本文整理汇总了Python中chainer.Variable.append方法的典型用法代码示例。如果您正苦于以下问题：Python Variable.append方法的具体用法？Python Variable.append怎么用？Python Variable.append使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chainer.Variable的用法示例。
在下文中一共展示了Variable.append方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __call__
# 需要导入模块: from chainer import Variable [as 别名]
# 或者: from chainer.Variable import append [as 别名]
def __call__(self, x, t=None):
on_gpu = isinstance(x.data, cupy.ndarray)
self.cae_ones1.train = self.train
self.vgg2.train = self.train
roi_scale = self.cae_ones1.encode(x)
if t is None:
assert self.train is False
if on_gpu:
roi_scale_data = cuda.to_cpu(roi_scale.data)
rois_data = self.initial_roi * roi_scale_data
if on_gpu:
rois_data = cuda.to_cpu(rois_data)
x_data = cuda.to_cpu(x.data)
rois_data = rois_data.astype(int)
cropped = []
for i in xrange(len(x_data)):
roi = rois_data[i]
im = blob_to_im(x_data[i])
im = im[roi[0]:roi[2], roi[1]:roi[3]]
im = resize(im, (128, 128), preserve_range=True)
cropped.append(im_to_blob(im))
cropped = np.array(cropped, dtype=np.float32)
if on_gpu:
cropped = cuda.to_gpu(cropped)
cropped = Variable(cropped, volatile=not self.train)
self.vgg2(cropped)
self.y = self.vgg2.y
return self.y
# randomly change the param and estimate good parameter for the task
min_y = None
rands_shape = [self.learning_n_sample] + list(roi_scale.data.shape)
rands = self.learning_rate * (2 * np.random.random(rands_shape) - 1) + 1
rands[0] = np.ones(roi_scale.data.shape)
for i, rand in enumerate(rands):
if on_gpu:
roi_scale_data = cuda.to_cpu(roi_scale.data)
rois_data = rand * (self.initial_roi * roi_scale_data)
x_data = cuda.to_cpu(x.data)
skip = False
rois_data = rois_data.astype(int)
cropped = []
for j in xrange(len(x_data)):
roi = rois_data[j]
im = blob_to_im(x_data[j])
im = im[roi[0]:roi[2], roi[1]:roi[3]]
if im.size == 0:
skip = True
break
im = resize(im, (128, 128), preserve_range=True)
cropped.append(im_to_blob(im))
if skip:
continue
cropped = np.array(cropped)
if on_gpu:
cropped = cuda.to_gpu(cropped)
cropped = Variable(cropped, volatile=not self.train)
self.vgg2(cropped, t)
h = self.vgg2.y
loss = F.softmax_cross_entropy(h, t)
if min_y is None:
min_loss_data = float(loss.data)
min_y = h
min_loss = loss
min_rand = rand
min_rois = rois_data
elif min_loss_data > float(loss.data):
min_loss_data = float(loss.data)
min_y = h
min_loss = loss
min_rand = rand
min_rois = rois_data
if on_gpu:
min_rand = cuda.to_gpu(min_rand)
rois_data = min_rand * roi_scale.data
xp = cuda.get_array_module(rois_data)
rois_data = rois_data.astype(xp.float32)
rois = Variable(rois_data, volatile=not self.train)
loss1 = F.mean_squared_error(roi_scale, rois)
loss2 = min_loss
self.loss = loss1 + loss2
self.accuracy = F.accuracy(min_y, t)
return self.loss