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247 lines
10 KiB
Python
247 lines
10 KiB
Python
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# -*- coding: utf-8 -*-
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'''ResNet50 model for Keras.
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# Reference:
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- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
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Adapted from code contributed by BigMoyan.
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'''
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from __future__ import print_function
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import numpy as np
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import warnings
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from keras.layers import merge, Input
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from keras.layers import Dense, Activation, Flatten
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from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D
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from keras.layers import BatchNormalization
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from keras.models import Model
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from keras.preprocessing import image
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import keras.backend as K
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from keras.utils.layer_utils import convert_all_kernels_in_model
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from keras.utils.data_utils import get_file
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TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_th_dim_ordering_th_kernels.h5'
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TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
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TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_th_dim_ordering_th_kernels_notop.h5'
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TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
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def identity_block(input_tensor, kernel_size, filters, stage, block):
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'''The identity_block is the block that has no conv layer at shortcut
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# Arguments
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input_tensor: input tensor
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kernel_size: defualt 3, the kernel size of middle conv layer at main path
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filters: list of integers, the nb_filters of 3 conv layer at main path
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stage: integer, current stage label, used for generating layer names
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block: 'a','b'..., current block label, used for generating layer names
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'''
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nb_filter1, nb_filter2, nb_filter3 = filters
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if K.image_dim_ordering() == 'tf':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
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x = Activation('relu')(x)
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x = Convolution2D(nb_filter2, kernel_size, kernel_size,
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border_mode='same', name=conv_name_base + '2b')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
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x = Activation('relu')(x)
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x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
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x = merge([x, input_tensor], mode='sum')
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x = Activation('relu')(x)
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return x
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def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
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'''conv_block is the block that has a conv layer at shortcut
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# Arguments
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input_tensor: input tensor
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kernel_size: defualt 3, the kernel size of middle conv layer at main path
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filters: list of integers, the nb_filters of 3 conv layer at main path
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stage: integer, current stage label, used for generating layer names
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block: 'a','b'..., current block label, used for generating layer names
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Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
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And the shortcut should have subsample=(2,2) as well
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'''
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nb_filter1, nb_filter2, nb_filter3 = filters
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if K.image_dim_ordering() == 'tf':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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x = Convolution2D(nb_filter1, 1, 1, subsample=strides,
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name=conv_name_base + '2a')(input_tensor)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
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x = Activation('relu')(x)
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x = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same',
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name=conv_name_base + '2b')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
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x = Activation('relu')(x)
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x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
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shortcut = Convolution2D(nb_filter3, 1, 1, subsample=strides,
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name=conv_name_base + '1')(input_tensor)
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shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
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x = merge([x, shortcut], mode='sum')
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x = Activation('relu')(x)
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return x
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def ResNet50(include_top=True, weights='imagenet',
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input_tensor=None):
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'''Instantiate the ResNet50 architecture,
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optionally loading 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_dim_ordering="tf"` 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 dimension ordering
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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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or "imagenet" (pre-training on ImageNet).
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input_tensor: optional Keras tensor (i.e. xput of `layers.Input()`)
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to use as image input for the model.
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# Returns
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A Keras model instance.
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'''
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if weights not in {'imagenet', None}:
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raise ValueError('The `weights` argument should be either '
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'`None` (random initialization) or `imagenet` '
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'(pre-training on ImageNet).')
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# Determine proper input shape
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if K.image_dim_ordering() == 'th':
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if include_top:
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input_shape = (3, 224, 224)
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else:
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input_shape = (3, None, None)
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else:
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if include_top:
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input_shape = (224, 224, 3)
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else:
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input_shape = (None, None, 3)
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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)
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else:
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img_input = input_tensor
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if K.image_dim_ordering() == 'tf':
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bn_axis = 3
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else:
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bn_axis = 1
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x = ZeroPadding2D((3, 3))(img_input)
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x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1')(x)
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x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
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x = Activation('relu')(x)
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x = MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
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x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
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x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
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x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
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x = AveragePooling2D((7, 7), name='avg_pool')(x)
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if include_top:
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x = Flatten()(x)
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x = Dense(1000, activation='softmax', name='fc1000')(x)
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model = Model(img_input, x)
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# load weights
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if weights == 'imagenet':
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print('K.image_dim_ordering:', K.image_dim_ordering())
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if K.image_dim_ordering() == 'th':
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if include_top:
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weights_path = get_file('resnet50_weights_th_dim_ordering_th_kernels.h5',
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TH_WEIGHTS_PATH,
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cache_subdir='models')
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else:
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weights_path = get_file('resnet50_weights_th_dim_ordering_th_kernels_notop.h5',
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TH_WEIGHTS_PATH_NO_TOP,
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cache_subdir='models')
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model.load_weights(weights_path)
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if K.backend() == 'tensorflow':
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warnings.warn('You are using the TensorFlow backend, yet you '
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'are using the Theano '
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'image dimension ordering convention '
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'(`image_dim_ordering="th"`). '
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'For best performance, set '
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'`image_dim_ordering="tf"` in '
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'your Keras config '
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'at ~/.keras/keras.json.')
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convert_all_kernels_in_model(model)
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else:
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if include_top:
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weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',
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TF_WEIGHTS_PATH,
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cache_subdir='models')
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else:
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weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
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TF_WEIGHTS_PATH_NO_TOP,
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cache_subdir='models')
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model.load_weights(weights_path)
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if K.backend() == 'theano':
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convert_all_kernels_in_model(model)
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return model
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if __name__ == '__main__':
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model = ResNet50(include_top=True, weights='imagenet')
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img_path = 'elephant.jpg'
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img = image.load_img(img_path, target_size=(224, 224))
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x = image.img_to_array(img)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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print('Input image shape:', x.shape)
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preds = model.predict(x)
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print('Predicted:', decode_predictions(preds))
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