一、深層神經網絡

神經網絡符號：

輸入數據層：layer0，不包括在層數中

n^[l]：第l層包含的神經元數目

二、前向和反向傳播

z^[l] = w^[l] * a^[l-1] + b^[l]

a^[l] = g^[l](z^[l])

向量化：

Z^[l] = W^[l] * A^[l-1] + b^[l]

A^[l] = g^[l](Z^[l])

第l層反向傳播：

輸入：da^[l]

輸出：da^[l-1] dw^[l] db^[l]

dz^[l] = da^[l] * g^[l]‘(z^[l])

dw^[l] = dz^[l] * a^[l-1]

db^[l] = dz^[l]

da^[l-1] = w^[l]T * dz^[l]

向量化：

dZ^[l] = dA^[l] * g^[l]‘(Z^[l])

dW^[l] = 1/m dZ^[l] * A^[l-1]T

db^[l] = 1/m np.sum( dz^[l] ,axis=1, keepdim=TRUE)

dA^[l-1] = W^[l]T * dZ^[l]

第四周—深層神經網絡