'target': [0]
}]
def drawPrediction():
resolution = 10
cols = rows = int(side / resolution)
for i in range(cols):
for j in range(rows):
x1 = i / cols
x2 = j / rows
color = int(brain.predict([x1, x2])[0][0] * 255)
rect = pygame.draw.rect(
screen, (color, color, color),
(i * resolution, j * resolution, resolution, resolution))
pygame.display.flip()
while running:
event = pygame.event.poll()
if event.type == pygame.QUIT:
brain.saveState('XOR.state.json')
running = 0
if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:
brain = NeuralNetwork(2, 4, 1)
for _ in range(1000):
data = np.random.choice(training_data)
brain.train(data['input'], data['target'])
drawPrediction()
import pandas
import numpy
from NN import NeuralNetwork
numbers = pandas.read_csv('processed_train.csv')
#print(numbers.iloc[:,1:])
brain = NeuralNetwork(784, 100, 10, 0.3)
total = numbers.shape[0]
for index, row in numbers.iterrows():
target = row[1]
inputs = row[2:]
targets = numpy.zeros(10) + 0.01
targets[int(target)] = 0.99
brain.train(inputs, targets)
print(str(index) + '/' + str(total))
brain.saveState('digit-recognizer.state.json')