def test_gradient(self):
h = 0.01
slstm = SLSTM(
num_rows=4,
num_cols=3,
batch_size=10,
num_channels=3)
# generate data
inputs = randn(slstm.batch_size, slstm.num_rows, slstm.num_cols, slstm.num_channels)
# assume we want to minimize squared hidden unit activity
grad = slstm.backward(slstm.forward(inputs))
params = deepcopy(slstm.parameters())
params_copy = deepcopy(params)
# numerical gradient
grad_n = {}
for key in grad:
grad_n[key] = empty_like(grad[key])
for i in range(grad_n[key].size):
# increase parameter value
params_copy[key].ravel()[i] = params[key].ravel()[i] + h
slstm.set_parameters(params_copy)
loss_p = sum(square(slstm.forward(inputs))) / 2.
# decrease parameter value
params_copy[key].ravel()[i] = params[key].ravel()[i] - h
slstm.set_parameters(params_copy)
loss_n = sum(square(slstm.forward(inputs))) / 2.
# reset parameter value
params_copy[key].ravel()[i] = params[key].ravel()[i]
# estimate gradient
grad_n[key].ravel()[i] = (loss_p - loss_n) / (2. * h)
self.assertLess(max(abs(grad_n[key] - grad[key])), 1e-3)
def test_gradient(self):
h = 0.01
slstm = SLSTM(num_rows=4, num_cols=3, batch_size=10, num_channels=3)
# generate data
inputs = randn(slstm.batch_size, slstm.num_rows, slstm.num_cols,
slstm.num_channels)
# assume we want to minimize squared hidden unit activity
grad = slstm.backward(slstm.forward(inputs))
params = deepcopy(slstm.parameters())
params_copy = deepcopy(params)
# numerical gradient
grad_n = {}
for key in grad:
grad_n[key] = empty_like(grad[key])
for i in range(grad_n[key].size):
# increase parameter value
params_copy[key].ravel()[i] = params[key].ravel()[i] + h
slstm.set_parameters(params_copy)
loss_p = sum(square(slstm.forward(inputs))) / 2.
# decrease parameter value
params_copy[key].ravel()[i] = params[key].ravel()[i] - h
slstm.set_parameters(params_copy)
loss_n = sum(square(slstm.forward(inputs))) / 2.
# reset parameter value
params_copy[key].ravel()[i] = params[key].ravel()[i]
# estimate gradient
grad_n[key].ravel()[i] = (loss_p - loss_n) / (2. * h)
self.assertLess(max(abs(grad_n[key] - grad[key])), 1e-3)
