summary

paddle. summary ( net, input_size, dtypes=None ) [源代码]

summary 函数能够打印网络的基础结构和参数信息。

参数:
  • net (Layer) - 网络实例,必须是 Layer 的子类。

  • input_size (tuple|InputSpec|list[tuple|InputSpec) - 输入张量的大小。如果网络只有一个输入,那么该值需要设定为tuple或InputSpec。如果模型有多个输入。那么该值需要设定为list[tuple|InputSpec],包含每个输入的shape。

  • dtypes (str,可选) - 输入张量的数据类型,如果没有给定,默认使用 float32 类型。默认值:None。

返回:字典,包含了总的参数量和总的可训练的参数量。

代码示例

import paddle
import paddle.nn as nn

class LeNet(nn.Layer):
    def __init__(self, num_classes=10):
        super(LeNet, self).__init__()
        self.num_classes = num_classes
        self.features = nn.Sequential(
            nn.Conv2D(
                1, 6, 3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2D(2, 2),
            nn.Conv2D(
                6, 16, 5, stride=1, padding=0),
            nn.ReLU(),
            nn.MaxPool2D(2, 2))

        if num_classes > 0:
            self.fc = nn.Sequential(
                nn.Linear(400, 120),
                nn.Linear(120, 84),
                nn.Linear(
                    84, 10))

    def forward(self, inputs):
        x = self.features(inputs)

        if self.num_classes > 0:
            x = paddle.flatten(x, 1)
            x = self.fc(x)
        return x

lenet = LeNet()

params_info = paddle.summary(lenet, (1, 1, 28, 28))
print(params_info)
# ---------------------------------------------------------------------------
# Layer (type)       Input Shape          Output Shape         Param #
# ===========================================================================
# Conv2D-11      [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
#     ReLU-11       [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
# MaxPool2D-11     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
# Conv2D-12      [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
#     ReLU-12      [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
# MaxPool2D-12    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
# Linear-16         [[1, 400]]            [1, 120]           48,120
# Linear-17         [[1, 120]]            [1, 84]            10,164
# Linear-18         [[1, 84]]             [1, 10]              850
# ===========================================================================
# Total params: 61,610
# Trainable params: 61,610
# Non-trainable params: 0
# ---------------------------------------------------------------------------
# Input size (MB): 0.00
# Forward/backward pass size (MB): 0.11
# Params size (MB): 0.24
# Estimated Total Size (MB): 0.35
# ---------------------------------------------------------------------------
# {'total_params': 61610, 'trainable_params': 61610}