import paddle
import paddle.nn as nn

__all__ = []


class LeNet(nn.Layer):
    """LeNet model from
    `"LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.`_

    Args:
        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
                            will not be defined. Default: 10.

    Examples:
        .. code-block:: python

            from paddle.vision.models import LeNet

            model = LeNet()
    """

    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, num_classes))

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

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