423 lines
16 KiB
Python
423 lines
16 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=invalid-name
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"""Inception V3 model for Keras.
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Reference:
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- [Rethinking the Inception Architecture for Computer Vision](
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http://arxiv.org/abs/1512.00567) (CVPR 2016)
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.keras import backend
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.lib.io import file_io
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from tensorflow.python.util.tf_export import keras_export
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WEIGHTS_PATH = (
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'https://storage.googleapis.com/tensorflow/keras-applications/'
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'inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5')
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WEIGHTS_PATH_NO_TOP = (
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'https://storage.googleapis.com/tensorflow/keras-applications/'
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'inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
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layers = VersionAwareLayers()
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@keras_export('keras.applications.inception_v3.InceptionV3',
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'keras.applications.InceptionV3')
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def InceptionV3(
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include_top=True,
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weights='imagenet',
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation='softmax'):
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"""Instantiates the Inception v3 architecture.
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Reference:
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- [Rethinking the Inception Architecture for Computer Vision](
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http://arxiv.org/abs/1512.00567) (CVPR 2016)
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Optionally loads weights pre-trained on ImageNet.
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Note that the data format convention used by the model is
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the one specified in the `tf.keras.backend.image_data_format()`.
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Note: each Keras Application expects a specific kind of input preprocessing.
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For InceptionV3, call `tf.keras.applications.inception_v3.preprocess_input`
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on your inputs before passing them to the model.
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Arguments:
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include_top: Boolean, whether to include the fully-connected
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layer at the top, as the last layer of the network. Default to `True`.
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weights: One of `None` (random initialization),
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`imagenet` (pre-training on ImageNet),
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or the path to the weights file to be loaded. Default to `imagenet`.
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input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
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to use as image input for the model. `input_tensor` is useful for sharing
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inputs between multiple different networks. Default to None.
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input_shape: Optional shape tuple, only to be specified
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if `include_top` is False (otherwise the input shape
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has to be `(299, 299, 3)` (with `channels_last` data format)
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or `(3, 299, 299)` (with `channels_first` data format).
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It should have exactly 3 inputs channels,
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and width and height should be no smaller than 75.
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E.g. `(150, 150, 3)` would be one valid value.
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`input_shape` will be ignored if the `input_tensor` is provided.
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pooling: Optional pooling mode for feature extraction
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when `include_top` is `False`.
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- `None` (default) means that the output of the model will be
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the 4D tensor output of the last convolutional block.
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- `avg` means that global average pooling
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will be applied to the output of the
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last convolutional block, and thus
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the output of the model will be a 2D tensor.
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- `max` means that global max pooling will be applied.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is True, and
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if no `weights` argument is specified. Default to 1000.
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classifier_activation: A `str` or callable. The activation function to use
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on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top" layer.
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Returns:
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A `keras.Model` instance.
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Raises:
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ValueError: in case of invalid argument for `weights`,
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or invalid input shape.
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ValueError: if `classifier_activation` is not `softmax` or `None` when
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using a pretrained top layer.
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"""
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if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
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raise ValueError('The `weights` argument should be either '
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'`None` (random initialization), `imagenet` '
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'(pre-training on ImageNet), '
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'or the path to the weights file to be loaded.')
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if weights == 'imagenet' and include_top and classes != 1000:
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raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
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' as true, `classes` should be 1000')
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# Determine proper input shape
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input_shape = imagenet_utils.obtain_input_shape(
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input_shape,
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default_size=299,
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min_size=75,
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data_format=backend.image_data_format(),
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require_flatten=include_top,
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weights=weights)
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if input_tensor is None:
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img_input = layers.Input(shape=input_shape)
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else:
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if not backend.is_keras_tensor(input_tensor):
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img_input = layers.Input(tensor=input_tensor, shape=input_shape)
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else:
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img_input = input_tensor
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if backend.image_data_format() == 'channels_first':
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channel_axis = 1
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else:
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channel_axis = 3
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x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')
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x = conv2d_bn(x, 32, 3, 3, padding='valid')
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x = conv2d_bn(x, 64, 3, 3)
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x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = conv2d_bn(x, 80, 1, 1, padding='valid')
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x = conv2d_bn(x, 192, 3, 3, padding='valid')
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x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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# mixed 0: 35 x 35 x 256
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branch1x1 = conv2d_bn(x, 64, 1, 1)
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branch5x5 = conv2d_bn(x, 48, 1, 1)
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branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding='same')(x)
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branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
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x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name='mixed0')
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# mixed 1: 35 x 35 x 288
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branch1x1 = conv2d_bn(x, 64, 1, 1)
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branch5x5 = conv2d_bn(x, 48, 1, 1)
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branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding='same')(x)
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branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
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x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name='mixed1')
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# mixed 2: 35 x 35 x 288
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branch1x1 = conv2d_bn(x, 64, 1, 1)
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branch5x5 = conv2d_bn(x, 48, 1, 1)
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branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding='same')(x)
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branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
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x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name='mixed2')
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# mixed 3: 17 x 17 x 768
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branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(
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branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')
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branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = layers.concatenate([branch3x3, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name='mixed3')
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# mixed 4: 17 x 17 x 768
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branch1x1 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(x, 128, 1, 1)
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branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
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branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
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branch7x7dbl = conv2d_bn(x, 128, 1, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding='same')(x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],
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axis=channel_axis,
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name='mixed4')
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# mixed 5, 6: 17 x 17 x 768
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for i in range(2):
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branch1x1 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(x, 160, 1, 1)
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branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
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branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
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branch7x7dbl = conv2d_bn(x, 160, 1, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch_pool = layers.AveragePooling2D((3, 3),
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strides=(1, 1),
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padding='same')(
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x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],
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axis=channel_axis,
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name='mixed' + str(5 + i))
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# mixed 7: 17 x 17 x 768
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branch1x1 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
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branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
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branch7x7dbl = conv2d_bn(x, 192, 1, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding='same')(x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],
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axis=channel_axis,
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name='mixed7')
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# mixed 8: 8 x 8 x 1280
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branch3x3 = conv2d_bn(x, 192, 1, 1)
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branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding='valid')
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branch7x7x3 = conv2d_bn(x, 192, 1, 1)
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branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
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branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
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branch7x7x3 = conv2d_bn(
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branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')
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branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = layers.concatenate([branch3x3, branch7x7x3, branch_pool],
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axis=channel_axis,
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name='mixed8')
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# mixed 9: 8 x 8 x 2048
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for i in range(2):
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branch1x1 = conv2d_bn(x, 320, 1, 1)
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branch3x3 = conv2d_bn(x, 384, 1, 1)
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branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
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branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
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branch3x3 = layers.concatenate([branch3x3_1, branch3x3_2],
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axis=channel_axis,
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name='mixed9_' + str(i))
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branch3x3dbl = conv2d_bn(x, 448, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
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branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
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branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
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branch3x3dbl = layers.concatenate([branch3x3dbl_1, branch3x3dbl_2],
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axis=channel_axis)
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branch_pool = layers.AveragePooling2D((3, 3),
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strides=(1, 1),
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padding='same')(
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x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate([branch1x1, branch3x3, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name='mixed' + str(9 + i))
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if include_top:
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# Classification block
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x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
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imagenet_utils.validate_activation(classifier_activation, weights)
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x = layers.Dense(classes, activation=classifier_activation,
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name='predictions')(x)
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else:
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if pooling == 'avg':
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x = layers.GlobalAveragePooling2D()(x)
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elif pooling == 'max':
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x = layers.GlobalMaxPooling2D()(x)
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# Ensure that the model takes into account
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# any potential predecessors of `input_tensor`.
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if input_tensor is not None:
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inputs = layer_utils.get_source_inputs(input_tensor)
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else:
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inputs = img_input
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# Create model.
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model = training.Model(inputs, x, name='inception_v3')
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# Load weights.
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if weights == 'imagenet':
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if include_top:
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weights_path = data_utils.get_file(
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'inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
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WEIGHTS_PATH,
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cache_subdir='models',
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file_hash='9a0d58056eeedaa3f26cb7ebd46da564')
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else:
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weights_path = data_utils.get_file(
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'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
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WEIGHTS_PATH_NO_TOP,
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cache_subdir='models',
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file_hash='bcbd6486424b2319ff4ef7d526e38f63')
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model.load_weights(weights_path)
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elif weights is not None:
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model.load_weights(weights)
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return model
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def conv2d_bn(x,
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filters,
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num_row,
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num_col,
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padding='same',
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strides=(1, 1),
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name=None):
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"""Utility function to apply conv + BN.
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Arguments:
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x: input tensor.
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filters: filters in `Conv2D`.
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num_row: height of the convolution kernel.
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num_col: width of the convolution kernel.
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padding: padding mode in `Conv2D`.
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strides: strides in `Conv2D`.
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name: name of the ops; will become `name + '_conv'`
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for the convolution and `name + '_bn'` for the
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batch norm layer.
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Returns:
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Output tensor after applying `Conv2D` and `BatchNormalization`.
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"""
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if name is not None:
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bn_name = name + '_bn'
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conv_name = name + '_conv'
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else:
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bn_name = None
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conv_name = None
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if backend.image_data_format() == 'channels_first':
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bn_axis = 1
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else:
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bn_axis = 3
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x = layers.Conv2D(
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filters, (num_row, num_col),
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strides=strides,
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padding=padding,
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use_bias=False,
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name=conv_name)(
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x)
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x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
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x = layers.Activation('relu', name=name)(x)
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return x
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@keras_export('keras.applications.inception_v3.preprocess_input')
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def preprocess_input(x, data_format=None):
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return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
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@keras_export('keras.applications.inception_v3.decode_predictions')
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def decode_predictions(preds, top=5):
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return imagenet_utils.decode_predictions(preds, top=top)
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preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
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mode='',
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ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
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error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
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decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
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