我从 https://github.com/lovish1234/YOLOv1 那里得到了Yolo v1代码(如果添加此链接有许可证问题，请与我联系(
据我所知，与 Yolo v1 论文不同，该代码不包含 dropout。同样根据原始论文，他们在第一个连接层之后添加了一个速率=0.5的dropout层，以防止层之间的协同适应。
所以我将代码更改为
def build_graph(self):
"""Build the computational graph for the network"""
# Print
if self.verbose:
print('Building Yolo Graph....')
# Reset default graph
tf.reset_default_graph()
# Input placeholder
self.x = tf.placeholder('float32', [None, 448, 448, 3])
self.label_batch = tf.placeholder('float32', [None, 73])
self.keep_prob = tf.placeholder('float32')
# conv1, pool1
self.conv1 = self.conv_layer(1, self.x, 64, 7, 2)
self.pool1 = self.maxpool_layer(2, self.conv1, 2, 2)
# size reduced to 64x112x112
# conv2, pool2
self.conv2 = self.conv_layer(3, self.pool1, 192, 3, 1)
self.pool2 = self.maxpool_layer(4, self.conv2, 2, 2)
# size reduced to 192x56x56
# conv3, conv4, conv5, conv6, pool3
self.conv3 = self.conv_layer(5, self.pool2, 128, 1, 1)
self.conv4 = self.conv_layer(6, self.conv3, 256, 3, 1)
self.conv5 = self.conv_layer(7, self.conv4, 256, 1, 1)
self.conv6 = self.conv_layer(8, self.conv5, 512, 3, 1)
self.pool3 = self.maxpool_layer(9, self.conv6, 2, 2)
# size reduced to 512x28x28
# conv7 - conv16, pool4
self.conv7 = self.conv_layer(10, self.pool3, 256, 1, 1)
self.conv8 = self.conv_layer(11, self.conv7, 512, 3, 1)
self.conv9 = self.conv_layer(12, self.conv8, 256, 1, 1)
self.conv10 = self.conv_layer(13, self.conv9, 512, 3, 1)
self.conv11 = self.conv_layer(14, self.conv10, 256, 1, 1)
self.conv12 = self.conv_layer(15, self.conv11, 512, 3, 1)
self.conv13 = self.conv_layer(16, self.conv12, 256, 1, 1)
self.conv14 = self.conv_layer(17, self.conv13, 512, 3, 1)
self.conv15 = self.conv_layer(18, self.conv14, 512, 1, 1)
self.conv16 = self.conv_layer(19, self.conv15, 1024, 3, 1)
self.pool4 = self.maxpool_layer(20, self.conv16, 2, 2)
# size reduced to 1024x14x14
# conv17 - conv24
self.conv17 = self.conv_layer(21, self.pool4, 512, 1, 1)
self.conv18 = self.conv_layer(22, self.conv17, 1024, 3, 1)
self.conv19 = self.conv_layer(23, self.conv18, 512, 1, 1)
self.conv20 = self.conv_layer(24, self.conv19, 1024, 3, 1)
self.conv21 = self.conv_layer(25, self.conv20, 1024, 3, 1)
self.conv22 = self.conv_layer(26, self.conv21, 1024, 3, 2)
self.conv23 = self.conv_layer(27, self.conv22, 1024, 3, 1)
self.conv24 = self.conv_layer(28, self.conv23, 1024, 3, 1)
# size reduced to 1024x7x7
# fc1, fc2, fc3
self.fc1 = self.fc_layer(29, self.conv24, 512,
flatten=True, linear=False)
self.dropout = tf.nn.dropout(self.fc1, self.keep_prob)
self.fc2 = self.fc_layer(
30, self.dropout, 4096, flatten=False, linear=False)
self.fc3 = self.fc_layer(
31, self.fc2, 1470, flatten=False, linear=True)
我期待一个积极的结果。但是添加辍学后的训练会降低它的功能。有些甚至没有显示盒子，对于那些显示盒子的人，盒子是错误的，信心较低。
我找不到我得到这些结果的原因。 (我的猜测是，要么训练好的模型在某处包含 dropout，要么向预训练模型添加 dropout 层会降低其功能。
这是我必须做的一个重要项目。但我是Tensorflow的新手。所以如果这是一个简单的问题，请原谅我。另外，如果有人知道答案，请告诉我。谢谢。
Dropout 是一个超参数;有时它有帮助，有时它没有帮助。它是否有帮助取决于其他因素，例如其他超参数值(例如批量大小、学习率等(和数据集的属性。