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Keras API reference /
Keras Applications /
DenseNet

`DenseNet121`

function```
tf.keras.applications.DenseNet121(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
)
```

Instantiates the Densenet121 architecture.

**Reference**

- Densely Connected Convolutional Networks (CVPR 2017)

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`

.

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.

**Arguments**

**include_top**: whether to include the fully-connected layer at the top of the network.**weights**: one of`None`

(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.**input_tensor**: optional Keras tensor (i.e. output of`layers.Input()`

) to use as image input for the model.**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.

**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.

**Returns**

A Keras model instance.

`DenseNet169`

function```
tf.keras.applications.DenseNet169(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
)
```

Instantiates the Densenet169 architecture.

**Reference**

- Densely Connected Convolutional Networks (CVPR 2017)

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`

.

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.

**Arguments**

**include_top**: whether to include the fully-connected layer at the top of the network.**weights**: one of`None`

(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.**input_tensor**: optional Keras tensor (i.e. output of`layers.Input()`

) to use as image input for the model.**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.

**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.

**Returns**

A Keras model instance.

`DenseNet201`

function```
tf.keras.applications.DenseNet201(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
)
```

Instantiates the Densenet201 architecture.

**Reference**

- Densely Connected Convolutional Networks (CVPR 2017)

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`

.

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.

**Arguments**

**include_top**: whether to include the fully-connected layer at the top of the network.**weights**: one of`None`

(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.**input_tensor**: optional Keras tensor (i.e. output of`layers.Input()`

) to use as image input for the model.**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.

**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.

**Returns**

A Keras model instance.