cs231n assignment2 dropout

代码：https://github.com/LiuZhe6/CS231N

为了防止神经网络过拟合数据，可以采用dropout方法。其主要思想是：对隐藏层中部分输出或者权重随机置为0。

Dropout

forward pass

题目要求使用inverted dropout，其主要思想是在训练阶段在mask的基础上处以p，使得在测试阶段不需要修改。
layers.py中dropout_forward()

    if mode == 'train':
        #######################################################################
        #       Implement training phase forward pass for inverted dropout.   #
        # Store the dropout mask in the mask variable.                        #
        #######################################################################
        # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
        mask = (np.random.rand(*x.shape) < p) / p
        out = x * mask

        pass

        # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
        #######################################################################
        #                           END OF YOUR CODE                          #
        #######################################################################
    elif mode == 'test':
        #######################################################################
        #       Implement the test phase forward pass for inverted dropout.   #
        #######################################################################
        # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
        out = x
        pass

        # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
        #######################################################################
        #                            END OF YOUR CODE                         #

backward pass

前向传播过程中将mask保存下来了，故直接使用mask进行梯度计算。
layers.py中的dropout_backward()

    if mode == 'train':
        #######################################################################
        #       Implement training phase backward pass for inverted dropout   #
        #######################################################################
        # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
        dx = mask * dout
        pass

        # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
        #######################################################################
        #                          END OF YOUR CODE                           #
        #######################################################################
    elif mode == 'test':
        dx = dout

Inline Question

Inline Question 1
What happens if we do not divide the values being passed through inverse dropout by p in the dropout layer? Why does that happen?
Answer
如果在训练阶段不除以p，则需要在测试时处以p。

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Inline Question 2
Compare the validation and training accuracies with and without dropout – what do your results suggest about dropout as a regularizer?

Answer
使用了dropout的网络，很明显使训练集准确率得到控制，避免了过拟合。

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Inline Question 3
Suppose we are training a deep fully-connected network for image classification, with dropout after hidden layers (parameterized by keep probability p). If we are concerned about overfitting, how should we modify p (if at all) when we decide to decrease the size of the hidden layers (that is, the number of nodes in each layer)?

Answer
当减少网络中的隐藏层时，我们应该尽可能减少dropout的程度，即增加p值。