I'm trying to make an ANN from scratch and I've run into a big problem...
from math import *
from random import random
import numpy as np
training = np.array([[0,0], [0,1], [1,0], [1,1]])
trainingLabels = np.array([0, 1, 1, 0])
testing = np.array([[0,1], [0,0], [1,0], [1,1], [0,1]])
testingLabels = np.array([1, 0, 1, 0, 1])
hiddenLayers = [2, 2]
outputs = 1
weights = []
weights.append(np.random.rand(len(training), hiddenLayers[0]) - 0.5)
if len(hiddenLayers) > 1:
for i in range(len(hiddenLayers) - 1):
weights.append(np.random.rand(hiddenLayers[i+1], hiddenLayers[i]) - 0.5)
weights.append(np.random.rand(outputs, hiddenLayers[-1]) - 0.5)
biases = []
biases.append(np.random.rand(1, hiddenLayers[0]) - 0.5)
if len(hiddenLayers) > 1:
for i in range(len(hiddenLayers) - 1):
biases.append(np.random.rand(1, hiddenLayers[i + 1]) - 0.5)
biases.append(np.random.rand(1, outputs) - 0.5)
forward = []
forward.append(np.dot(weights[0], training) + biases[0])
This is the output I got from running that:
Traceback (most recent call last):
File "c:\Users\kaila\Desktop\ANN\ANN.py", line 80, in <module>
forward.append(np.dot(weights[0], training) + biases[0])
File "<__array_function__ internals>", line 5, in dot
ValueError: shapes (4,2) and (4,2) not aligned: 2 (dim 1) != 4 (dim 0)
I tried printing different things to see if anything was wrong, but everything was as I intended.
print(np.shape(training))
print(np.shape(weights[0]))
print(training, "\n\n")
print(weights[0])
This returned:
(4, 2)
(4, 2)
[[0 0]
[0 1]
[1 0]
[1 1]]
[[ 0.31642805 0.23512315]
[ 0.07491602 -0.27518716]
[ 0.47279068 0.12803371]
[ 0.48695467 -0.07876347]]
Any ideas? Even if it isn't the full fix, anything is appreciated.
I think that I understood your problem.
Remember that when taking the dot product of two vectors with equal shape, the one on the left should be transposed, so:
np.dot(weights[0].T, training)
Should work, with .T transposing the tensor.