return (inputTrain, outputTrain, inputTest, outputTest)
def run(dataset, arhitecture, learning_rate, eval_every, stop):
input_size = dataset["train_imgs"][0].shape
nn = FeedForward(input_size, arhitecture)
print(nn.to_string())
return train_nn(nn, dataset, learning_rate, eval_every, stop)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--learning_rate", type = float, default = 0.001,
help="Learning rate")
parser.add_argument("--eval_every", type = int, default = 2000,
help="Learning rate")
args = parser.parse_args()
#dataset = load_mnist()
dataset = load_cifrar()
input_size = dataset["train_imgs"][0].shape
nn = FeedForward(input_size, [(CONV, (6, 28, 28), 5, 1), (RELU, -1), (MAX_POOLING, (6, 14, 14)), (CONV, (16, 10, 10), 5, 1), (RELU, -1), (MAX_POOLING, (16, 5, 5)),
(LINEARIZE, -1), (FULLY_CONNECTED, 120), (FULLY_CONNECTED, 84), (FULLY_CONNECTED, 10) ,(SOFTMAX, -1)])
#nn = FeedForward(input_size, [(LINEARIZE, -1), (FULLY_CONNECTED, 300), (TANH, -1), (FULLY_CONNECTED, 100), (TANH, -1), (FULLY_CONNECTED, 10), (SOFTMAX, -1)])
print(nn.to_string())
train_nn(nn, dataset, args.learning_rate, args.eval_every, 10000)
#!/usr/bin/python
from pylab import plot, show, arange, legend, xlabel, ylabel
from train import run
from data_loader_mnist import load_mnist
from data_loader_cifrar import load_cifrar
from feed_forward import LINEARIZE, FULLY_CONNECTED, TANH, SOFTMAX
xlabel('Numarul de imagini de antrenare')
ylabel('Acuratete')
mnist = load_mnist()
cifrar = load_cifrar()
arhitecture = [(LINEARIZE, -1), (FULLY_CONNECTED, 300), (TANH, -1), (FULLY_CONNECTED, 100), (TANH, -1), (FULLY_CONNECTED, 10), (SOFTMAX, -1)]
(inputTrainC, outputTrainC, inputTestC, outputTestC) = run(cifrar, arhitecture, 0.002, 4000, 40000)
x = inputTrainC
y = outputTrainC
plot(x, y, color='green', label='CIFAR_TRAIN')
x = inputTestC
y = outputTestC
plot(x, y, color='blue', label='CIFAR_TEST')
legend(prop={'size':6})
show()