def grad_array(self):
''' Returns the gradient array. '''
if self._tmp_grad_array is None:
if self.array is None:
raise ValueError('Parameter not setup')
self._tmp_grad_array = ca.zeros_like(self.array)
return self._tmp_grad_array
def grad_array(self):
''' Returns the gradient array. '''
if self._tmp_grad_array is None:
if self.array is None:
raise ValueError('Parameter not setup')
self._tmp_grad_array = ca.zeros_like(self.array)
return self._tmp_grad_array
def __init__(self, node, in_port, out_port, in_arrays, grad_arrays):
self.node = node
self.in_port = in_port
self.out_port = out_port
self.in_arrays = {}
for k, array in in_arrays.items():
self.in_arrays[k] = ca.array(array)
self.grad_arrays = {}
for k, array in grad_arrays.items():
self.grad_arrays[k + '_grad'] = ca.zeros_like(array)
def __init__(self, node, in_port, out_port, in_arrays, grad_arrays):
self.node = node
self.in_port = in_port
self.out_port = out_port
self.in_arrays = {}
for k, array in in_arrays.items():
self.in_arrays[k] = ca.array(array)
self.grad_arrays = {}
for k, array in grad_arrays.items():
self.grad_arrays[k + '_grad'] = ca.zeros_like(array)
def _update(self):
# Forward propagation
next_x = self.x.array
x_feats = [None]*len(self.layers)
x_grams = [None]*len(self.layers)
for l, layer in enumerate(self.layers):
next_x = layer.fprop(next_x)
if self.subject_weights[l] > 0:
x_feats[l] = next_x
if self.style_weights[l] > 0:
x_feats[l] = next_x
x_grams[l] = gram_matrix(next_x)
# Backward propagation
grad = ca.zeros_like(next_x)
loss = ca.zeros(1)
for l, layer in reversed(list(enumerate(self.layers))):
if self.subject_weights[l] > 0:
diff = x_feats[l] - self.subject_feats[l]
norm = ca.sum(ca.fabs(diff)) + 1e-8
weight = float(self.subject_weights[l]) / norm
grad += diff * weight
loss += 0.5*weight*ca.sum(diff**2)
if self.style_weights[l] > 0:
diff = x_grams[l] - self.style_grams[l]
n_channels = diff.shape[0]
x_feat = ca.reshape(x_feats[l], (n_channels, -1))
style_grad = ca.reshape(ca.dot(diff, x_feat), x_feats[l].shape)
norm = ca.sum(ca.fabs(style_grad))
weight = float(self.style_weights[l]) / norm
style_grad *= weight
grad += style_grad
loss += 0.25*weight*ca.sum(diff**2)
grad = layer.bprop(grad)
if self.tv_weight > 0:
x = ca.reshape(self.x.array, (3, 1) + grad.shape[2:])
tv = self.tv_conv.fprop(x, self.tv_kernel)
tv *= self.tv_weight
grad -= ca.reshape(tv, grad.shape)
ca.copyto(self.x.grad_array, grad)
return loss
def _update(self):
# Forward propagation
next_x = self.x.array
x_feats = [None] * len(self.layers)
for l, layer in enumerate(self.layers):
next_x = layer.fprop(next_x)
if self.subject_weights[l] > 0 or self.style_weights[l] > 0:
x_feats[l] = next_x
# Backward propagation
grad = ca.zeros_like(next_x)
loss = ca.zeros(1)
for l, layer in reversed(list(enumerate(self.layers))):
if self.subject_weights[l] > 0:
diff = x_feats[l] - self.subject_feats[l]
norm = ca.sum(ca.fabs(diff)) + 1e-8
weight = float(self.subject_weights[l]) / norm
grad += diff * weight
loss += 0.5 * weight * ca.sum(diff**2)
if self.style_weights[l] > 0:
diff = gram_matrix(x_feats[l]) - self.style_grams[l]
n_channels = diff.shape[0]
x_feat = ca.reshape(x_feats[l], (n_channels, -1))
style_grad = ca.reshape(ca.dot(diff, x_feat), x_feats[l].shape)
norm = ca.sum(ca.fabs(style_grad))
weight = float(self.style_weights[l]) / norm
style_grad *= weight
grad += style_grad
loss += 0.25 * weight * ca.sum(diff**2)
grad = layer.bprop(grad)
if self.tv_weight > 0:
x = ca.reshape(self.x.array, (3, 1) + grad.shape[2:])
tv = self.tv_conv.fprop(x, self.tv_kernel)
tv *= self.tv_weight
grad -= ca.reshape(tv, grad.shape)
ca.copyto(self.x.grad_array, grad)
return loss
def test_dot():
a = np.random.normal(size=(5, 5))
b = np.random.normal(size=(5, 5))
c_np = np.dot(a, b)
a = ca.array(a)
b = ca.array(b)
c_ca = ca.dot(a, b)
print(np.allclose(c_np, np.array(c_ca)))
c_ca = ca.zeros_like(a)
ca.dot(a, b, c_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(5))
b_np = np.random.normal(size=(5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np)
c_ca = ca.dot(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(5, 5))
b_np = np.random.normal(size=(5, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np)
c_ca = ca.dot(a_ca.T, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(3, 4))
b_np = np.random.normal(size=(5, 4))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np.T)
c_ca = ca.dot(a_ca, b_ca.T)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4, 3))
b_np = np.random.normal(size=(4, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np)
c_ca = ca.dot(a_ca.T, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4, 3))
b_np = np.random.normal(size=(5, 4))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np.T)
c_ca = ca.dot(a_ca.T, b_ca.T)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4))
b_np = np.random.normal(size=(4, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np)
c_ca = ca.dot(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4, 5))
b_np = np.random.normal(size=(5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np)
c_ca = ca.dot(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4))
b_np = np.random.normal(size=(5, 4))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np.T)
c_ca = ca.dot(a_ca, b_ca.T)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(5, 4))
b_np = np.random.normal(size=(5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np)
c_ca = ca.dot(a_ca.T, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
def _setup(self, params, batch_size):
self.params = params
self.batch_size = batch_size
self.steps = [ca.zeros_like(p.grad_array) for p in params]
def grad_array(self):
''' Returns the gradient array. '''
if self._tmp_grad_array is None:
self._tmp_grad_array = ca.zeros_like(self.array)
return self._tmp_grad_array
def init_state(self, param):
m = ca.zeros_like(param.grad_array)
v = ca.zeros_like(param.grad_array)
t = np.zeros(1, dtype=int)
return m, v, t
def _setup(self, params, batch_size):
self.batch_size = batch_size
self.params = params
self.steps = [ca.zeros_like(param.grad_array) for param in params]
def _setup(self, params, batch_size):
self.params = params
self.batch_size = batch_size
self.steps = [ca.zeros_like(p.values) for p in params]
def init_state(self, param):
last_step = ca.zeros_like(param.grad_array)
return last_step
def bprop(self):
return {'x_grad': ca.zeros_like(self.array)}
def test_dot():
a = np.random.normal(size=(5, 5))
b = np.random.normal(size=(5, 5))
c_np = np.dot(a, b)
a = ca.array(a)
b = ca.array(b)
c_ca = ca.dot(a, b)
print(np.allclose(c_np, np.array(c_ca)))
c_ca = ca.zeros_like(a)
ca.dot(a, b, c_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(5))
b_np = np.random.normal(size=(5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np)
c_ca = ca.dot(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(5, 5))
b_np = np.random.normal(size=(5, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np)
c_ca = ca.dot(a_ca.T, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(3, 4))
b_np = np.random.normal(size=(5, 4))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np.T)
c_ca = ca.dot(a_ca, b_ca.T)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4, 3))
b_np = np.random.normal(size=(4, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np)
c_ca = ca.dot(a_ca.T, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4, 3))
b_np = np.random.normal(size=(5, 4))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np.T)
c_ca = ca.dot(a_ca.T, b_ca.T)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4))
b_np = np.random.normal(size=(4, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np)
c_ca = ca.dot(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4, 5))
b_np = np.random.normal(size=(5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np)
c_ca = ca.dot(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(4))
b_np = np.random.normal(size=(5, 4))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np, b_np.T)
c_ca = ca.dot(a_ca, b_ca.T)
print(np.allclose(c_np, np.array(c_ca)))
a_np = np.random.normal(size=(5, 4))
b_np = np.random.normal(size=(5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.dot(a_np.T, b_np)
c_ca = ca.dot(a_ca.T, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
def init_state(self, param):
m = ca.zeros_like(param.grad_array)
v = ca.zeros_like(param.grad_array)
t = np.zeros(1, dtype=int)
return m, v, t
def init_state(self, param):
last_step = ca.zeros_like(param.grad_array)
return last_step
def bprop(self):
return {"x_grad": ca.zeros_like(self.array)}
def grad_array(self):
''' Returns the gradient array. '''
if self._tmp_grad_array is None:
self._tmp_grad_array = ca.zeros_like(self.array)
return self._tmp_grad_array
def init_state(self, param):
mean_square = ca.zeros_like(param.grad_array)
return mean_square
