implement visualization for predictions

README.md

@@ -1,2 +1,55 @@
 # CoordConv
+![](https://img.shields.io/badge/pytorch-0.4.0-blue.svg) ![](https://img.shields.io/badge/python-3.6.5-brightgreen.svg)
 Pytorch implementation of CoordConv for N-D ConvLayers, and the experiments.
+
+Reference from the paper "An intriguing failing of convolutional neural networks and the CoordConv solution."
+
+Extends the CoordinateChannel concatenation from 2D to 1D and 3D tensors.
+
+# Requirements
+- pytorch 0.4.0
+- torchvision 0.2.1
+- torchsummary 1.3
+- sklearn 0.19.1
+
+# Usage
+```python
+from coordconv import CoordConv1d, CoordConv2d, CoordConv3d
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.coordconv = CoordConv2d(2, 32, 1, with_r=True)
+        self.conv1 = nn.Conv2d(32, 64, 1)
+        self.conv2 = nn.Conv2d(64, 64, 1)
+        self.conv3 = nn.Conv2d(64,  1, 1)
+        self.conv4 = nn.Conv2d( 1,  1, 1)
+
+    def forward(self, x):
+        x = self.coordconv(x)
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = self.conv4(x)
+        x = x.view(-1, 64*64)
+        return x
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+net = Net().to(device)
+```
+
+# Experiments
+Implement experiments from origin paper.
+
+## Coordinate Classification
+Use `experiments/generate_data.py` to generate `Uniform` and `Quadrant` datasets for Coordinate Classification task.
+
+Use `experiments/train_and_test.py` to train and test neural network model.
+
+### Images
+
+|Train|Test|Predictions|
+|:---:|:---:|:---:|
+|![](https://i.loli.net/2018/07/16/5b4c7db11abf9.png)|![](https://i.loli.net/2018/07/16/5b4c7dbd03169.png)|![](https://i.loli.net/2018/07/16/5b4c8d88a70a2.png)|
+
+