def main():
lsizes = np.array(([784], [50], [30], [10]))
learning_rate = 0.4
bias_learning_rate = 0.4
n = ANN.ArtificialNeuralNet(lsizes, ANN.squared_error, learning_rate,
bias_learning_rate)
training_data, validation_data, testing_data = mnist_loader.load_data_wrapper(
)
nc = ANN.NetworkChecker()
if nc.check(n, training_data[0][0], training_data[0][1].T.ravel()):
n.train(training_data)
percentage, correct, incorrect = n.test(
testing_data=testing_data, output_size=training_data[0][1].size)
print percentage, "%"
print "Correct [1-9]", correct
print "Incorrect [1-9]", incorrect
# n.save_weights(percentage, lsizes, learning_rate)
user_input = raw_input(
"Please select image file to detect. Or type q to quit.\n")
while True:
if user_input == "q":
break
elif user_input == "draw":
PygamePaint.draw()
raw_image = Image.open("screenshot.png").convert('L')
else:
try:
raw_image = Image.open(user_input).convert('L')
except:
print "Not a valid file. Please Try again\n"
user_input = raw_input(
"Please select image file to detect. Or type q to quit.\n")
continue
scaled_image = raw_image.resize((28, 28))
scaled_image.save("scaled.png")
image = np.reshape(np.asarray(scaled_image), (1, 784))
image = np.true_divide(image, 255)
vals = n.forward(image)
print "I see a {}".format(np.argmax(vals))
print vals
user_input = raw_input(
"Please select image file to detect. Or type q to quit.\n")
