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March 27, 2018 18:39
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python used for inception import
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# -*- coding: utf-8 -*- | |
'''Inception V3 model for Keras. | |
Note that the ImageNet weights provided are from a model that had not fully converged. | |
Inception v3 should be able to reach 6.9% top-5 error, but our model | |
only gets to 7.8% (same as a fully-converged ResNet 50). | |
For comparison, VGG16 only gets to 9.9%, quite a bit worse. | |
Also, do note that the input image format for this model is different than for | |
other models (299x299 instead of 224x224), and that the input preprocessing function | |
is also different. | |
# Reference: | |
- [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) | |
''' | |
from __future__ import print_function | |
import numpy as np | |
import warnings | |
from keras.models import Model | |
from keras.layers import Flatten, Dense, Input, BatchNormalization, merge | |
from keras.layers import Convolution2D, MaxPooling2D, AveragePooling2D | |
from keras.preprocessing import image | |
from keras.utils.layer_utils import convert_all_kernels_in_model | |
from keras.utils.data_utils import get_file | |
from keras import backend as K | |
from imagenet_utils import decode_predictions | |
TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_th_dim_ordering_th_kernels.h5' | |
TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_tf_dim_ordering_tf_kernels.h5' | |
TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_th_dim_ordering_th_kernels_notop.h5' | |
TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5' | |
def conv2d_bn(x, nb_filter, nb_row, nb_col, | |
border_mode='same', subsample=(1, 1), | |
name=None): | |
'''Utility function to apply conv + BN. | |
''' | |
if name is not None: | |
bn_name = name + '_bn' | |
conv_name = name + '_conv' | |
else: | |
bn_name = None | |
conv_name = None | |
if K.image_dim_ordering() == 'th': | |
bn_axis = 1 | |
else: | |
bn_axis = 3 | |
x = Convolution2D(nb_filter, nb_row, nb_col, | |
subsample=subsample, | |
activation='relu', | |
border_mode=border_mode, | |
name=conv_name)(x) | |
x = BatchNormalization(axis=bn_axis, name=bn_name)(x) | |
return x | |
def InceptionV3(include_top=True, weights='imagenet', | |
input_tensor=None): | |
'''Instantiate the Inception v3 architecture, | |
optionally loading weights pre-trained | |
on ImageNet. Note that when using TensorFlow, | |
for best performance you should set | |
`image_dim_ordering="tf"` in your Keras config | |
at ~/.keras/keras.json. | |
The model and the weights are compatible with both | |
TensorFlow and Theano. The dimension ordering | |
convention used by the model is the one | |
specified in your Keras config file. | |
Note that the default input image size for this model is 299x299. | |
# Arguments | |
include_top: whether to include the 3 fully-connected | |
layers at the top of the network. | |
weights: one of `None` (random initialization) | |
or "imagenet" (pre-training on ImageNet). | |
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) | |
to use as image input for the model. | |
# Returns | |
A Keras model instance. | |
''' | |
if weights not in {'imagenet', None}: | |
raise ValueError('The `weights` argument should be either ' | |
'`None` (random initialization) or `imagenet` ' | |
'(pre-training on ImageNet).') | |
# Determine proper input shape | |
if K.image_dim_ordering() == 'th': | |
if include_top: | |
input_shape = (3, 299, 299) | |
else: | |
input_shape = (3, None, None) | |
else: | |
if include_top: | |
input_shape = (299, 299, 3) | |
else: | |
input_shape = (None, None, 3) | |
if input_tensor is None: | |
img_input = Input(shape=input_shape) | |
else: | |
if not K.is_keras_tensor(input_tensor): | |
img_input = Input(tensor=input_tensor) | |
else: | |
img_input = input_tensor | |
if K.image_dim_ordering() == 'th': | |
channel_axis = 1 | |
else: | |
channel_axis = 3 | |
x = conv2d_bn(img_input, 32, 3, 3, subsample=(2, 2), border_mode='valid') | |
x = conv2d_bn(x, 32, 3, 3, border_mode='valid') | |
x = conv2d_bn(x, 64, 3, 3) | |
x = MaxPooling2D((3, 3), strides=(2, 2))(x) | |
x = conv2d_bn(x, 80, 1, 1, border_mode='valid') | |
x = conv2d_bn(x, 192, 3, 3, border_mode='valid') | |
x = MaxPooling2D((3, 3), strides=(2, 2))(x) | |
# mixed 0, 1, 2: 35 x 35 x 256 | |
for i in range(3): | |
branch1x1 = conv2d_bn(x, 64, 1, 1) | |
branch5x5 = conv2d_bn(x, 48, 1, 1) | |
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) | |
branch3x3dbl = conv2d_bn(x, 64, 1, 1) | |
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) | |
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) | |
branch_pool = AveragePooling2D( | |
(3, 3), strides=(1, 1), border_mode='same')(x) | |
branch_pool = conv2d_bn(branch_pool, 32, 1, 1) | |
x = merge([branch1x1, branch5x5, branch3x3dbl, branch_pool], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed' + str(i)) | |
# mixed 3: 17 x 17 x 768 | |
branch3x3 = conv2d_bn(x, 384, 3, 3, subsample=(2, 2), border_mode='valid') | |
branch3x3dbl = conv2d_bn(x, 64, 1, 1) | |
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) | |
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, | |
subsample=(2, 2), border_mode='valid') | |
branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) | |
x = merge([branch3x3, branch3x3dbl, branch_pool], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed3') | |
# mixed 4: 17 x 17 x 768 | |
branch1x1 = conv2d_bn(x, 192, 1, 1) | |
branch7x7 = conv2d_bn(x, 128, 1, 1) | |
branch7x7 = conv2d_bn(branch7x7, 128, 1, 7) | |
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) | |
branch7x7dbl = conv2d_bn(x, 128, 1, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | |
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x) | |
branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | |
x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed4') | |
# mixed 5, 6: 17 x 17 x 768 | |
for i in range(2): | |
branch1x1 = conv2d_bn(x, 192, 1, 1) | |
branch7x7 = conv2d_bn(x, 160, 1, 1) | |
branch7x7 = conv2d_bn(branch7x7, 160, 1, 7) | |
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) | |
branch7x7dbl = conv2d_bn(x, 160, 1, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | |
branch_pool = AveragePooling2D( | |
(3, 3), strides=(1, 1), border_mode='same')(x) | |
branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | |
x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed' + str(5 + i)) | |
# mixed 7: 17 x 17 x 768 | |
branch1x1 = conv2d_bn(x, 192, 1, 1) | |
branch7x7 = conv2d_bn(x, 192, 1, 1) | |
branch7x7 = conv2d_bn(branch7x7, 192, 1, 7) | |
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) | |
branch7x7dbl = conv2d_bn(x, 160, 1, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) | |
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | |
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x) | |
branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | |
x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed7') | |
# mixed 8: 8 x 8 x 1280 | |
branch3x3 = conv2d_bn(x, 192, 1, 1) | |
branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, | |
subsample=(2, 2), border_mode='valid') | |
branch7x7x3 = conv2d_bn(x, 192, 1, 1) | |
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7) | |
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1) | |
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 3, 3, | |
subsample=(2, 2), border_mode='valid') | |
branch_pool = AveragePooling2D((3, 3), strides=(2, 2))(x) | |
x = merge([branch3x3, branch7x7x3, branch_pool], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed8') | |
# mixed 9: 8 x 8 x 2048 | |
for i in range(2): | |
branch1x1 = conv2d_bn(x, 320, 1, 1) | |
branch3x3 = conv2d_bn(x, 384, 1, 1) | |
branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3) | |
branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1) | |
branch3x3 = merge([branch3x3_1, branch3x3_2], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed9_' + str(i)) | |
branch3x3dbl = conv2d_bn(x, 448, 1, 1) | |
branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3) | |
branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3) | |
branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1) | |
branch3x3dbl = merge([branch3x3dbl_1, branch3x3dbl_2], | |
mode='concat', concat_axis=channel_axis) | |
branch_pool = AveragePooling2D( | |
(3, 3), strides=(1, 1), border_mode='same')(x) | |
branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | |
x = merge([branch1x1, branch3x3, branch3x3dbl, branch_pool], | |
mode='concat', concat_axis=channel_axis, | |
name='mixed' + str(9 + i)) | |
if include_top: | |
# Classification block | |
x = AveragePooling2D((8, 8), strides=(8, 8), name='avg_pool')(x) | |
x = Flatten(name='flatten')(x) | |
x = Dense(1000, activation='softmax', name='predictions')(x) | |
# Create model | |
model = Model(img_input, x) | |
# load weights | |
if weights == 'imagenet': | |
if K.image_dim_ordering() == 'th': | |
if include_top: | |
weights_path = get_file('inception_v3_weights_th_dim_ordering_th_kernels.h5', | |
TH_WEIGHTS_PATH, | |
cache_subdir='models', | |
md5_hash='b3baf3070cc4bf476d43a2ea61b0ca5f') | |
else: | |
weights_path = get_file('inception_v3_weights_th_dim_ordering_th_kernels_notop.h5', | |
TH_WEIGHTS_PATH_NO_TOP, | |
cache_subdir='models', | |
md5_hash='79aaa90ab4372b4593ba3df64e142f05') | |
model.load_weights(weights_path) | |
if K.backend() == 'tensorflow': | |
warnings.warn('You are using the TensorFlow backend, yet you ' | |
'are using the Theano ' | |
'image dimension ordering convention ' | |
'(`image_dim_ordering="th"`). ' | |
'For best performance, set ' | |
'`image_dim_ordering="tf"` in ' | |
'your Keras config ' | |
'at ~/.keras/keras.json.') | |
convert_all_kernels_in_model(model) | |
else: | |
if include_top: | |
weights_path = get_file('inception_v3_weights_tf_dim_ordering_tf_kernels.h5', | |
TF_WEIGHTS_PATH, | |
cache_subdir='models', | |
md5_hash='fe114b3ff2ea4bf891e9353d1bbfb32f') | |
else: | |
weights_path = get_file('inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5', | |
TF_WEIGHTS_PATH_NO_TOP, | |
cache_subdir='models', | |
md5_hash='2f3609166de1d967d1a481094754f691') | |
model.load_weights(weights_path) | |
if K.backend() == 'theano': | |
convert_all_kernels_in_model(model) | |
return model | |
def preprocess_input(x): | |
x /= 255. | |
x -= 0.5 | |
x *= 2. | |
return x | |
if __name__ == '__main__': | |
model = InceptionV3(include_top=True, weights='imagenet') | |
img_path = 'elephant.jpg' | |
img = image.load_img(img_path, target_size=(299, 299)) | |
x = image.img_to_array(img) | |
x = np.expand_dims(x, axis=0) | |
x = preprocess_input(x) | |
preds = model.predict(x) | |
## Save Model | |
model.save('inception_v3_complete.h5') | |
model.save_weights('inception_v3.h5') | |
json_string = model.to_json() | |
text_file = open("inception_V3_config", "w") | |
text_file.write(json_string) | |
text_file.close() | |
print('Predicted:', decode_predictions(preds)) |
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