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input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
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pooling: Optional pooling mode for feature extraction when include_top is False.
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None means that the output of the model will be the 4D tensor output of the last convolutional block.
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avg means that global average pooling will be applied to the output of the last convolutional block, and thus 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 into, only to be specified if include_top is True, and if no weights argument is specified.
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Returns
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A Keras model instance.
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DenseNet169 function
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tf.keras.applications.DenseNet169(
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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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)
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Instantiates the Densenet169 architecture.
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Reference
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Densely Connected Convolutional Networks (CVPR 2017)
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Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.
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Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call tf.keras.applications.densenet.preprocess_input on your inputs before passing them to the model.
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Arguments
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include_top: whether to include the fully-connected layer at the top of the network.
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weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), 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()) to use as image input for the model.
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input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
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pooling: Optional pooling mode for feature extraction when include_top is False.
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None means that the output of the model will be the 4D tensor output of the last convolutional block.
|
avg means that global average pooling will be applied to the output of the last convolutional block, and thus 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 into, only to be specified if include_top is True, and if no weights argument is specified.
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Returns
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A Keras model instance.
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DenseNet201 function
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tf.keras.applications.DenseNet201(
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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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)
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Instantiates the Densenet201 architecture.
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Reference
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Densely Connected Convolutional Networks (CVPR 2017)
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Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.
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Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call tf.keras.applications.densenet.preprocess_input on your inputs before passing them to the model.
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Arguments
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include_top: whether to include the fully-connected layer at the top of the network.
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weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), 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()) to use as image input for the model.
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input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
|
pooling: Optional pooling mode for feature extraction when include_top is False.
|
None means that the output of the model will be the 4D tensor output of the last convolutional block.
|
avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
|
max means that global max pooling will be applied.
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classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.
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Returns
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A Keras model instance.
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VGG16 and VGG19
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VGG16 function
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tf.keras.applications.VGG16(
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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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)
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Instantiates the VGG16 model.
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Reference
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Very Deep Convolutional Networks for Large-Scale Image Recognition (ICLR 2015)
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For image classification use cases, see this page for detailed examples.
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For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
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The default input size for this model is 224x224.
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Note: each Keras Application expects a specific kind of input preprocessing. For VGG16, call tf.keras.applications.vgg16.preprocess_input on your inputs before passing them to the model. vgg16.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.
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