38. Neural Networks¶

This was adopted from the PyTorch Tutorials.
http://pytorch.org/tutorials/beginner/pytorch_with_examples.html

38.1. Neural Networks¶

Neural networks are the foundation of deep learning, which has revolutionized the

In the mathematical theory of artificial neural networks, the universal approximation theorem states[1] that a feed-forward network with a single hidden layer containing a finite number of neurons (i.e., a multilayer perceptron), can approximate continuous functions on compact subsets of Rn, under mild assumptions on the activation function.

38.1.1. Generate Fake Data¶

D_in is the number of dimensions of an input varaible.
D_out is the number of dimentions of an output variable.
Here we are learning some special “fake” data that represents the xor problem.
Here, the dv is 1 if either the first or second variable is

# -*- coding: utf-8 -*-
import numpy as np

#This is our independent and dependent variables. 
x = np.array([ [0,0,0],[1,0,0],[0,1,0],[0,0,0] ])
y = np.array([[0,1,1,0]]).T
print("Input data:\n",x,"\n Output data:\n",y)

Input data:
 [[0 0 0]
 [1 0 0]
 [0 1 0]
 [0 0 0]] 
 Output data:
 [[0]
 [1]
 [1]
 [0]]

38.1.2. A Simple Neural Network¶

Here we are going to build a neural network with 2 hidden layers.

np.random.seed(seed=83832)
#D_in is the number of input variables. 
#H is the hidden dimension.
#D_out is the number of dimensions for the output. 
D_in, H, D_out = 3, 2, 1

# Randomly initialize weights og out 2 hidden layer network.
w1 = np.random.randn(D_in, H)
w2 = np.random.randn(H, D_out)
bias = np.random.randn(H, 1)

38.1.3. Learn the Appropriate Weights via Backpropogation¶

Learning rate adjust how quickly the model will adjust parameters.

# -*- coding: utf-8 -*-

learning_rate = .01
for t in range(500):
    # Forward pass: compute predicted y
    h = x.dot(w1)

    #A relu is just the activation.
    h_relu = np.maximum(h, 0)
    y_pred = h_relu.dot(w2)

    # Compute and print loss
    loss = np.square(y_pred - y).sum()
    print(t, loss)

    # Backprop to compute gradients of w1 and w2 with respect to loss
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.T.dot(grad_y_pred)
    grad_h_relu = grad_y_pred.dot(w2.T)
    grad_h = grad_h_relu.copy()
    grad_h[h < 0] = 0
    grad_w1 = x.T.dot(grad_h)

    # Update weights
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2

10.6579261591
9.10203339893
7.92822558061
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5.03049559874e-05
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4.6502564322e-05
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3.82069322694e-05
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1.28063306584e-07
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1.18383356539e-07
1.13821320184e-07
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1.05217882363e-07
1.01163192419e-07
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9.35165473464e-08
8.99127815589e-08
8.64478909551e-08
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7.68339763517e-08
7.38730922756e-08
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6.82892301472e-08
6.56576275159e-08
6.31274366642e-08
6.06947495746e-08
5.83558088295e-08
5.61070018079e-08
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4.9867313135e-08
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4.43215434606e-08
4.26135627074e-08
4.09714009217e-08
3.93925216956e-08
3.7874486364e-08
3.64149502389e-08
3.50116589871e-08
3.3662445149e-08
3.23652247904e-08
3.11179942838e-08
2.99188272138e-08
2.87658714016e-08
2.76573460442e-08
2.65915389638e-08
2.55668039634e-08
2.4581558284e-08
2.363428016e-08
2.27235064688e-08
2.18478304708e-08
2.10058996367e-08
2.01964135585e-08
1.9418121941e-08
1.86698226703e-08
1.79503599575e-08
1.72586225532e-08
1.65935420314e-08
1.5954091139e-08
1.53392822092e-08
1.47481656362e-08
1.41798284082e-08
1.36333926976e-08
1.31080145044e-08
1.26028823535e-08
1.21172160408e-08
1.16502654283e-08
1.12013092852e-08

#CFully connected

pred = np.maximum(x.dot(w1),0).dot(w2)

print (pred, "\n", y)

[[ 0.        ]
 [ 0.99992661]
 [ 1.00007337]
 [ 0.        ]] 
 [[0]
 [1]
 [1]
 [0]]

38.1.4. Hidden Layers are Often Viewed as Unknown¶

Just a weighting matrix

#However
w1

array([[-0.20401151,  1.01377406],
       [-0.10186284,  1.01392285],
       [ 1.07856887,  0.01873049]])

w2

array([[ 0.49346731],
       [ 0.98634069]])

# Relu just removes the negative numbers.  
h_relu

array([[ 0.        ,  0.        ,  0.        ],
       [ 0.72108356,  0.        ,  0.        ],
       [ 0.72753913,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ]])

MGMT 4190/6560 Introduction to Machine Learning Applications @Rensselaer