def ascontiguousarray(a):
if not a.transposed:
return a
out = cudarray.empty_like(a)
n, m = a.shape
array_ops._transpose(a._data, m, n, out._data)
return out
def ascontiguousarray(a):
if not a.transposed:
return a
out = cudarray.empty_like(a)
n, m = a.shape
array_ops._transpose(a._data, m, n, out._data)
return out
def batches(self):
x1 = ca.empty(self.x_shape, dtype=self.x.dtype)
x2 = ca.empty_like(x1)
for start, stop in self._batch_slices():
if stop > start:
x1_np = self.x[start:stop]
x2_np = self.x2[start:stop]
else:
x1_np = np.concatenate((self.x[start:], self.x[:stop]))
x2_np = np.concatenate((self.x[start:], self.x[:stop]))
ca.copyto(x1, x1_np)
ca.copyto(x2, x2_np)
yield x1, x2
def batches(self):
x1 = ca.empty(self.x_shape, dtype=self.x.dtype)
x2 = ca.empty_like(x1)
for start, stop in self._batch_slices():
if stop > start:
x1_np = self.x[start:stop]
x2_np = self.x2[start:stop]
else:
x1_np = np.concatenate((self.x[start:], self.x[:stop]))
x2_np = np.concatenate((self.x[start:], self.x[:stop]))
ca.copyto(x1, x1_np)
ca.copyto(x2, x2_np)
yield x1, x2
def batches(self):
x1 = ca.empty(self.x_shape, dtype=self.x.dtype)
x2 = ca.empty_like(x1)
y = ca.empty(self.y_shape, dtype=self.y.dtype)
for start, stop in self._batch_slices():
if stop > start:
x1_np = self.x[start:stop]
x2_np = self.x2[start:stop]
y_np = self.y[start:stop]
else:
x1_np = np.concatenate((self.x[start:], self.x[:stop]))
x2_np = np.concatenate((self.x[start:], self.x[:stop]))
y_np = np.concatenate((self.y[start:], self.y[:stop]))
ca.copyto(x1, x1_np)
ca.copyto(x2, x2_np)
ca.copyto(y, y_np)
yield {'x1': x1, 'x2': x2, 'y': y}
def batches(self):
x1 = ca.empty(self.x_shape, dtype=self.x.dtype)
x2 = ca.empty_like(x1)
y = ca.empty(self.y_shape, dtype=self.y.dtype)
for start, stop in self._batch_slices():
if stop > start:
x1_np = self.x[start:stop]
x2_np = self.x2[start:stop]
y_np = self.y[start:stop]
else:
x1_np = np.concatenate((self.x[start:], self.x[:stop]))
x2_np = np.concatenate((self.x[start:], self.x[:stop]))
y_np = np.concatenate((self.y[start:], self.y[:stop]))
ca.copyto(x1, x1_np)
ca.copyto(x2, x2_np)
ca.copyto(y, y_np)
yield {'x1': x1, 'x2': x2, 'y': y}
def grad_array(self):
''' Returns the gradient array. '''
if self._grad is None:
self._grad = ca.empty_like(self.array)
return self._grad
def grad_array(self):
''' Returns the gradient array. '''
if self._tmp_grad_array is None:
self._tmp_grad_array = ca.empty_like(self.array)
return self._tmp_grad_array
