229 lines
8.4 KiB
Python
229 lines
8.4 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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# pylint: disable=unused-import
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"""VGG16 model for Keras.
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# Reference
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- [Very Deep Convolutional Networks for Large-Scale Image
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Recognition](https://arxiv.org/abs/1409.1556)
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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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import os
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape
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from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
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from tensorflow.python.keras.applications.imagenet_utils import preprocess_input
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from tensorflow.python.keras.layers import Conv2D
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from tensorflow.python.keras.layers import Dense
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from tensorflow.python.keras.layers import Flatten
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from tensorflow.python.keras.layers import GlobalAveragePooling2D
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from tensorflow.python.keras.layers import GlobalMaxPooling2D
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from tensorflow.python.keras.layers import Input
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from tensorflow.python.keras.layers import MaxPooling2D
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from tensorflow.python.keras.models import Model
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.keras.utils.data_utils import get_file
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.util.tf_export import tf_export
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WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
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WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
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@tf_export('keras.applications.VGG16', 'keras.applications.vgg16.VGG16')
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def VGG16(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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"""Instantiates the VGG16 architecture.
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Optionally loads weights pre-trained
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on ImageNet. Note that when using TensorFlow,
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for best performance you should set
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`image_data_format='channels_last'` in your Keras config
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at ~/.keras/keras.json.
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The model and the weights are compatible with both
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TensorFlow and Theano. The data format
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convention used by the model is the one
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specified in your Keras config file.
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Arguments:
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include_top: whether to include the 3 fully-connected
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layers at the top of the network.
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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.
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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.
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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 `(224, 224, 3)` (with `channels_last` data format)
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or `(3, 224, 224)` (with `channels_first` data format).
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It should have exactly 3 input channels,
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and width and height should be no smaller than 48.
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E.g. `(200, 200, 3)` would be one valid value.
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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` means that the output of the model will be
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the 4D tensor output of the
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last convolutional layer.
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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 layer, 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
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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.
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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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"""
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if not (weights in {'imagenet', None} or os.path.exists(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 = _obtain_input_shape(
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input_shape,
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default_size=224,
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min_size=48,
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data_format=K.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 = Input(shape=input_shape)
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else:
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if not K.is_keras_tensor(input_tensor):
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img_input = Input(tensor=input_tensor, shape=input_shape)
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else:
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img_input = input_tensor
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# Block 1
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x = Conv2D(
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64, (3, 3), activation='relu', padding='same', name='block1_conv1')(
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img_input)
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x = Conv2D(
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64, (3, 3), activation='relu', padding='same', name='block1_conv2')(
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x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
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# Block 2
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x = Conv2D(
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128, (3, 3), activation='relu', padding='same', name='block2_conv1')(
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x)
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x = Conv2D(
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128, (3, 3), activation='relu', padding='same', name='block2_conv2')(
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x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
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# Block 3
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x = Conv2D(
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256, (3, 3), activation='relu', padding='same', name='block3_conv1')(
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x)
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x = Conv2D(
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256, (3, 3), activation='relu', padding='same', name='block3_conv2')(
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x)
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x = Conv2D(
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256, (3, 3), activation='relu', padding='same', name='block3_conv3')(
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x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
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# Block 4
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x = Conv2D(
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512, (3, 3), activation='relu', padding='same', name='block4_conv1')(
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x)
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x = Conv2D(
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512, (3, 3), activation='relu', padding='same', name='block4_conv2')(
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x)
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x = Conv2D(
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512, (3, 3), activation='relu', padding='same', name='block4_conv3')(
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x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
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# Block 5
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x = Conv2D(
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512, (3, 3), activation='relu', padding='same', name='block5_conv1')(
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x)
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x = Conv2D(
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512, (3, 3), activation='relu', padding='same', name='block5_conv2')(
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x)
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x = Conv2D(
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512, (3, 3), activation='relu', padding='same', name='block5_conv3')(
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x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
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if include_top:
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# Classification block
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x = Flatten(name='flatten')(x)
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x = Dense(4096, activation='relu', name='fc1')(x)
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x = Dense(4096, activation='relu', name='fc2')(x)
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x = Dense(classes, activation='softmax', name='predictions')(x)
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else:
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if pooling == 'avg':
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x = GlobalAveragePooling2D()(x)
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elif pooling == 'max':
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x = 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 = Model(inputs, x, name='vgg16')
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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 = get_file(
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'vgg16_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='64373286793e3c8b2b4e3219cbf3544b')
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else:
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weights_path = get_file(
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'vgg16_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='6d6bbae143d832006294945121d1f1fc')
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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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