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