diff --git a/build/lib/squeeze_and_excitation/__init__.py b/build/lib/squeeze_and_excitation/__init__.py
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diff --git a/build/lib/squeeze_and_excitation/squeeze_and_excitation.py b/build/lib/squeeze_and_excitation/squeeze_and_excitation.py
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+"""
+Squeeze and Excitation Module
+*****************************
+
+Collection of squeeze and excitation classes where each can be inserted as a block into a neural network architechture
+
+    1. `Channel Squeeze and Excitation <https://arxiv.org/abs/1709.01507>`_
+    2. `Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_
+    3. `Channel and Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_
+
+"""
+
+from enum import Enum
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ChannelSELayer(nn.Module):
+    """
+    Re-implementation of Squeeze-and-Excitation (SE) block described in:
+        *Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
+
+    """
+
+    def __init__(self, num_channels, reduction_ratio=2):
+        """
+
+        :param num_channels: No of input channels
+        :param reduction_ratio: By how much should the num_channels should be reduced
+        """
+        super(ChannelSELayer, self).__init__()
+        num_channels_reduced = num_channels // reduction_ratio
+        self.reduction_ratio = reduction_ratio
+        self.fc1 = nn.Linear(num_channels, num_channels_reduced, bias=True)
+        self.fc2 = nn.Linear(num_channels_reduced, num_channels, bias=True)
+        self.relu = nn.ReLU()
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, input_tensor):
+        """
+
+        :param input_tensor: X, shape = (batch_size, num_channels, H, W)
+        :return: output tensor
+        """
+        batch_size, num_channels, H, W = input_tensor.size()
+        # Average along each channel
+        squeeze_tensor = input_tensor.view(batch_size, num_channels, -1).mean(dim=2)
+
+        # channel excitation
+        fc_out_1 = self.relu(self.fc1(squeeze_tensor))
+        fc_out_2 = self.sigmoid(self.fc2(fc_out_1))
+
+        a, b = squeeze_tensor.size()
+        output_tensor = torch.mul(input_tensor, fc_out_2.view(a, b, 1, 1))
+        return output_tensor
+
+
+class SpatialSELayer(nn.Module):
+    """
+    Re-implementation of SE block -- squeezing spatially and exciting channel-wise described in:
+        *Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 2018*
+    """
+
+    def __init__(self, num_channels):
+        """
+
+        :param num_channels: No of input channels
+        """
+        super(SpatialSELayer, self).__init__()
+        self.conv = nn.Conv2d(num_channels, 1, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, input_tensor, weights=None):
+        """
+
+        :param weights: weights for few shot learning
+        :param input_tensor: X, shape = (batch_size, num_channels, H, W)
+        :return: output_tensor
+        """
+        # spatial squeeze
+        batch_size, channel, a, b = input_tensor.size()
+
+        if weights:
+            weights = weights.view(1, channel, 1, 1)
+            out = F.conv2d(input_tensor, weights)
+        else:
+            out = self.conv(input_tensor)
+        squeeze_tensor = self.sigmoid(out)
+
+        # spatial excitation
+        output_tensor = torch.mul(input_tensor, squeeze_tensor.view(batch_size, 1, a, b))
+
+        return output_tensor
+
+
+class ChannelSpatialSELayer(nn.Module):
+    """
+    Re-implementation of concurrent spatial and channel squeeze & excitation:
+        *Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, arXiv:1803.02579*
+    """
+
+    def __init__(self, num_channels, reduction_ratio=2):
+        """
+
+        :param num_channels: No of input channels
+        :param reduction_ratio: By how much should the num_channels should be reduced
+        """
+        super(ChannelSpatialSELayer, self).__init__()
+        self.cSE = ChannelSELayer(num_channels, reduction_ratio)
+        self.sSE = SpatialSELayer(num_channels)
+
+    def forward(self, input_tensor):
+        """
+
+        :param input_tensor: X, shape = (batch_size, num_channels, H, W)
+        :return: output_tensor
+        """
+        output_tensor = torch.max(self.cSE(input_tensor), self.sSE(input_tensor))
+        return output_tensor
+
+
+class SELayer(Enum):
+    """
+    Enum restricting the type of SE Blockes available. So that type checking can be adding when adding these blockes to
+    a neural network::
+
+        if self.se_block_type == se.SELayer.CSE.value:
+            self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
+
+        elif self.se_block_type == se.SELayer.SSE.value:
+            self.SELayer = se.SpatialSELayer(params['num_filters'])
+
+        elif self.se_block_type == se.SELayer.CSSE.value:
+            self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
+    """
+    NONE = 'NONE'
+    CSE = 'CSE'
+    SSE = 'SSE'
+    CSSE = 'CSSE'
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