def __init__(self, tag1, tag2, tag3):
super(SummaryDemoTag, self).__init__()
self.s = P.ScalarSummary()
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
self.tag1 = tag1
self.tag2 = tag2
self.tag3 = tag3
def __init__(self, tag_tuple=None, scalar=1):
super(SummaryNet, self).__init__()
self.summary_s = P.ScalarSummary()
self.summary_i = P.ImageSummary()
self.summary_t = P.TensorSummary()
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
self.tag_tuple = tag_tuple
self.scalar = scalar
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._original_construct = super().construct
self.scalar_summary = P.ScalarSummary()
self.histogram_summary = P.HistogramSummary()
self.weight_names = [param.name for param in self.parameters]
self.gradient_names = [
param.name + ".gradient" for param in self.parameters
]
def __init__(self, num_class=10, channel=1):
super(LeNet5, self).__init__()
self.num_class = num_class
self.conv1 = conv(channel, 6, 5)
self.conv2 = conv(6, 16, 5)
self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
self.fc2 = fc_with_initialize(120, 84)
self.fc3 = fc_with_initialize(84, self.num_class)
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.flatten = nn.Flatten()
self.scalar_summary = P.ScalarSummary()
self.image_summary = P.ImageSummary()
self.histogram_summary = P.HistogramSummary()
self.tensor_summary = P.TensorSummary()
self.channel = Tensor(channel)
def __init__(self, num_class=10, num_channel=1, include_top=True):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.include_top = include_top
if self.include_top:
self.flatten = nn.Flatten()
self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
self.scalar_summary = P.ScalarSummary()
self.image_summary = P.ImageSummary()
self.histogram_summary = P.HistogramSummary()
self.tensor_summary = P.TensorSummary()
self.channel = Tensor(num_channel)
def __init__(self):
super(HistogramSummaryNet, self).__init__()
self.summary = P.HistogramSummary()
def __init__(self, ):
super(SummaryDemo, self).__init__()
self.s = P.ScalarSummary()
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
def __init__(self,):
super(HistogramSummaryNet, self).__init__()
self.summary = P.HistogramSummary()
self.add = P.TensorAdd()
def __init__(self):
super().__init__()
self.scalar_summary = P.ScalarSummary()
self.image_summary = P.ImageSummary()
self.tensor_summary = P.TensorSummary()
self.histogram_summary = P.HistogramSummary()
def __init__(self, tag_tuple):
super(SummaryDemoTagForSet, self).__init__()
self.s = P.ScalarSummary()
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
self.tag_tuple = tag_tuple
def __init__(self, value):
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
self.value = value
