def L_model_backward(AL, Y, caches):
"""
Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group
Arguments:
AL -- probability vector, output of the forward propagation (L_model_forward())
Y -- true "label" vector (containing 0 if non-cat, 1 if cat)
caches -- list of caches containing:
every cache of linear_activation_forward() with "relu" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)
the cache of linear_activation_forward() with "sigmoid" (it's caches[L-1])
Returns:
grads -- A dictionary with the gradients
grads["dA" + str(l)] = ...
grads["dW" + str(l)] = ...
grads["db" + str(l)] = ...
"""
grads = {}
L = len(caches) # the number of layers
m = AL.shape[1]
Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
# Initializing the backpropagation
### START CODE HERE ### (1 line of code)
dAL = -(np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
### END CODE HERE ###
# Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "AL, Y, caches". Outputs: "grads["dAL"], grads["dWL"], grads["dbL"]
### START CODE HERE ### (approx. 2 lines)
current_cache = caches[-1]
grads["dA" +
str(L)], grads["dW" +
str(L)], grads["db" + str(L)] = linear_backward(
sigmoid_backward(dAL, current_cache[1]),
current_cache[0])
### END CODE HERE ###
for l in reversed(range(L - 1)):
# lth layer: (RELU -> LINEAR) gradients.
# Inputs: "grads["dA" + str(l + 2)], caches". Outputs: "grads["dA" + str(l + 1)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)]
### START CODE HERE ### (approx. 5 lines)
current_cache = caches[l]
dA_prev_temp, dW_temp, db_temp = linear_backward(
sigmoid_backward(dAL, current_cache[1]), current_cache[0])
grads["dA" + str(l + 1)] = dA_prev_temp
grads["dW" + str(l + 1)] = dW_temp
grads["db" + str(l + 1)] = db_temp
### END CODE HERE ###
return grads
def linear_activation_backward(dA, cache, activation="relu"):
"""
实现【LINEAR -> ACTIVATION】线性+激活部分反向计算。
:param dA: 当前层l的激活后的梯度值
:param cache: 我们存储的用于有效计算反向传播的值的元组（值为linear_cache，activation_cache）
:param activation: 要在此层中使用的激活函数名，字符串类型，【"sigmoid" | "relu"】
:return:
dA_prev - 相对于激活（前一层l-1）的成本梯度值，与A_prev维度相同
dW - 相对于W（当前层l）的成本梯度值，与W的维度相同
db - 相对于b（当前层l）的成本梯度值，与b的维度相同
"""
# linear_activation_forward()的返回值为：
# cache = (linear_cache, activation_cache)
# linear_cache(上层的A, 本层的W, 本层的b)
# activation_cache(本层的Z)
linear_cache, activation_cache = cache
# 当激活函数选择ReLU时
if activation == "relu":
# 使用函数relu_backward()计算反向传播dZ（外套，激活函数求导）
dZ = relu_backward(dA, activation_cache)
# 使用函数linear_backward()计算反向传播dA^[l-1]、dW^[l]、db^[l]（内含，线性部分求导）
dA_prev, dW, db = linear_backward(dZ, linear_cache)
# 当激活函数选择Sigmoid时
elif activation == "sigmoid":
# 使用函数sigmoid_backward()计算激活函数反向传播dZ（外套，激活函数求导）
dZ = sigmoid_backward(dA, activation_cache)
# 使用函数linear_backward()计算线性部分反向传播dA^[l-1]、dW^[l]、db^[l]（内含，线性部分求导）
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def __linear_activation_backward(self, dA, cache, activation):
"""
Implement the backward propagation for the LINEAR->ACTIVATION layer.
Arguments:
dA -- post-activation gradient for current layer l
cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently
activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
Returns:
dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
dW -- Gradient of the cost with respect to W (current layer l), same shape as W
db -- Gradient of the cost with respect to b (current layer l), same shape as b
"""
linear_cache, activation_cache = cache[0], cache[1]
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
else:
raise Exception("No such activation method: {}.".format(activation))
dA_prev, dW, db = self.__linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cachee, activation):
if activation == "relu":
dZ = relu_backward(dA, cachee[1])
else:
dZ = sigmoid_backward(dA, cachee[1])
dA_prev, dW, db = linear_backward(dZ, cachee[0])
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation):
"""
Implement the backward propagation for the LINEAR->ACTIVATION layer.
Arguments:
dA -- post-activation gradient for current layer l
cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently
activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
Returns:
dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
dW -- Gradient of the cost with respect to W (current layer l), same shape as W
db -- Gradient of the cost with respect to b (current layer l), same shape as b
"""
linear_cache, activation_cache = cache
Z = activation_cache
A_prev, W, b = linear_cache
if activation == "relu":
### START CODE HERE ### (≈ 2 lines of code)
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
### END CODE HERE ###
elif activation == "sigmoid":
### START CODE HERE ### (≈ 2 lines of code)
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
### END CODE HERE ###
return dA_prev, dW, db
def l_model_backward(AL, Y, cache):
"""
:param AL: output of forward propagation
:param Y: true labels
:param cache: list of caches (l-1) output for relu and cache l output for sigmoid
:return:
grads: gradients for dA, dW and db
"""
grads = {}
L = len(cache)
m = AL.shape[1]
Y = Y.reshape(AL.shape)
dAL = -(np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
current_cache = cache[-1]
grads["dA" +
str(L - 1)], grads["dW" +
str(L)], grads["db" + str(L)] = linear_backward(
sigmoid_backward(dAL, current_cache[1]),
current_cache[0])
for layers in reversed(range(L - 1)):
current_cache = cache[layers]
dA_prev_temp, dW_temp, db_temp = linear_backward(
relu_backward(grads["dA" + str(layers + 1)], current_cache[1]),
current_cache[0])
grads["dA" + str(layers)] = dA_prev_temp
grads["dW" + str(layers + 1)] = dW_temp
grads["db" + str(layers + 1)] = db_temp
np.set_printoptions(suppress=True)
return grads
def linear_activation_backward(dA, cache, activation):
"""
Implement the backward propagation for the LINEAR->ACTIVATION layer.
Arguments:
dA {np array} -- [post-activation gradient for current layer l]
cache {tuple} -- [(linear_cache, activation_cache)]
activation {string} -- the activation to be used in this layer
Returns:
[dA_prev] -- [gradient of the cost wrt the activation of the previous layer]
[dW] -- []
[db] -- []
"""
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation="relu"):
"""
实现LINEAR-> ACTIVATION层的后向传播。
参数：
dA - 当前层l的激活后的梯度值
cache - 我们存储的用于有效计算反向传播的值的元组（值为linear_cache，activation_cache）
activation - 要在此层中使用的激活函数名，字符串类型，【"sigmoid" | "relu"】
返回：
dA_prev - 相对于激活（前一层l-1）的成本梯度值，与A_prev维度相同
dW - 相对于W（当前层l）的成本梯度值，与W的维度相同
db - 相对于b（当前层l）的成本梯度值，与b的维度相同
"""
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "softmax":
dZ = softmax_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def L_model_backward(AL,Y,cache):
"""
Implement backprop for (LINEAR->ReLU)*L-1 -> LINEAR->SIGMOID.
Arguments:
AL-- probability vector output of forward propgation process (L_model_forward())
Y-- labels vector (1 if cat, 0 if non-cat)
cache --
Returns:
grads -- A dictionary with the gradients
grads["dA" + str(l)] = ...
grads["dW" + str(l)] = ...
grads["db" + str(l)] = ...
"""
grads = {}
L = len(cache) # number of layers
m = AL.shape[1] # number of training examples
Y = Y.reshape(AL.shape)
# Initialise backpropagation
dAL = np.divide(1-Y,1-AL) - np.divide(Y,AL)
"""
Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "AL, Y, caches".
Outputs: "grads["dAL"], grads["dWL"], grads["dbL"]
"""
current_cache = cache[-1]
grads["dA"+str(L)],grads["dW"+str(L)],grads["db"+str(L)] =linear_backward(
sigmoid_backward(dAL,current_cache[1]),current_cache[0])
for l in reversed(range(L-1)):
current_cache = cache[l]
dA_prev_temp,dW_temp,db_temp = linear_backward(sigmoid_backward(
dAL,current_cache[1]),
current_cache[0])
grads["dA"+str(l+1)] = dA_prev_temp
grads["dW"+str(l+1)] = dW_prev_temp
grads["db"+str(l+1)] = db_prev_temp
return grads
def linear_activation_backward(dA, cache, activation="relu"):
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA,cache,activation='relu'):
linear_cache,activation_cache = cache
if activation == 'relu':
dZ = relu_backward(dA,activation_cache)
elif activation == 'sigmoid':
dZ = sigmoid_backward(dA,activation_cache)
else:
dZ = dA
return linear_backward(dZ,linear_cache)
def L_model_backward(AL, Y, caches):
grads = {}
L = len(caches)
m = AL.shape[1]
Y = Y.reshape(AL.shape)
dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
current_cache = caches[-1]
grads["dA" + str(L)], grads["dW" + str(L)], grads["db" + str(L)] = linear_backward(sigmoid_backward(dAL, current_cache[1]), current_cache[0])
for l in reversed(range(L-1)):
current_cache = cache[l]
dA_prev_temp, dW_temp, db_temp = linear_backward(sigmoid_backward(dAL, current_cache[1]), current_cache[0])
grads["dA" + str(l + 1)] = dA_prev_temp
grads["dW" + str(l + 1)] = dW_temp
grads["db" + str(l + 1)] = db_temp
return grads
def __linear_activation_backward(self, dA, cache, activation):
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
elif activation == "tanh":
dZ = tanh_backward(dA, activation_cache)
dA_prev, dW, db = self.__linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation):
'''
Implement the backward propagation for the Linear->Activation layer
'''
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation):
linear_cache, active_cache = cache
#计算dZ
if activation == 'relu':
dZ = utils.relu_backward(dA, cache)
elif activation == 'sigmoid':
dZ = utils.sigmoid_backward(dA, cache)
#由dZ计算dW,db,dA_prev
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(self, dA, cache, activation):
# 先激活函数求导算出dZ
linear_cache, activation_cache = cache # 取出两部分缓存
if activation == 'relu':
dZ = relu_backward(dA, activation_cache) # 求导需要dA和Z
elif activation == 'sigmoid':
dZ = sigmoid_backward(dA, activation_cache)
# 随后对参数线性求导
dA_prev, dW, db = self.linear_backward(
dZ, linear_cache) # dA_prev会用作上一层的激活函数求导
return dA_prev, dW, db
def L_model_backward(AL, Y, caches):
'''
Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group
Arguments:
AL -- probability vector, output of the forward propagation (L_model_forward())
Y -- true "label" vector (containing 0 if non-cat, 1 if cat)
caches -- list of caches containing:
every cache of linear_activation_forward() with "relu" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)
the cache of linear_activation_forward() with "sigmoid" (it's caches[L-1])
Returns:
grads -- A dictionary with the gradients
grads["dA" + str(l)] = ...
grads["dW" + str(l)] = ...
grads["db" + str(l)] = ...
'''
grads = {}
L = len(caches)
m = AL.shape[1]
y = Y.reshape(AL.shape)
# Initializing the backpropagation
dAL = -(np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
# Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "AL, Y, caches". Outputs: "grads["dAL"], grads["dWL"], grads["dbL"]
current_cache = caches[-1]
grads["dA" +
str(L)], grads["dW" +
str(L)], grads["db" + str(L)] = linear_backward(
sigmoid_backward(dAL, current_cache[1]),
current_cache[0])
for l in reversed(range(L - 1)):
current_cache = caches[l]
dA_prev_temp, dw_temp, db_temp = linear_backward(
sigmoid_backward(AL, current_cache[1]), current_cache[0])
grads["dA" + str(l + 1)] = dA_prev_temp
grads["dW" + str(l + 1)] = dw_temp
grads["db" + str(l + 1)] = db_temp
return grads
def linear_activation_backward(dA, cache, activation):
linear_cache, activation_function = cache
if activation == "relu":
dZ = relu_backward(dA, cache[1])
dA_prev, dW, db = linear_backward(dZ, cache[0])
if activation == "sigmoid":
dZ = sigmoid_backward(dA, cache[1])
dA_prev, dW, db = linear_backward(dZ, cache[0])
return dA_prev, dW, db
def linear_activation_backward(dA,cache,activation): """ Implement the backward propagation of neural unit """ if activation == "Relu": dz = relu_backward(dA,cache[1]) elif activation == "sigmoid": dz = sigmoid_backward(dA,cache[1]) dw,db,dA_pre = linear_backward(dz,cache[0]) return dw,db,dA_pre
def L_model_backward(AL, Y, caches, lamba):
grads = {}
L = len(caches)
Y = Y.reshape(AL.shape)
dAL = np.divide(1 - Y, 1 - AL) - np.divide(Y, AL)
current_cache = caches[L - 1]
grads["dA" + str(L)], grads["dW" + str(L)], grads[
"db" + str(L)] = linear_activation_backward(
dAL, current_cache, lambda dA, Z: sigmoid_backward(dA, Z), lamba)
for l in reversed(range(L - 1)):
current_cache = caches[l]
grads["dA" + str(l + 1)], grads["dW" + str(l + 1)], grads[
"db" + str(l + 1)] = linear_activation_backward(
grads["dA" + str(l + 2)], current_cache,
lambda dA, Z: relu_backward(dA, Z), lamba)
return grads
def linear_activation_backward(dA, cache, activation):
"""
:param dA:
:param cache:
:param activation:
:return:dA_prev, dW, db
"""
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "softmax":
dZ = softmax_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation):
linear_cache, activation_cache = cache
if activation == "relu":
### START CODE HERE ### (≈ 2 lines of code)
dZ = relu_backward(dA, activation_cache)
### END CODE HERE ###
elif activation == "sigmoid":
### START CODE HERE ### (≈ 2 lines of code)
dZ = sigmoid_backward(dA, activation_cache)
### END CODE HERE ###
# Shorten the code
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation):
'''
实现linear -> Activation 层的后向传播
:param dA: 当前层激活后的梯度值
:param cache: 我们存储用于有效计算反向传播的值的元组,值为（linear_cache(# linear_cache = (A, W, b)),activation_cache(# Z))
:param activation:要在此层中使用的激活函数的名称，字符串类型，如["relu"|"sigmoid"]
:return:
dA_prev:相对于激活（前一层L-1）的成本梯度值，与A_prev的维度相同
dW:相对于W(当前层l)的成本梯度值，与W维度相同
db:相对于b(当前层l)的成本梯度值，与b维度相同
'''
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache) # activation_cache = Z
if activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation):
"""
:param dA: 当前层l的激活后的梯度值
:param cache:我们存储的用于有效计算反向传播的值的元组（值为linear_cache，activation_cache）
:param activation: 要在此层中使用的激活函数名，字符串类型，【"sigmoid" | "relu"】
:return: dA_prev - 相对于激活（前一层l-1）的成本梯度值，与A_prev维度相同
dW - 相对于W（当前层l）的成本梯度值，与W的维度相同
db - 相对于b（当前层l）的成本梯度值，与b的维度相同
"""
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA,cache,activation='relu'):
'''
实现linear->activation层的后向传播
:param dA: 当前层l的激活后的梯度值
:param cache: 我们存储的用于有效计算反向传播的值的元组(值为linear_cache,activation_cache)
:param activation: 要在此层中使用的激活函数名,字符串类型,['sigmoid'|'relu]
:return:
dA_prev-相当于激活(前一层)的 成本梯度值,与W的维度相同
dW-相当于W(当前层l)的成本梯度值,与W的维度相同
db-相当于b(当前层l)的成本梯度值,与b的维度相同
'''
linear_cache,activation_cache=cache
if activation == 'relu':
dZ = relu_backward(dA,activation_cache)
dA_prev,dW,db = linear_backward(dZ,linear_cache)
elif activation == 'sigmoid':
dZ = sigmoid_backward(dA,activation_cache)
dA_prev,dW,db = linear_backward(dZ,linear_cache)
return dA_prev,dW,db
def linear_activation_backward(dA, cache, activation="relu"):
"""
实现linear->activation层的后向传播
参数:
dA -当前层的激活后的梯度值
cache -前向过程中存储的用于计算反向传播的值的元组
activation -激活函数名称 sigmoid或者relu
返回:
dA_prev -相对于激活(前一层l-1)成本梯度,与A_prev维度相同
dW -相对于W(当前层l)的成本梯度,与W维度相同
db -相对于b(当前层l)的成本梯度,与b维度相同
"""
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
if activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation="relu"):
"""
实现LINEAR->ACTIVATION层的后向传播
参数：
dA：当前层l的激活后的梯度值
cache：我们存储的用于有效计算反向传播的值的元组（值为linear_cache，activation_cache）
activation： 要在此层中使用的激活函数名，字符串类型，【"sigmoid" | "relu"】
返回：
dA_prev：相对于前一层的成本梯度值，与A_prev维度相同
dW：相对于W的成本梯度值，与W的维度相同
db：相对于b的成本梯度值，与b的维度相同
"""
linear_cache, activation_cache = cache
if activation == "relu":
dZ = relu_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
elif activation == "sigmoid":
dZ = sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = linear_backward(dZ, linear_cache)
return dA_prev, dW, db
def linear_activation_backward(dA, cache, activation):
"""
Implement the backward propagation for the LINEAR->ACTIVATION layer.
Arguments:
dA -- post-activation gradient for current layer l
cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently
activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
Returns:
dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
dW -- Gradient of the cost with respect to W (current layer l), same shape as W
db -- Gradient of the cost with respect to b (current layer l), same shape as b
"""
A_prev, W, b, Z = cache
dL_dZ = None
if activation == "relu":
dL_dZ = utils.relu_backward(dA, Z)
elif activation == "sigmoid":
dL_dZ = utils.sigmoid_backward(dA, Z)
m = A_prev.shape[1]
dL_dW = 1 / m * np.dot(dL_dZ, A_prev.T) # dL_dA * dA_dZ * dZ_dW = dL_dW
dL_db = 1 / m * np.sum(dL_dZ, axis=1,
keepdims=True) # dL_dA * dA_dZ * dZ_db = dL_db
dA_prev = np.dot(
W.T, dL_dZ) # think of this step as -> dA[l-1] = W[l] * dL_dZ[l]
assert (dA_prev.shape == A_prev.shape)
assert (dL_dW.shape == W.shape)
assert (dL_db.shape == b.shape)
return dL_dW, dL_db, dA_prev
### END CODE HERE ###
# Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "AL, Y, caches". Outputs: "grads["dAL"], grads["dWL"], grads["dbL"]
### START CODE HERE ### (approx. 2 lines)
current_cache = caches[-1]
grads["dA" + str(L)], grads["dW" + str(L)], grads["db" + str(L)] = linear_backward(sigmoid_backward(dAL,current_cache[1]),current_cache[0])
)
### END CODE HERE ###
for l in reversed(range(L-1)):
# lth layer: (RELU -> LINEAR) gradients.
# Inputs: "grads["dA" + str(l + 2)], caches". Outputs: "grads["dA" + str(l + 1)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)]
### START CODE HERE ### (approx. 5 lines)
current_cache = caches[l]
dA_prev_temp, dW_temp, db_temp = linear_backward(sigmoid_backward(dAL, current_cache[1]), current_cache[0])
grads["dA" + str(l + 1)] = dA_prev_temp
grads["dW" + str(l + 1)] = dW_temp
grads["db" + str(l + 1)] = db_temp
### END CODE HERE ###
return grads
X_assess, Y_assess, AL, caches = L_model_backward_test_case()
grads = L_model_backward(AL, Y_assess, caches)
print ("dW1 = "+ str(grads["dW1"]))
print ("db1 = "+ str(grads["db1"]))
print ("dA1 = "+ str(grads["dA1"]))
# GRADED FUNCTION: update_parameters
def update_parameters(parameters, grads, learning_rate):
"""