def __init__(self, params):
    '\n        We define an convolutional network that predicts the sign from an image. The components\n        required are:\n\n        Args:\n            params: (Params) contains num_channels\n        '
    super(studentB, self).__init__()
    self.num_channels = params.num_channels
    self.conv1 = nn.Conv2d(3, 32, 5, stride=1, padding=2)
    self.bn1 = nn.BatchNorm2d(32)
    self.conv2_1 = nn.Conv2d(32, 32, 1, stride=1, padding=0)
    self.conv2_2 = nn.Conv2d(32, 32, 3, stride=1, padding=1)
    self.conv2_3 = nn.Conv2d(32, 64, 1, stride=1, padding=0)
    self.bn2 = nn.BatchNorm2d(64)
    self.conv3_1 = nn.Conv2d(64, 64, 1, stride=1, padding=0)
    self.conv3_2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
    self.conv3_3 = nn.Conv2d(64, 128, 1, stride=1, padding=0)
    self.bn3 = nn.BatchNorm2d(128)
    self.fc1 = nn.Linear(((4 * 4) * 128), 500)
    self.fcbn1 = nn.BatchNorm1d(500)
    self.fc2 = nn.Linear(500, 10)
    self.dropout_rate = params.dropout_rate
We define an convolutional network that predicts the sign from an image. The components
required are:

Args:
    params: (Params) contains num_channels
def __init__(self, params):
    '\n        We define an convolutional network that predicts the sign from an image. The components\n        required are:\n\n        Args:\n            params: (Params) contains num_channels\n        '
    super(studentB, self).__init__()
    self.num_channels = params.num_channels
    self.conv1 = nn.Conv2d(3, 32, 5, stride=1, padding=2)
    self.bn1 = nn.BatchNorm2d(32)
    self.conv2_1 = nn.Conv2d(32, 32, 1, stride=1, padding=0)
    self.conv2_2 = nn.Conv2d(32, 32, 3, stride=1, padding=1)
    self.conv2_3 = nn.Conv2d(32, 64, 1, stride=1, padding=0)
    self.bn2 = nn.BatchNorm2d(64)
    self.conv3_1 = nn.Conv2d(64, 64, 1, stride=1, padding=0)
    self.conv3_2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
    self.conv3_3 = nn.Conv2d(64, 128, 1, stride=1, padding=0)
    self.bn3 = nn.BatchNorm2d(128)
    self.fc1 = nn.Linear(((4 * 4) * 128), 500)
    self.fcbn1 = nn.BatchNorm1d(500)
    self.fc2 = nn.Linear(500, 10)
    self.dropout_rate = params.dropout_rate