def start():
nn = NN([400, 250, 10], ["tanh", "sigmoid"], cost_function="ce")
training_data = scipy.io.loadmat('../data/digits/ex4data1.mat')
X = training_data.get("X")
y = training_data.get("y")
targets = []
for j in y:
t = [0] * 10
t[j-1] = 1
targets.append(t)
nn.train(X, targets, batch_size=5, alpha=0.1, lamda=0.0, iterations=10)
print "training finished"
wrong = 0
right = 0
for jindex, x in enumerate(X):
p = nn.predict(x)
maxind = p.argmax() + 1
if maxind == y[jindex]:
right += 1
else:
wrong += 1
print "right: %s, wrong: %s" % (right, wrong)
acc = right / float(len(y))
return acc
def start():
nn = NN([400, 250, 10], ["tanh", "sigmoid"], cost_function="ce")
training_data = scipy.io.loadmat('../data/digits/ex4data1.mat')
X = training_data.get("X")
y = training_data.get("y")
targets = []
for j in y:
t = [0] * 10
t[j - 1] = 1
targets.append(t)
nn.train(X, targets, batch_size=5, alpha=0.1, lamda=0.0, iterations=10)
print "training finished"
wrong = 0
right = 0
for jindex, x in enumerate(X):
p = nn.predict(x)
maxind = p.argmax() + 1
if maxind == y[jindex]:
right += 1
else:
wrong += 1
print "right: %s, wrong: %s" % (right, wrong)
acc = right / float(len(y))
return acc
from neuron.neuralnet import NN
import time
if __name__ == "__main__":
start_time = time.time()
inputs = [[0, 0], [0, 1], [1, 0], [1, 1]]
targets = [[0], [1], [1], [0]]
nn = NN([2, 2, 1], ["sigmoid", "sigmoid"], cost_function="ce")
nn.train(inputs,
targets,
batch_size=4,
alpha=1,
lamda=0.0,
iterations=3000)
preds = []
for index, inp in enumerate(inputs):
pred = nn.predict(inp)
preds.append(pred)
print "%s -> %s" % (inp, pred)
end = time.time()
print "duration: %s" % (end - start_time)
assert preds[0] < 0.01 and preds[1] > 0.99 and preds[2] > 0.99 and preds[
3] < 0.01
from neuron.neuralnet import NN
import time
if __name__ == "__main__":
start_time = time.time()
inputs = [[0, 0, 1], [1, 0, 0], [0, 1, 0]]
targets = [[1, 0, 0], [0, 0, 1], [0, 1, 0]]
nn = NN([3, 3, 5, 3], ["sigmoid", "tanh", "softmax"], cost_function="softmax_ce")
nn.train(inputs, targets, batch_size=4, alpha=1, lamda=0.0, iterations=1000)
preds = []
for index, inp in enumerate(inputs):
pred = nn.predict(inp)
preds.append(pred)
print "%s -> %s" % (inp, pred)
end = time.time()
e1 = sum(abs(preds[0] - targets[0]))
e2 = sum(abs(preds[1] - targets[1]))
e3 = sum(abs(preds[2] - targets[2]))
print "%s %s %s" % (e1, e2, e3)
print "duration: %s" % (end - start_time)
assert e1 < 0.006 and e2 < 0.006 and e3 < 006
