# Deep Neural Networks Backward module

## L-Model Backward module:

In this part, we will implement the backward function for the whole network. Recall that when we implemented the L_model_forward function, we stored a cache containing a cache at each iteration (X, W, b, and z). In the backpropagation module, we will use those variables to compute the gradients. Therefore, in the L_model_forward function, we will iterate through all the hidden layers backward, starting from layer L. In each step, we will use the cached values for layer l to backpropagate through layer l. The figure below shows the backward pass.

To backpropagate through this network, we know that the output is:

${A}^{\left[L\right]}=\sigma \left({Z}^{\left[L\right]}\right)$

Our code needs to compute:

$dAL=\frac{\partial L}{\partial {A}^{\left[L\right]}}$

To do so, we'll use this formula (derived using calculus which you don't need to remember):

# derivative of cost with respect to AL
dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))

We can then use this post-activation gradient dAL to keep going backward. As seen in the figure above, we can now feed in dAL into the LINEAR->SIGMOID backward function we implemented (which will use the cached values stored by the L_model_forward function). After that, we will have to use a for loop to iterate through all the other layers using the LINEAR->RELU backward function. We should store each dA, dW, and db in the grads dictionary. To do so, we'll use this formula:

$grads\left["dW"+str\left(l\right)\right]=d{W}^{\left[l\right]}$

For example, for l=3 this would store dW[l] in grads["dW3"].

## Code for our linear_backward function:

Arguments:

AL - probability vector, the 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:
1. every cache of linear_activation_forward() with "relu" (it's caches[l], for l in range(L-1) i.e l = 0...L-2);
2. the cache of linear_activation_forward() with "sigmoid" (it's caches[L-1]).

Return:

def L_model_backward(AL, Y, caches):

# the number of layers
L = len(caches)
m = AL.shape[1]

# after this line, Y is the same shape as AL
Y = Y.reshape(AL.shape)

# Initializing the backpropagation
dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))

current_cache = caches[L-1]

# Loop from l=L-2 to l=0
for l in reversed(range(L-1)):
# lth layer: (RELU -> LINEAR) gradients.
# Inputs: "grads["dA" + str(l + 1)], current_cache".
# Outputs: "grads["dA" + str(l)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)]

current_cache = caches[l]
dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA"+str(l+1)], current_cache, "relu")
grads["dW" + str(l + 1)] = dW_temp
grads["db" + str(l + 1)] = db_temp

return grads

## Update Parameters module:

In this section, we will update the parameters of the model using gradient descent:

${W}^{\left[l\right]}={W}^{\left[l\right]}-\alpha d{W}^{\left[l\right]}\phantom{\rule{0ex}{0ex}}{b}^{\left[l\right]}={b}^{\left[l\right]}-\alpha d{b}^{\left[l\right]}$

here α is the learning rate. After computing the updated parameters, we'll store them in the parameters dictionary.

## Code for our update_parameters function:

Arguments:

parameters - python dictionary containing our parameters.

Return:

parameters - python dictionary containing our updated parameters:
parameters["W" + str(l)] = ...
parameters["b" + str(l)] = ...

def update_parameters(parameters, grads, learning_rate):
# number of layers in the neural network
L = len(parameters) // 2

# Update rule for each parameter
for l in range(L):
parameters["W" + str(l+1)] = parameters["W" + str(l+1)] - learning_rate*grads["dW" + str(l+1)]
parameters["b" + str(l+1)] = parameters["b" + str(l+1)] - learning_rate*grads["db" + str(l+1)]

return parameters

Conclusion:

Congrats on implementing all the functions required for building a deep neural network. It was a long tutorial, but from now on, it will only get better. We'll put all these together to build An L-layer neural network (deep) in the next part. In fact, we'll use these models to classify cat vs. dog images.