1# Applications 2 3Keras Applications are deep learning models that are made available alongside pre-trained weights. 4These models can be used for prediction, feature extraction, and fine-tuning. 5 6Weights are downloaded automatically when instantiating a model. They are stored at `~/.keras/models/`. 7 8## Available models 9 10### Models for image classification with weights trained on ImageNet: 11 12- [Xception](#xception) 13- [VGG16](#vgg16) 14- [VGG19](#vgg19) 15- [ResNet, ResNetV2](#resnet) 16- [InceptionV3](#inceptionv3) 17- [InceptionResNetV2](#inceptionresnetv2) 18- [MobileNet](#mobilenet) 19- [MobileNetV2](#mobilenetv2) 20- [DenseNet](#densenet) 21- [NASNet](#nasnet) 22 23All of these architectures are compatible with all the backends (TensorFlow, Theano, and CNTK), and upon instantiation the models will be built according to the image data format set in your Keras configuration file at `~/.keras/keras.json`. For instance, if you have set `image_data_format=channels_last`, then any model loaded from this repository will get built according to the TensorFlow data format convention, "Height-Width-Depth". 24 25Note that: 26- For `Keras < 2.2.0`, The Xception model is only available for TensorFlow, due to its reliance on `SeparableConvolution` layers. 27- For `Keras < 2.1.5`, The MobileNet model is only available for TensorFlow, due to its reliance on `DepthwiseConvolution` layers. 28 29----- 30 31## Usage examples for image classification models 32 33### Classify ImageNet classes with ResNet50 34 35```python 36from keras.applications.resnet50 import ResNet50 37from keras.preprocessing import image 38from keras.applications.resnet50 import preprocess_input, decode_predictions 39import numpy as np 40 41model = ResNet50(weights='imagenet') 42 43img_path = 'elephant.jpg' 44img = image.load_img(img_path, target_size=(224, 224)) 45x = image.img_to_array(img) 46x = np.expand_dims(x, axis=0) 47x = preprocess_input(x) 48 49preds = model.predict(x) 50# decode the results into a list of tuples (class, description, probability) 51# (one such list for each sample in the batch) 52print('Predicted:', decode_predictions(preds, top=3)[0]) 53# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)] 54``` 55 56### Extract features with VGG16 57 58```python 59from keras.applications.vgg16 import VGG16 60from keras.preprocessing import image 61from keras.applications.vgg16 import preprocess_input 62import numpy as np 63 64model = VGG16(weights='imagenet', include_top=False) 65 66img_path = 'elephant.jpg' 67img = image.load_img(img_path, target_size=(224, 224)) 68x = image.img_to_array(img) 69x = np.expand_dims(x, axis=0) 70x = preprocess_input(x) 71 72features = model.predict(x) 73``` 74 75### Extract features from an arbitrary intermediate layer with VGG19 76 77```python 78from keras.applications.vgg19 import VGG19 79from keras.preprocessing import image 80from keras.applications.vgg19 import preprocess_input 81from keras.models import Model 82import numpy as np 83 84base_model = VGG19(weights='imagenet') 85model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output) 86 87img_path = 'elephant.jpg' 88img = image.load_img(img_path, target_size=(224, 224)) 89x = image.img_to_array(img) 90x = np.expand_dims(x, axis=0) 91x = preprocess_input(x) 92 93block4_pool_features = model.predict(x) 94``` 95 96### Fine-tune InceptionV3 on a new set of classes 97 98```python 99from keras.applications.inception_v3 import InceptionV3 100from keras.preprocessing import image 101from keras.models import Model 102from keras.layers import Dense, GlobalAveragePooling2D 103from keras import backend as K 104 105# create the base pre-trained model 106base_model = InceptionV3(weights='imagenet', include_top=False) 107 108# add a global spatial average pooling layer 109x = base_model.output 110x = GlobalAveragePooling2D()(x) 111# let's add a fully-connected layer 112x = Dense(1024, activation='relu')(x) 113# and a logistic layer -- let's say we have 200 classes 114predictions = Dense(200, activation='softmax')(x) 115 116# this is the model we will train 117model = Model(inputs=base_model.input, outputs=predictions) 118 119# first: train only the top layers (which were randomly initialized) 120# i.e. freeze all convolutional InceptionV3 layers 121for layer in base_model.layers: 122 layer.trainable = False 123 124# compile the model (should be done *after* setting layers to non-trainable) 125model.compile(optimizer='rmsprop', loss='categorical_crossentropy') 126 127# train the model on the new data for a few epochs 128model.fit_generator(...) 129 130# at this point, the top layers are well trained and we can start fine-tuning 131# convolutional layers from inception V3. We will freeze the bottom N layers 132# and train the remaining top layers. 133 134# let's visualize layer names and layer indices to see how many layers 135# we should freeze: 136for i, layer in enumerate(base_model.layers): 137 print(i, layer.name) 138 139# we chose to train the top 2 inception blocks, i.e. we will freeze 140# the first 249 layers and unfreeze the rest: 141for layer in model.layers[:249]: 142 layer.trainable = False 143for layer in model.layers[249:]: 144 layer.trainable = True 145 146# we need to recompile the model for these modifications to take effect 147# we use SGD with a low learning rate 148from keras.optimizers import SGD 149model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy') 150 151# we train our model again (this time fine-tuning the top 2 inception blocks 152# alongside the top Dense layers 153model.fit_generator(...) 154``` 155 156 157### Build InceptionV3 over a custom input tensor 158 159```python 160from keras.applications.inception_v3 import InceptionV3 161from keras.layers import Input 162 163# this could also be the output a different Keras model or layer 164input_tensor = Input(shape=(224, 224, 3)) # this assumes K.image_data_format() == 'channels_last' 165 166model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=True) 167``` 168 169----- 170 171# Documentation for individual models 172 173| Model | Size | Top-1 Accuracy | Top-5 Accuracy | Parameters | Depth | 174| ----- | ----: | --------------: | --------------: | ----------: | -----: | 175| [Xception](#xception) | 88 MB | 0.790 | 0.945 | 22,910,480 | 126 | 176| [VGG16](#vgg16) | 528 MB | 0.713 | 0.901 | 138,357,544 | 23 | 177| [VGG19](#vgg19) | 549 MB | 0.713 | 0.900 | 143,667,240 | 26 | 178| [ResNet50](#resnet) | 98 MB | 0.749 | 0.921 | 25,636,712 | - | 179| [ResNet101](#resnet) | 171 MB | 0.764 | 0.928 | 44,707,176 | - | 180| [ResNet152](#resnet) | 232 MB | 0.766 | 0.931 | 60,419,944 | - | 181| [ResNet50V2](#resnet) | 98 MB | 0.760 | 0.930 | 25,613,800 | - | 182| [ResNet101V2](#resnet) | 171 MB | 0.772 | 0.938 | 44,675,560 | - | 183| [ResNet152V2](#resnet) | 232 MB | 0.780 | 0.942 | 60,380,648 | - | 184| [InceptionV3](#inceptionv3) | 92 MB | 0.779 | 0.937 | 23,851,784 | 159 | 185| [InceptionResNetV2](#inceptionresnetv2) | 215 MB | 0.803 | 0.953 | 55,873,736 | 572 | 186| [MobileNet](#mobilenet) | 16 MB | 0.704 | 0.895 | 4,253,864 | 88 | 187| [MobileNetV2](#mobilenetv2) | 14 MB | 0.713 | 0.901 | 3,538,984 | 88 | 188| [DenseNet121](#densenet) | 33 MB | 0.750 | 0.923 | 8,062,504 | 121 | 189| [DenseNet169](#densenet) | 57 MB | 0.762 | 0.932 | 14,307,880 | 169 | 190| [DenseNet201](#densenet) | 80 MB | 0.773 | 0.936 | 20,242,984 | 201 | 191| [NASNetMobile](#nasnet) | 23 MB | 0.744 | 0.919 | 5,326,716 | - | 192| [NASNetLarge](#nasnet) | 343 MB | 0.825 | 0.960 | 88,949,818 | - | 193 194The top-1 and top-5 accuracy refers to the model's performance on the ImageNet validation dataset. 195 196Depth refers to the topological depth of the network. This includes activation layers, batch normalization layers etc. 197 198----- 199 200 201## Xception 202 203 204```python 205keras.applications.xception.Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 206``` 207 208Xception V1 model, with weights pre-trained on ImageNet. 209 210On ImageNet, this model gets to a top-1 validation accuracy of 0.790 211and a top-5 validation accuracy of 0.945. 212 213This model and can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 214 215The default input size for this model is 299x299. 216 217### Arguments 218 219- include_top: whether to include the fully-connected layer at the top of the network. 220- weights: one of `None` (random initialization) or `'imagenet'` (pre-training on ImageNet). 221- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 222- input_shape: optional shape tuple, only to be specified 223 if `include_top` is `False` (otherwise the input shape 224 has to be `(299, 299, 3)`. 225 It should have exactly 3 inputs channels, 226 and width and height should be no smaller than 71. 227 E.g. `(150, 150, 3)` would be one valid value. 228- pooling: Optional pooling mode for feature extraction 229 when `include_top` is `False`. 230 - `None` means that the output of the model will be 231 the 4D tensor output of the 232 last convolutional block. 233 - `'avg'` means that global average pooling 234 will be applied to the output of the 235 last convolutional block, and thus 236 the output of the model will be a 2D tensor. 237 - `'max'` means that global max pooling will 238 be applied. 239- classes: optional number of classes to classify images 240 into, only to be specified if `include_top` is `True`, and 241 if no `weights` argument is specified. 242 243### Returns 244 245A Keras `Model` instance. 246 247### References 248 249- [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357) 250 251### License 252 253These weights are trained by ourselves and are released under the MIT license. 254 255 256----- 257 258 259## VGG16 260 261```python 262keras.applications.vgg16.VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 263``` 264 265VGG16 model, with weights pre-trained on ImageNet. 266 267This model can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 268 269The default input size for this model is 224x224. 270 271### Arguments 272 273- include_top: whether to include the 3 fully-connected layers at the top of the network. 274- weights: one of `None` (random initialization) or `'imagenet'` (pre-training on ImageNet). 275- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 276- input_shape: optional shape tuple, only to be specified 277 if `include_top` is `False` (otherwise the input shape 278 has to be `(224, 224, 3)` (with `'channels_last'` data format) 279 or `(3, 224, 224)` (with `'channels_first'` data format). 280 It should have exactly 3 inputs channels, 281 and width and height should be no smaller than 32. 282 E.g. `(200, 200, 3)` would be one valid value. 283- pooling: Optional pooling mode for feature extraction 284 when `include_top` is `False`. 285 - `None` means that the output of the model will be 286 the 4D tensor output of the 287 last convolutional block. 288 - `'avg'` means that global average pooling 289 will be applied to the output of the 290 last convolutional block, and thus 291 the output of the model will be a 2D tensor. 292 - `'max'` means that global max pooling will 293 be applied. 294- classes: optional number of classes to classify images 295 into, only to be specified if `include_top` is `True`, and 296 if no `weights` argument is specified. 297 298### Returns 299 300A Keras `Model` instance. 301 302### References 303 304- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556): please cite this paper if you use the VGG models in your work. 305 306### License 307 308These weights are ported from the ones [released by VGG at Oxford](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) under the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/). 309 310----- 311 312## VGG19 313 314 315```python 316keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 317``` 318 319 320VGG19 model, with weights pre-trained on ImageNet. 321 322This model can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 323 324The default input size for this model is 224x224. 325 326### Arguments 327 328- include_top: whether to include the 3 fully-connected layers at the top of the network. 329- weights: one of `None` (random initialization) or `'imagenet'` (pre-training on ImageNet). 330- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 331- input_shape: optional shape tuple, only to be specified 332 if `include_top` is `False` (otherwise the input shape 333 has to be `(224, 224, 3)` (with `'channels_last'` data format) 334 or `(3, 224, 224)` (with `'channels_first'` data format). 335 It should have exactly 3 inputs channels, 336 and width and height should be no smaller than 32. 337 E.g. `(200, 200, 3)` would be one valid value. 338- pooling: Optional pooling mode for feature extraction 339 when `include_top` is `False`. 340 - `None` means that the output of the model will be 341 the 4D tensor output of the 342 last convolutional block. 343 - `'avg'` means that global average pooling 344 will be applied to the output of the 345 last convolutional block, and thus 346 the output of the model will be a 2D tensor. 347 - `'max'` means that global max pooling will 348 be applied. 349- classes: optional number of classes to classify images 350 into, only to be specified if `include_top` is `True`, and 351 if no `weights` argument is specified. 352 353### Returns 354 355A Keras `Model` instance. 356 357 358### References 359 360- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) 361 362### License 363 364These weights are ported from the ones [released by VGG at Oxford](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) under the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/). 365 366----- 367 368## ResNet 369 370 371```python 372keras.applications.resnet.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 373keras.applications.resnet.ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 374keras.applications.resnet.ResNet152(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 375keras.applications.resnet_v2.ResNet50V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 376keras.applications.resnet_v2.ResNet101V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 377keras.applications.resnet_v2.ResNet152V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 378``` 379 380 381ResNet, ResNetV2 models, with weights pre-trained on ImageNet. 382 383This model and can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 384 385The default input size for this model is 224x224. 386 387 388### Arguments 389 390- include_top: whether to include the fully-connected layer at the top of the network. 391- weights: one of `None` (random initialization) or `'imagenet'` (pre-training on ImageNet). 392- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 393- input_shape: optional shape tuple, only to be specified 394 if `include_top` is `False` (otherwise the input shape 395 has to be `(224, 224, 3)` (with `'channels_last'` data format) 396 or `(3, 224, 224)` (with `'channels_first'` data format). 397 It should have exactly 3 inputs channels, 398 and width and height should be no smaller than 32. 399 E.g. `(200, 200, 3)` would be one valid value. 400- pooling: Optional pooling mode for feature extraction 401 when `include_top` is `False`. 402 - `None` means that the output of the model will be 403 the 4D tensor output of the 404 last convolutional block. 405 - `'avg'` means that global average pooling 406 will be applied to the output of the 407 last convolutional block, and thus 408 the output of the model will be a 2D tensor. 409 - `'max'` means that global max pooling will 410 be applied. 411- classes: optional number of classes to classify images 412 into, only to be specified if `include_top` is `True`, and 413 if no `weights` argument is specified. 414 415### Returns 416 417A Keras `Model` instance. 418 419### References 420 421- `ResNet`: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) 422- `ResNetV2`: [Identity Mappings in Deep Residual Networks](https://arxiv.org/abs/1603.05027) 423 424### License 425 426These weights are ported from the following: 427 428- `ResNet`: [The original repository of Kaiming He](https://github.com/KaimingHe/deep-residual-networks) under the [MIT license](https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE). 429- `ResNetV2`: [Facebook](https://github.com/facebook/fb.resnet.torch) under the [BSD license](https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE). 430 431----- 432 433## InceptionV3 434 435 436```python 437keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 438``` 439 440Inception V3 model, with weights pre-trained on ImageNet. 441 442This model and can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 443 444The default input size for this model is 299x299. 445 446 447### Arguments 448 449- include_top: whether to include the fully-connected layer at the top of the network. 450- weights: one of `None` (random initialization) or `'imagenet'` (pre-training on ImageNet). 451- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 452- input_shape: optional shape tuple, only to be specified 453 if `include_top` is `False` (otherwise the input shape 454 has to be `(299, 299, 3)` (with `'channels_last'` data format) 455 or `(3, 299, 299)` (with `'channels_first'` data format). 456 It should have exactly 3 inputs channels, 457 and width and height should be no smaller than 75. 458 E.g. `(150, 150, 3)` would be one valid value. 459- pooling: Optional pooling mode for feature extraction 460 when `include_top` is `False`. 461 - `None` means that the output of the model will be 462 the 4D tensor output of the 463 last convolutional block. 464 - `'avg'` means that global average pooling 465 will be applied to the output of the 466 last convolutional block, and thus 467 the output of the model will be a 2D tensor. 468 - `'max'` means that global max pooling will 469 be applied. 470- classes: optional number of classes to classify images 471 into, only to be specified if `include_top` is `True`, and 472 if no `weights` argument is specified. 473 474### Returns 475 476A Keras `Model` instance. 477 478### References 479 480- [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) 481 482### License 483 484These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE). 485 486----- 487 488## InceptionResNetV2 489 490 491```python 492keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 493``` 494 495Inception-ResNet V2 model, with weights pre-trained on ImageNet. 496 497This model and can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 498 499The default input size for this model is 299x299. 500 501 502### Arguments 503 504- include_top: whether to include the fully-connected layer at the top of the network. 505- weights: one of `None` (random initialization) or `'imagenet'` (pre-training on ImageNet). 506- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 507- input_shape: optional shape tuple, only to be specified 508 if `include_top` is `False` (otherwise the input shape 509 has to be `(299, 299, 3)` (with `'channels_last'` data format) 510 or `(3, 299, 299)` (with `'channels_first'` data format). 511 It should have exactly 3 inputs channels, 512 and width and height should be no smaller than 75. 513 E.g. `(150, 150, 3)` would be one valid value. 514- pooling: Optional pooling mode for feature extraction 515 when `include_top` is `False`. 516 - `None` means that the output of the model will be 517 the 4D tensor output of the 518 last convolutional block. 519 - `'avg'` means that global average pooling 520 will be applied to the output of the 521 last convolutional block, and thus 522 the output of the model will be a 2D tensor. 523 - `'max'` means that global max pooling will 524 be applied. 525- classes: optional number of classes to classify images 526 into, only to be specified if `include_top` is `True`, and 527 if no `weights` argument is specified. 528 529### Returns 530 531A Keras `Model` instance. 532 533### References 534 535- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) 536 537### License 538 539These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE). 540 541----- 542 543## MobileNet 544 545 546```python 547keras.applications.mobilenet.MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000) 548``` 549 550MobileNet model, with weights pre-trained on ImageNet. 551 552This model and can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 553 554The default input size for this model is 224x224. 555 556### Arguments 557 558- input_shape: optional shape tuple, only to be specified 559 if `include_top` is `False` (otherwise the input shape 560 has to be `(224, 224, 3)` (with `'channels_last'` data format) 561 or `(3, 224, 224)` (with `'channels_first'` data format). 562 It should have exactly 3 inputs channels, 563 and width and height should be no smaller than 32. 564 E.g. `(200, 200, 3)` would be one valid value. 565- alpha: controls the width of the network. 566 - If `alpha` < 1.0, proportionally decreases the number 567 of filters in each layer. 568 - If `alpha` > 1.0, proportionally increases the number 569 of filters in each layer. 570 - If `alpha` = 1, default number of filters from the paper 571 are used at each layer. 572- depth_multiplier: depth multiplier for depthwise convolution 573 (also called the resolution multiplier) 574- dropout: dropout rate 575- include_top: whether to include the fully-connected 576 layer at the top of the network. 577- weights: `None` (random initialization) or 578 `'imagenet'` (ImageNet weights) 579- input_tensor: optional Keras tensor (i.e. output of 580 `layers.Input()`) 581 to use as image input for the model. 582- pooling: Optional pooling mode for feature extraction 583 when `include_top` is `False`. 584 - `None` means that the output of the model 585 will be the 4D tensor output of the 586 last convolutional block. 587 - `'avg'` means that global average pooling 588 will be applied to the output of the 589 last convolutional block, and thus 590 the output of the model will be a 591 2D tensor. 592 - `'max'` means that global max pooling will 593 be applied. 594- classes: optional number of classes to classify images 595 into, only to be specified if `include_top` is `True`, and 596 if no `weights` argument is specified. 597 598### Returns 599 600A Keras `Model` instance. 601 602### References 603 604- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf) 605 606### License 607 608These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE). 609 610----- 611 612## DenseNet 613 614 615```python 616keras.applications.densenet.DenseNet121(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 617keras.applications.densenet.DenseNet169(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 618keras.applications.densenet.DenseNet201(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000) 619``` 620 621DenseNet models, with weights pre-trained on ImageNet. 622 623This model and can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 624 625The default input size for this model is 224x224. 626 627### Arguments 628 629- blocks: numbers of building blocks for the four dense layers. 630- include_top: whether to include the fully-connected 631 layer at the top of the network. 632- weights: one of `None` (random initialization), 633 'imagenet' (pre-training on ImageNet), 634 or the path to the weights file to be loaded. 635- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) 636 to use as image input for the model. 637- input_shape: optional shape tuple, only to be specified 638 if `include_top` is False (otherwise the input shape 639 has to be `(224, 224, 3)` (with `'channels_last'` data format) 640 or `(3, 224, 224)` (with `'channels_first'` data format). 641 It should have exactly 3 inputs channels, 642 and width and height should be no smaller than 32. 643 E.g. `(200, 200, 3)` would be one valid value. 644- pooling: optional pooling mode for feature extraction 645 when `include_top` is `False`. 646 - `None` means that the output of the model will be 647 the 4D tensor output of the 648 last convolutional block. 649 - `avg` means that global average pooling 650 will be applied to the output of the 651 last convolutional block, and thus 652 the output of the model will be a 2D tensor. 653 - `max` means that global max pooling will 654 be applied. 655- classes: optional number of classes to classify images 656 into, only to be specified if `include_top` is True, and 657 if no `weights` argument is specified. 658 659### Returns 660 661A Keras model instance. 662 663### References 664 665- [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award) 666 667### License 668 669These weights are released under [the BSD 3-clause License](https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE). 670 671----- 672 673## NASNet 674 675 676```python 677keras.applications.nasnet.NASNetLarge(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000) 678keras.applications.nasnet.NASNetMobile(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000) 679``` 680 681Neural Architecture Search Network (NASNet) models, with weights pre-trained on ImageNet. 682 683The default input size for the NASNetLarge model is 331x331 and for the 684NASNetMobile model is 224x224. 685 686### Arguments 687 688- input_shape: optional shape tuple, only to be specified 689 if `include_top` is `False` (otherwise the input shape 690 has to be `(224, 224, 3)` (with `'channels_last'` data format) 691 or `(3, 224, 224)` (with `'channels_first'` data format) 692 for NASNetMobile or `(331, 331, 3)` (with `'channels_last'` 693 data format) or `(3, 331, 331)` (with `'channels_first'` 694 data format) for NASNetLarge. 695 It should have exactly 3 inputs channels, 696 and width and height should be no smaller than 32. 697 E.g. `(200, 200, 3)` would be one valid value. 698- include_top: whether to include the fully-connected 699 layer at the top of the network. 700- weights: `None` (random initialization) or 701 `'imagenet'` (ImageNet weights) 702- input_tensor: optional Keras tensor (i.e. output of 703 `layers.Input()`) 704 to use as image input for the model. 705- pooling: Optional pooling mode for feature extraction 706 when `include_top` is `False`. 707 - `None` means that the output of the model 708 will be the 4D tensor output of the 709 last convolutional block. 710 - `'avg'` means that global average pooling 711 will be applied to the output of the 712 last convolutional block, and thus 713 the output of the model will be a 714 2D tensor. 715 - `'max'` means that global max pooling will 716 be applied. 717- classes: optional number of classes to classify images 718 into, only to be specified if `include_top` is `True`, and 719 if no `weights` argument is specified. 720 721### Returns 722 723A Keras `Model` instance. 724 725### References 726 727- [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012) 728 729### License 730 731These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE). 732 733----- 734 735## MobileNetV2 736 737 738```python 739keras.applications.mobilenet_v2.MobileNetV2(input_shape=None, alpha=1.0, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000) 740``` 741 742MobileNetV2 model, with weights pre-trained on ImageNet. 743 744This model and can be built both with `'channels_first'` data format (channels, height, width) or `'channels_last'` data format (height, width, channels). 745 746The default input size for this model is 224x224. 747 748### Arguments 749 750- input_shape: optional shape tuple, to be specified if you would 751 like to use a model with an input img resolution that is not 752 (224, 224, 3). 753 It should have exactly 3 inputs channels (224, 224, 3). 754 You can also omit this option if you would like 755 to infer input_shape from an input_tensor. 756 If you choose to include both input_tensor and input_shape then 757 input_shape will be used if they match, if the shapes 758 do not match then we will throw an error. 759 E.g. `(160, 160, 3)` would be one valid value. 760- alpha: controls the width of the network. This is known as the 761 width multiplier in the MobileNetV2 paper. 762 - If `alpha` < 1.0, proportionally decreases the number 763 of filters in each layer. 764 - If `alpha` > 1.0, proportionally increases the number 765 of filters in each layer. 766 - If `alpha` = 1, default number of filters from the paper 767 are used at each layer. 768- include_top: whether to include the fully-connected 769 layer at the top of the network. 770- weights: one of `None` (random initialization), 771 'imagenet' (pre-training on ImageNet), 772 or the path to the weights file to be loaded. 773- input_tensor: optional Keras tensor (i.e. output of 774 `layers.Input()`) 775 to use as image input for the model. 776- pooling: Optional pooling mode for feature extraction 777 when `include_top` is `False`. 778 - `None` means that the output of the model 779 will be the 4D tensor output of the 780 last convolutional block. 781 - `'avg'` means that global average pooling 782 will be applied to the output of the 783 last convolutional block, and thus 784 the output of the model will be a 785 2D tensor. 786 - `'max'` means that global max pooling will 787 be applied. 788- classes: optional number of classes to classify images 789 into, only to be specified if `include_top` is True, and 790 if no `weights` argument is specified. 791 792### Returns 793 794A Keras model instance. 795 796### Raises 797 798ValueError: in case of invalid argument for `weights`, 799 or invalid input shape, alpha, 800 rows when weights='imagenet' 801 802### References 803 804- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) 805 806### License 807 808These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE). 809
