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README.md

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+# densenet169
+Implementation of DenseNet proposed in [Densely Connected Convolutional
+Networks](https://arxiv.org/abs/1608.06993)
+
+ Create a default models
+
+ ``` {.sourceCode .}
+ DenseNet.densenet121()
+ DenseNet.densenet161()
+ DenseNet.densenet169()
+ DenseNet.densenet201()
+ ```
+
+ Examples:
+
+  ``` {.sourceCode .}
+  # change activation
+  DenseNet.densenet121(activation = nn.SELU)
+  # change number of classes (default is 1000 )
+  DenseNet.densenet121(n_classes=100)
+  # pass a different block
+  DenseNet.densenet121(block=...)
+  # change the initial convolution
+  model = DenseNet.densenet121()
+  model.encoder.gate.conv1 = nn.Conv2d(3, 64, kernel_size=3)
+  # store each feature
+  x = torch.rand((1, 3, 224, 224))
+  model = DenseNet.densenet121()
+  # first call .features, this will activate the forward hooks and tells the model you'll like to get the features
+  model.encoder.features
+  model(torch.randn((1,3,224,224)))
+  # get the features from the encoder
+  features = model.encoder.features
+  print([x.shape for x in features])
+  # [torch.Size([1, 128, 28, 28]), torch.Size([1, 256, 14, 14]), torch.Size([1, 512, 7, 7]), torch.Size([1, 1024, 7, 7])]
+  ```
+
+