This is the second file.
The data file is located here, and just sits in the same folder.

import NeuralNetwork as nn
import numpy as np
#Data collection
with np.load('mnist.npz') as data:
training_images = data['training_images']
print(training_images.shape)
training_labels = data['training_labels']
print(training_labels.shape)

layer_sizes = (784,5,10)

net = nn.NeuralNetwork(layer_sizes)
prediction = net.predict(training_images[:1])
#changing this value to a single integer does not give any problems, passing in multiple values does however. I’m thinking I need to iterate on the outside of the function rather than on the inside.

print('Prediction Shape: ')
print(prediction.shape)

I have this project split into 2 files, this is the first.
I am definitely getting matrices with the wrong sizes.

import numpy as np

class NeuralNetwork:

def __init__(self, layer_sizes ):
	#layer_sizes = (784,5,10)
	weight_shapes = [(a,b) for a,b in zip(layer_sizes[1:],layer_sizes[:-1])]
	print(weight_shapes) #prints (5,784),(10,5)
	self.weights = [np.random.standard_normal(s)/s[0]**.5 for s in weight_shapes]
	self.biases = [np.zeros((s,1))for s in layer_sizes[1:]]
	
def predict(self, a):
	for w,b in zip(self.weights,self.biases):
		g = np.array([])
		for a in a:
			a = self.activation(np.dot(w, a))+b
			g = np.append(g,a)
			print(" Data Set Processed")
		print('Result of Activation Function')
	return g
		
@staticmethod
def activation(x):
	return 1/(1+ np.exp(-x))

Hey, I just recently got pythonista, and love the convenience of it. I’m not a super experienced coder, and I think I’m looking for an alternative to numpy’s matmul, which does not seem to be present in the numpy native to the app. The ‘@‘ operator present in base Python does not accept ndarray datatypes. I think I might also be having another issue with my code, completely separate, but any piece of the puzzle is a step forward.

The matrices I am multiplying are 2-D, so np.dot() almost worked, but it started giving me a completely different error. As I pass in multiple matrices, it passes one large 3-D matrix that no longer works for np.dot(). Something with my iterating must be off.