class TestLocalNeurons(object):
def __init__(self, shape=(100,100)):
mat = np.ones(shape)
self.matrix_neurons = LocalNeurons(mat, shape)
def test_operators(self):
logger.info("Testeando operadores...")
other = np.ones(self.matrix_neurons.shape)
# Return: LocalNeurons
res = self.matrix_neurons * other
assert np.array_equiv(res.matrix, np.ones(self.matrix_neurons.shape))
res = self.matrix_neurons / other
assert np.array_equiv(res.matrix, np.ones(self.matrix_neurons.shape))
res = self.matrix_neurons - other
assert np.array_equiv(res.matrix, np.zeros(self.matrix_neurons.shape))
res = self.matrix_neurons + other
assert np.array_equiv(res.matrix, np.ones(self.matrix_neurons.shape) * 2)
res **= 2 # LocalNeurons
assert np.array_equiv(res.matrix, np.ones(self.matrix_neurons.shape) * 4)
assert self.matrix_neurons == LocalNeurons(self.matrix_neurons.matrix, self.matrix_neurons.shape)
logger.info("OK")
def test_linalg(self):
logger.info("Testeando operaciones algebraicas...")
N = self.matrix_neurons.rows
other = np.array(range(N))
# Producto matricial
res = self.matrix_neurons.mul_array(other)
sum_range = sum(range(N))
assert np.array_equiv(res.matrix, np.array([sum_range]*N))
# Producto punto
#res = res.transpose().dot(range(N))
#assert res == (sum_range ** 2) * N
# Producto entre elementos
assert self.matrix_neurons.mul_elemwise(self.matrix_neurons) == self.matrix_neurons.collect()
# Suma entre arreglos
assert np.array_equiv(self.matrix_neurons.sum_array(self.matrix_neurons), self.matrix_neurons * 2)
assert self.matrix_neurons.sum() == self.matrix_neurons.count()
logger.info("OK")
def test_activation(self):
logger.info("Testeando la integración de funciones de activación...")
activation = 'Tanh'
fun = fun_activation[activation]
res = self.matrix_neurons.activation(fun)
fun_d = fun_activation_d[activation]
res_d = self.matrix_neurons.activation(fun_d)
assert np.array_equiv(res.matrix, fun(np.ones(self.matrix_neurons.shape)))
assert np.array_equiv(res_d.matrix, fun_d(np.ones(self.matrix_neurons.shape)))
# Test de softmax como funcion de activacion
N = self.matrix_neurons.rows
shape = (N,)
x = LocalNeurons(sc.parallelize(range(N)), shape) # Aprovecho para testear con RDD
res = x.softmax()
res = map(lambda e: e[0], res.matrix)
exp_x = np.exp(range(N))
y = exp_x / float(sum(exp_x))
assert np.allclose(res, y)
logger.info("OK")
def test_loss(self):
logger.info("Testeando la integración de funciones de activación...")
loss = 'MSE'
y = np.array(range(self.matrix_neurons.shape[0]))
fun = fun_loss[loss]
res = self.matrix_neurons.loss(fun, y)
fun_d = fun_loss_d[loss]
res_d = self.matrix_neurons.loss_d(fun_d, y)
assert res == fun(np.ones(self.matrix_neurons.shape), y)
assert np.allclose(res_d.matrix, fun_d(np.ones(self.matrix_neurons.shape), y))
logger.info("OK")
# TODO: test regularization stuff (i.e. l1, l2, dropout)
def test_all(self):
logger.info("Test de funcionalidades para la clase LocalNeurons")
self.test_operators()
self.test_linalg()
self.test_activation()
self.test_loss()
class TestLocalNeurons(object):
def __init__(self, shape=(100, 100)):
mat = np.ones(shape)
self.matrix_neurons = LocalNeurons(mat, shape)
def test_operators(self):
logger.info("Testeando operadores...")
other = np.ones(self.matrix_neurons.shape)
# Return: LocalNeurons
res = self.matrix_neurons * other
assert np.array_equiv(res.matrix, np.ones(self.matrix_neurons.shape))
res = self.matrix_neurons / other
assert np.array_equiv(res.matrix, np.ones(self.matrix_neurons.shape))
res = self.matrix_neurons - other
assert np.array_equiv(res.matrix, np.zeros(self.matrix_neurons.shape))
res = self.matrix_neurons + other
assert np.array_equiv(res.matrix,
np.ones(self.matrix_neurons.shape) * 2)
res **= 2 # LocalNeurons
assert np.array_equiv(res.matrix,
np.ones(self.matrix_neurons.shape) * 4)
assert self.matrix_neurons == LocalNeurons(self.matrix_neurons.matrix,
self.matrix_neurons.shape)
logger.info("OK")
def test_linalg(self):
logger.info("Testeando operaciones algebraicas...")
N = self.matrix_neurons.rows
other = np.array(range(N))
# Producto matricial
res = self.matrix_neurons.mul_array(other)
sum_range = sum(range(N))
assert np.array_equiv(res.matrix, np.array([sum_range] * N))
# Producto punto
#res = res.transpose().dot(range(N))
#assert res == (sum_range ** 2) * N
# Producto entre elementos
assert self.matrix_neurons.mul_elemwise(
self.matrix_neurons) == self.matrix_neurons.collect()
# Suma entre arreglos
assert np.array_equiv(
self.matrix_neurons.sum_array(self.matrix_neurons),
self.matrix_neurons * 2)
assert self.matrix_neurons.sum() == self.matrix_neurons.count()
logger.info("OK")
def test_activation(self):
logger.info("Testeando la integración de funciones de activación...")
activation = 'Tanh'
fun = fun_activation[activation]
res = self.matrix_neurons.activation(fun)
fun_d = fun_activation_d[activation]
res_d = self.matrix_neurons.activation(fun_d)
assert np.array_equiv(res.matrix,
fun(np.ones(self.matrix_neurons.shape)))
assert np.array_equiv(res_d.matrix,
fun_d(np.ones(self.matrix_neurons.shape)))
# Test de softmax como funcion de activacion
N = self.matrix_neurons.rows
shape = (N, )
x = LocalNeurons(sc.parallelize(range(N)),
shape) # Aprovecho para testear con RDD
res = x.softmax()
res = map(lambda e: e[0], res.matrix)
exp_x = np.exp(range(N))
y = exp_x / float(sum(exp_x))
assert np.allclose(res, y)
logger.info("OK")
def test_loss(self):
logger.info("Testeando la integración de funciones de activación...")
loss = 'MSE'
y = np.array(range(self.matrix_neurons.shape[0]))
fun = fun_loss[loss]
res = self.matrix_neurons.loss(fun, y)
fun_d = fun_loss_d[loss]
res_d = self.matrix_neurons.loss_d(fun_d, y)
assert res == fun(np.ones(self.matrix_neurons.shape), y)
assert np.allclose(res_d.matrix,
fun_d(np.ones(self.matrix_neurons.shape), y))
logger.info("OK")
# TODO: test regularization stuff (i.e. l1, l2, dropout)
def test_all(self):
logger.info("Test de funcionalidades para la clase LocalNeurons")
self.test_operators()
self.test_linalg()
self.test_activation()
self.test_loss()
