Deep Neural Networks step by step

Posted May 3, 2019 by Rokas Balsys

##### 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, at each iteration, we stored a cache which contains (X, W, b, and z). In the back propagation 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 $L$. On each step, we will use the cached values for layer $l$ to backpropagate through layer $l$. Figure below shows the backward pass. To backpropagate through this network, we know that the output is, $A^{[L]} = \sigma(Z^{[L]})$. Our code needs to compute $dAL = \frac{\partial \mathcal{L}}{\partial A^{[L]}}$. 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 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: $$grads["dW" + str(l)] = dW^{[l]}$$ For example, for $l=3$ this would store $dW^{[l]}$ in $grads["dW3"]$.

##### Code for our linear_backward function:

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]).

Return:

def L_model_backward(AL, Y, caches):

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

# 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



##### Update Parameters module:

In this section we will update the parameters of the model, using gradient descent: $$W^{[l]} = W^{[l]} - \alpha \text{ } dW^{[l]}$$ $$b^{[l]} = b^{[l]} - \alpha \text{ } db^{[l]}$$ here $\alpha$ 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


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