=mode))
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self.seed = seed
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self._rng = make_generator(self.seed)
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self.input_spec = InputSpec(ndim=4)
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super(RandomFlip, self).__init__(**kwargs)
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def call(self, inputs, training=None):
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if training is None:
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@ -376,14 +405,15 @@ class RandomFlip(Layer):
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def get_config(self):
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config = {
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'horizontal': self.horizontal,
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'vertical': self.vertical,
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'mode': self.mode,
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'seed': self.seed,
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}
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base_config = super(RandomFlip, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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# TODO(tanzheny): Add examples, here and everywhere.
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@keras_export('keras.layers.experimental.preprocessing.RandomTranslation')
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class RandomTranslation(Layer):
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"""Randomly translate each image during training.
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@ -404,12 +434,16 @@ class RandomTranslation(Layer):
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fill_value: Value used for points outside the boundaries of the input if
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`mode='constant'`.
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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Input shape:
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4D tensor with shape: `(samples, height, width, channels)`,
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data_format='channels_last'.
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Output shape:
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4D tensor with shape: `(samples, height, width, channels)`,
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data_format='channels_last'.
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Raise:
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ValueError: if lower bound is not between [0, 1], or upper bound is
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negative.
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@ -421,6 +455,7 @@ class RandomTranslation(Layer):
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fill_mode='nearest',
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fill_value=0.,
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seed=None,
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name=None,
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**kwargs):
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self.height_factor = height_factor
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if isinstance(height_factor, (tuple, list)):
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@ -456,7 +491,7 @@ class RandomTranslation(Layer):
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self.seed = seed
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self._rng = make_generator(self.seed)
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self.input_spec = InputSpec(ndim=4)
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super(RandomTranslation, self).__init__(**kwargs)
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super(RandomTranslation, self).__init__(name=name, **kwargs)
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def call(self, inputs, training=None):
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if training is None:
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@ -636,6 +671,7 @@ def get_rotation_matrix(angles, image_height, image_width, name=None):
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axis=1)
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@keras_export('keras.layers.experimental.preprocessing.RandomRotation')
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class RandomRotation(Layer):
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"""Randomly rotate each image.
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@ -659,6 +695,8 @@ class RandomRotation(Layer):
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to the given mode (one of `{'constant', 'nearest', 'bilinear', 'reflect',
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'wrap'}`).
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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Raise:
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ValueError: if lower bound is not between [0, 1], or upper bound is
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negative.
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@ -668,6 +706,7 @@ class RandomRotation(Layer):
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factor,
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fill_mode='nearest',
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seed=None,
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name=None,
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**kwargs):
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self.factor = factor
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if isinstance(factor, (tuple, list)):
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@ -685,7 +724,7 @@ class RandomRotation(Layer):
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self.seed = seed
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self._rng = make_generator(self.seed)
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self.input_spec = InputSpec(ndim=4)
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super(RandomRotation, self).__init__(**kwargs)
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super(RandomRotation, self).__init__(name=name, **kwargs)
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def call(self, inputs, training=None):
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if training is None:
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@ -725,6 +764,7 @@ class RandomRotation(Layer):
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export('keras.layers.experimental.preprocessing.RandomZoom')
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class RandomZoom(Layer):
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"""Randomly zoom each image during training.
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@ -744,6 +784,7 @@ class RandomZoom(Layer):
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fill_value: Value used for points outside the boundaries of the input if
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`mode='constant'`.
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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Input shape:
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4D tensor with shape:
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@ -764,6 +805,7 @@ class RandomZoom(Layer):
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fill_mode='nearest',
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fill_value=0.,
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seed=None,
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name=None,
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**kwargs):
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self.height_factor = height_factor
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if isinstance(height_factor, (tuple, list)):
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@ -799,7 +841,7 @@ class RandomZoom(Layer):
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self.seed = seed
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self._rng = make_generator(self.seed)
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self.input_spec = InputSpec(ndim=4)
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super(RandomZoom, self).__init__(**kwargs)
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super(RandomZoom, self).__init__(name=name, **kwargs)
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def call(self, inputs, training=None):
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if training is None:
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@ -890,6 +932,7 @@ def get_zoom_matrix(zooms, image_height, image_width, name=None):
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axis=1)
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@keras_export('keras.layers.experimental.preprocessing.RandomContrast')
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class RandomContrast(Layer):
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"""Adjust the contrast of an image or images by a random factor.
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@ -909,17 +952,19 @@ class RandomContrast(Layer):
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`(samples, height, width, channels)`, data_format='channels_last'.
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Attributes:
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factor: a positive float represented as fraction of value, or a tuple
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of size 2 representing lower and upper bound. When represented as a
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single float, lower = upper. The contrast factor will be randomly picked
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between [1.0 - lower, 1.0 + upper].
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factor: a positive float represented as fraction of value, or a tuple of
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size 2 representing lower and upper bound. When represented as a single
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float, lower = upper. The contrast factor will be randomly picked between
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[1.0 - lower, 1.0 + upper].
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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Raise:
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ValueError: if lower bound is not between [0, 1], or upper bound is
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negative.
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"""
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def __init__(self, factor, seed=None, **kwargs):
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def __init__(self, factor, seed=None, name=None, **kwargs):
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self.factor = factor
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if isinstance(factor, (tuple, list)):
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self.lower = factor[0]
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@ -931,7 +976,7 @@ class RandomContrast(Layer):
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'got {}'.format(factor))
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self.seed = seed
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self.input_spec = InputSpec(ndim=4)
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super(RandomContrast, self).__init__(**kwargs)
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super(RandomContrast, self).__init__(name=name, **kwargs)
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def call(self, inputs, training=None):
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if training is None:
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@ -958,6 +1003,7 @@ class RandomContrast(Layer):
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export('keras.layers.experimental.preprocessing.RandomHeight')
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class RandomHeight(Layer):
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"""Randomly vary the height of a batch of images during training.
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@ -967,8 +1013,8 @@ class RandomHeight(Layer):
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By default, this layer is inactive during inference.
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Arguments:
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factor: A positive float (fraction of original height), or a tuple of
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size 2 representing lower and upper bound for resizing vertically. When
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factor: A positive float (fraction of original height), or a tuple of size 2
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representing lower and upper bound for resizing vertically. When
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represented as a single float, this value is used for both the upper and
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lower bound. For instance, `factor=(0.2, 0.3)` results in an output height
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varying in the range `[original + 20%, original + 30%]`. `factor=(-0.2,
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@ -979,17 +1025,21 @@ class RandomHeight(Layer):
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Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`,
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`gaussian`, `mitchellcubic`
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seed: Integer. Used to create a random seed.
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name: A string, the name of the layer.
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Input shape:
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4D tensor with shape:
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`(samples, height, width, channels)` (data_format='channels_last').
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4D tensor with shape: `(samples, height, width, channels)`
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(data_format='channels_last').
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Output shape:
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4D tensor with shape:
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`(samples, random_height, width, channels)`.
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4D tensor with shape: `(samples, random_height, width, channels)`.
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"""
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def __init__(self, factor, interpolation='bilinear', seed=None, **kwargs):
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def __init__(self,
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factor,
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interpolation='bilinear',
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seed=None,
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name=None,
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**kwargs):
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self.factor = factor
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if isinstance(factor, (tuple, list)):
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self.height_lower = -factor[0]
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@ -1004,7 +1054,7 @@ class RandomHeight(Layer):
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self.input_spec = InputSpec(ndim=4)
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self.seed = seed
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self._rng = make_generator(self.seed)
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super(RandomHeight, self).__init__(**kwargs)
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super(RandomHeight, self).__init__(name=name, **kwargs)
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def call(self, inputs, training=None):
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if training is None:
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@ -1046,6 +1096,7 @@ class RandomHeight(Layer):
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return dict(list(base_config.items()) + list(config.items()))
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|
||||
@keras_export('keras.layers.experimental.preprocessing.RandomWidth')
|
||||
class RandomWidth(Layer):
|
||||
"""Randomly vary the width of a batch of images during training.
|
||||
|
||||
@ -1067,6 +1118,7 @@ class RandomWidth(Layer):
|
||||
Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`,
|
||||
`gaussian`, `mitchellcubic`
|
||||
seed: Integer. Used to create a random seed.
|
||||
name: A string, the name of the layer.
|
||||
|
||||
Input shape:
|
||||
4D tensor with shape:
|
||||
@ -1077,7 +1129,12 @@ class RandomWidth(Layer):
|
||||
`(samples, random_height, width, channels)`.
|
||||
"""
|
||||
|
||||
def __init__(self, factor, interpolation='bilinear', seed=None, **kwargs):
|
||||
def __init__(self,
|
||||
factor,
|
||||
interpolation='bilinear',
|
||||
seed=None,
|
||||
name=None,
|
||||
**kwargs):
|
||||
self.factor = factor
|
||||
if isinstance(factor, (tuple, list)):
|
||||
self.width_lower = -factor[0]
|
||||
@ -1092,7 +1149,7 @@ class RandomWidth(Layer):
|
||||
self.input_spec = InputSpec(ndim=4)
|
||||
self.seed = seed
|
||||
self._rng = make_generator(self.seed)
|
||||
super(RandomWidth, self).__init__(**kwargs)
|
||||
super(RandomWidth, self).__init__(name=name, **kwargs)
|
||||
|
||||
def call(self, inputs, training=None):
|
||||
if training is None:
|
||||
|
||||
@ -290,11 +290,7 @@ class RescalingTest(keras_parameterized.TestCase):
|
||||
@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
|
||||
class RandomFlipTest(keras_parameterized.TestCase):
|
||||
|
||||
def _run_test(self,
|
||||
flip_horizontal,
|
||||
flip_vertical,
|
||||
expected_output=None,
|
||||
mock_random=None):
|
||||
def _run_test(self, mode, expected_output=None, mock_random=None):
|
||||
np.random.seed(1337)
|
||||
num_samples = 2
|
||||
orig_height = 5
|
||||
@ -306,24 +302,24 @@ class RandomFlipTest(keras_parameterized.TestCase):
|
||||
inp = np.random.random((num_samples, orig_height, orig_width, channels))
|
||||
if expected_output is None:
|
||||
expected_output = inp
|
||||
if flip_horizontal:
|
||||
if mode == 'horizontal' or mode == 'horizontal_and_vertical':
|
||||
expected_output = np.flip(expected_output, axis=1)
|
||||
if flip_vertical:
|
||||
if mode == 'vertical' or mode == 'horizontal_and_vertical':
|
||||
expected_output = np.flip(expected_output, axis=2)
|
||||
with test.mock.patch.object(
|
||||
random_ops, 'random_uniform', return_value=mock_random):
|
||||
with tf_test_util.use_gpu():
|
||||
layer = image_preprocessing.RandomFlip(flip_horizontal, flip_vertical)
|
||||
layer = image_preprocessing.RandomFlip(mode)
|
||||
actual_output = layer(inp, training=1)
|
||||
self.assertAllClose(expected_output, actual_output)
|
||||
|
||||
@parameterized.named_parameters(('random_flip_horizontal', True, False),
|
||||
('random_flip_vertical', False, True),
|
||||
('random_flip_both', True, True),
|
||||
('random_flip_neither', False, False))
|
||||
def test_random_flip(self, flip_horizontal, flip_vertical):
|
||||
@parameterized.named_parameters(
|
||||
('random_flip_horizontal', 'horizontal'),
|
||||
('random_flip_vertical', 'vertical'),
|
||||
('random_flip_both', 'horizontal_and_vertical'))
|
||||
def test_random_flip(self, mode):
|
||||
with CustomObjectScope({'RandomFlip': image_preprocessing.RandomFlip}):
|
||||
self._run_test(flip_horizontal, flip_vertical)
|
||||
self._run_test(mode)
|
||||
|
||||
def test_random_flip_horizontal_half(self):
|
||||
with CustomObjectScope({'RandomFlip': image_preprocessing.RandomFlip}):
|
||||
@ -333,7 +329,7 @@ class RandomFlipTest(keras_parameterized.TestCase):
|
||||
input_images = np.random.random((2, 5, 8, 3)).astype(np.float32)
|
||||
expected_output = input_images.copy()
|
||||
expected_output[0, :, :, :] = np.flip(input_images[0, :, :, :], axis=0)
|
||||
self._run_test(True, False, expected_output, mock_random)
|
||||
self._run_test('horizontal', expected_output, mock_random)
|
||||
|
||||
def test_random_flip_vertical_half(self):
|
||||
with CustomObjectScope({'RandomFlip': image_preprocessing.RandomFlip}):
|
||||
@ -343,14 +339,14 @@ class RandomFlipTest(keras_parameterized.TestCase):
|
||||
input_images = np.random.random((2, 5, 8, 3)).astype(np.float32)
|
||||
expected_output = input_images.copy()
|
||||
expected_output[0, :, :, :] = np.flip(input_images[0, :, :, :], axis=1)
|
||||
self._run_test(False, True, expected_output, mock_random)
|
||||
self._run_test('vertical', expected_output, mock_random)
|
||||
|
||||
def test_random_flip_inference(self):
|
||||
with CustomObjectScope({'RandomFlip': image_preprocessing.RandomFlip}):
|
||||
input_images = np.random.random((2, 5, 8, 3)).astype(np.float32)
|
||||
expected_output = input_images
|
||||
with tf_test_util.use_gpu():
|
||||
layer = image_preprocessing.RandomFlip(True, True)
|
||||
layer = image_preprocessing.RandomFlip()
|
||||
actual_output = layer(input_images, training=0)
|
||||
self.assertAllClose(expected_output, actual_output)
|
||||
|
||||
@ -369,7 +365,7 @@ class RandomFlipTest(keras_parameterized.TestCase):
|
||||
|
||||
@tf_test_util.run_v2_only
|
||||
def test_config_with_custom_name(self):
|
||||
layer = image_preprocessing.RandomFlip(5, 5, name='image_preproc')
|
||||
layer = image_preprocessing.RandomFlip(name='image_preproc')
|
||||
config = layer.get_config()
|
||||
layer_1 = image_preprocessing.RandomFlip.from_config(config)
|
||||
self.assertEqual(layer_1.name, layer.name)
|
||||
|
||||
@ -0,0 +1,218 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing.CenterCrop"
|
||||
tf_class {
|
||||
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.image_preprocessing.CenterCrop\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
|
||||
is_instance: "<type \'object\'>"
|
||||
member {
|
||||
name: "activity_regularizer"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "dtype"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "dynamic"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "inbound_nodes"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_mask"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_shape"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_spec"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "losses"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "metrics"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "name"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "name_scope"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "non_trainable_variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "non_trainable_weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "outbound_nodes"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output_mask"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output_shape"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "stateful"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "submodules"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "updates"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height\', \'width\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_metric"
|
||||
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_update"
|
||||
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_variable"
|
||||
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "add_weight"
|
||||
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "apply"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "build"
|
||||
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "call"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "compute_mask"
|
||||
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "compute_output_shape"
|
||||
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "compute_output_signature"
|
||||
argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "count_params"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "from_config"
|
||||
argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_config"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_mask_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_shape_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_losses_for"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_mask_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_shape_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_updates_for"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_weights"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "set_weights"
|
||||
argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "with_name_scope"
|
||||
argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,218 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing.RandomContrast"
|
||||
tf_class {
|
||||
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.image_preprocessing.RandomContrast\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
|
||||
is_instance: "<type \'object\'>"
|
||||
member {
|
||||
name: "activity_regularizer"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "dtype"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "dynamic"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "inbound_nodes"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_mask"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_shape"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_spec"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "losses"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "metrics"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "name"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "name_scope"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "non_trainable_variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "non_trainable_weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "outbound_nodes"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output_mask"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output_shape"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "stateful"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "submodules"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "updates"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'factor\', \'seed\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
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@ -0,0 +1,218 @@
|
||||
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||||
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||||
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||||
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|
||||
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|
||||
@ -0,0 +1,218 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing.RandomFlip"
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
member_method {
|
||||
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|
||||
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|
||||
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|
||||
member_method {
|
||||
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|
||||
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|
||||
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|
||||
member_method {
|
||||
name: "add_weight"
|
||||
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
|
||||
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@ -0,0 +1,218 @@
|
||||
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@ -0,0 +1,218 @@
|
||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,218 @@
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@ -0,0 +1,218 @@
|
||||
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@ -0,0 +1,218 @@
|
||||
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|
||||
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|
||||
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||||
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||||
@ -0,0 +1,218 @@
|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
member_method {
|
||||
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|
||||
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|
||||
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|
||||
member_method {
|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
@ -0,0 +1,218 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing.Resizing"
|
||||
tf_class {
|
||||
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.image_preprocessing.Resizing\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
@ -1,5 +1,9 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
@ -8,6 +12,46 @@ tf_module {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
@ -0,0 +1,218 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing.CenterCrop"
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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@ -0,0 +1,218 @@
|
||||
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|
||||
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|
||||
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||||
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@ -0,0 +1,218 @@
|
||||
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||||
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||||
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||||
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@ -0,0 +1,218 @@
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||||
@ -0,0 +1,218 @@
|
||||
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||||
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@ -0,0 +1,218 @@
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@ -0,0 +1,218 @@
|
||||
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||||
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@ -0,0 +1,218 @@
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@ -0,0 +1,218 @@
|
||||
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||||
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member {
|
||||
name: "stateful"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "submodules"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "updates"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'scale\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_metric"
|
||||
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_update"
|
||||
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_variable"
|
||||
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "add_weight"
|
||||
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "apply"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "build"
|
||||
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "call"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "compute_mask"
|
||||
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "compute_output_shape"
|
||||
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "compute_output_signature"
|
||||
argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "count_params"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "from_config"
|
||||
argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_config"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_mask_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_shape_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_losses_for"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_mask_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_shape_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_updates_for"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_weights"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "set_weights"
|
||||
argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "with_name_scope"
|
||||
argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,218 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing.Resizing"
|
||||
tf_class {
|
||||
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.image_preprocessing.Resizing\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
|
||||
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
|
||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
|
||||
is_instance: "<type \'object\'>"
|
||||
member {
|
||||
name: "activity_regularizer"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "dtype"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "dynamic"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "inbound_nodes"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_mask"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_shape"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "input_spec"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "losses"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "metrics"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "name"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "name_scope"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "non_trainable_variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "non_trainable_weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "outbound_nodes"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output_mask"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "output_shape"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "stateful"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "submodules"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "trainable_weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "updates"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "variables"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member {
|
||||
name: "weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'height\', \'width\', \'interpolation\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'bilinear\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_metric"
|
||||
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_update"
|
||||
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_variable"
|
||||
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "add_weight"
|
||||
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "apply"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "build"
|
||||
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "call"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "compute_mask"
|
||||
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "compute_output_shape"
|
||||
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "compute_output_signature"
|
||||
argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "count_params"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "from_config"
|
||||
argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_config"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_mask_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_input_shape_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_losses_for"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_mask_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_output_shape_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_updates_for"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get_weights"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "set_weights"
|
||||
argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "with_name_scope"
|
||||
argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
}
|
||||
@ -1,5 +1,9 @@
|
||||
path: "tensorflow.keras.layers.experimental.preprocessing"
|
||||
tf_module {
|
||||
member {
|
||||
name: "CenterCrop"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "Normalization"
|
||||
mtype: "<type \'type\'>"
|
||||
@ -8,6 +12,46 @@ tf_module {
|
||||
name: "PreprocessingLayer"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomContrast"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomCrop"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomFlip"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomHeight"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomRotation"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomTranslation"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomWidth"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RandomZoom"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "Rescaling"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "Resizing"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "TextVectorization"
|
||||
mtype: "<type \'type\'>"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user