`DenseNet121`

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

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 TF-Keras config at `~/.keras/keras.json`

.

Note: each TF-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 TF-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.**classifier_activation**: A`str`

or callable. The activation function to use on the "top" layer. Ignored unless`include_top=True`

. Set`classifier_activation=None`

to return the logits of the "top" layer. When loading pretrained weights,`classifier_activation`

can only be`None`

or`"softmax"`

.

**Returns**

A TF-Keras model instance.

`DenseNet169`

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

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 TF-Keras config at `~/.keras/keras.json`

.

Note: each TF-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 TF-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.**classifier_activation**: A`str`

or callable. The activation function to use on the "top" layer. Ignored unless`include_top=True`

. Set`classifier_activation=None`

to return the logits of the "top" layer. When loading pretrained weights,`classifier_activation`

can only be`None`

or`"softmax"`

.

**Returns**

A TF-Keras model instance.

`DenseNet201`

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

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 TF-Keras config at `~/.keras/keras.json`

.

Note: each TF-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 TF-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.**classifier_activation**: A`str`

or callable. The activation function to use on the "top" layer. Ignored unless`include_top=True`

. Set`classifier_activation=None`

to return the logits of the "top" layer. When loading pretrained weights,`classifier_activation`

can only be`None`

or`"softmax"`

.

**Returns**

A TF-Keras model instance.