Repeated iterations

[naruto678]

``
for k in range(batch_size):

        correct_cnt+=int(np.argmax(layer_2[k:k+1]==np.argmax(labels[batch_start+k:batch_start+k+1]))
        layer_2_delta = (labels[batch_start:batch_end]-layer_2)/batch_size
        layer_1_delta = layer_2_delta.dot(weights_1_2.T)* relu2deriv(layer_1)
        layer_1_delta *= dropout_mask
        weights_1_2 += alpha * layer_1.T.dot(layer_2_delta)
        weights_0_1 += alpha * layer_0.T.dot(layer_1_delta)

#####################################################
In the above code , why are we computing the values of layer_1_delta and layer_2_delta again and again...should not one iteration suffice ..what is the purpose..this is the code in regularization chapter for mnist digit classification with mini batched SGD...I changed some
####################################################

``

    layer_2_delta = (labels[batch_start:batch_end]-layer_2)/batch_size
    layer_1_delta = layer_2_delta.dot(weights_1_2.T)* relu2deriv(layer_1)
    weights_1_2 += (batch_size-1)*alpha * layer_1.T.dot(layer_2_delta)
    weights_0_1 += (batch_size-1)*alpha * layer_0.T.dot(layer_1_delta)
    layer_1_delta *= dropout_mask
    for k in range(batch_size):
        correct_cnt += int(np.argmax(layer_2[k:k+1]) == np.argmax(labels[batch_start+k:batch_start+k+1]))

##############################

this seems much faster and reaches the same bench marks
###############################

``

[Bering]

I also struggled with that part. But if you look at the batching in the next chapter (on github), you'll see it's done differently. Only the correct_cnt calculation is in the loop:

for k in range(batch_size):
        correct_cnt += int(np.argmax(layer_2[k:k+1]) == np.argmax(labels[batch_start+k:batch_start+k+1]))

layer_2_delta = (labels[batch_start:batch_end]-layer_2) / (batch_size * layer_2.shape[0])
layer_1_delta = layer_2_delta.dot(weights_1_2.T) * tanh2deriv(layer_1)
layer_1_delta *= dropout_mask

weights_1_2 += alpha * layer_1.T.dot(layer_2_delta)
weights_0_1 += alpha * layer_0.T.dot(layer_1_delta)

[naruto678]

Thank you for clearing my doubt