From cef04aeb63f5c50af0226e077727c6eb399c6b27 Mon Sep 17 00:00:00 2001
From: elias <eliasbayonajr@gmail.com>
Date: Sun, 20 Oct 2019 00:38:10 +0200
Subject: [PATCH 1/2] Added Basic Neural Network

This is a basic implementation of a neural network using pytorch. The network accepts an input of 784 and generate a final output of 10. eg(classifying 28x28 -> 784 images of 10 digits)
---
 ML Cookbook/BasicNeuralNet.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 ML Cookbook/BasicNeuralNet.py

diff --git a/ML Cookbook/BasicNeuralNet.py b/ML Cookbook/BasicNeuralNet.py
new file mode 100644
index 0000000..92e6d2f
--- /dev/null
+++ b/ML Cookbook/BasicNeuralNet.py	
@@ -0,0 +1,23 @@
+from torch import nn
+
+class BasicNeuralNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        
+        # Inputs to hidden layer linear transformation
+        self.hidden = nn.Linear(784, 256)
+        # Output layer, 10 units 
+        self.output = nn.Linear(256, 10)
+        
+        # Define sigmoid activation and softmax output 
+        self.sigmoid = nn.Sigmoid()
+        self.softmax = nn.Softmax(dim=1)
+        
+    def forward(self, x):
+        # Pass the input tensor through each of the operations
+        x = self.hidden(x)
+        x = self.sigmoid(x)
+        x = self.output(x)
+        x = self.softmax(x)
+        
+        return x
\ No newline at end of file

From 2b127f14863bd89d114353c8321f5e3ad2c2f336 Mon Sep 17 00:00:00 2001
From: elias <eliasbayonajr@gmail.com>
Date: Sun, 20 Oct 2019 00:58:48 +0200
Subject: [PATCH 2/2] Added basic CNN

Basic implimentation of a CNN using pytorch.
1 converlutional layer:
-> input depth 3
-> output depth 16
-> kernel size 3
-> padding 1

1 maxpool layer
---
 ML Cookbook/CNNs/BasicCNN.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 ML Cookbook/CNNs/BasicCNN.py

diff --git a/ML Cookbook/CNNs/BasicCNN.py b/ML Cookbook/CNNs/BasicCNN.py
new file mode 100644
index 0000000..178ba57
--- /dev/null
+++ b/ML Cookbook/CNNs/BasicCNN.py	
@@ -0,0 +1,16 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+# CNN architecture definition
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        # convolutional layer
+        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
+        # max pooling layer
+        self.pool = nn.MaxPool2d(2, 2)
+
+    def forward(self, x):
+        # add sequence of convolutional and max pooling layers
+        x = self.pool(F.relu(self.conv1(x)))
+        return x
\ No newline at end of file