from nn import NeuralNetwork
import numpy as np
from keras import datasets
from keras.utils import to_categorical
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train / 255
x_train = np.array(x_train)
x_train = [np.reshape(x, (784, 1)) for x in x_train]
y_train = np.array(to_categorical(y_train))
y_train = [np.reshape(y, (10, 1)) for y in y_train]
nn = NeuralNetwork([784, 32, 32, 10])
nn.SGD(np.array(list((zip(x_train, y_train)))), 10, 32)
ctr = 0
for i in range(0, 100):
res = nn.predict(x_train[i])
print("guessed: {}, correct: {}".format(np.argmax(res), np.argmax(y_train[i])))
if np.argmax(res) == np.argmax(y_train[i]):
ctr += 1
print("{}/{}".format(ctr, 100))
res = nn.predict(x_train[0])
