Hello, I just made my way through the 2 layer network. Now I started working on the L-layer network and stumbled over this line: ### CONSTANTS ### layers_dims = [12288, 20, 7, 5, 1] # 4-layer model I know the input vector X had been flattened. That would explain the numbers ‘12288’ and ‘1’ in th…

Look at how layers_dims is used. That should make it clear what is going on. It is telling you the following things: That the network you are specifying has 4 layers: 3 hidden layers and the output layer. What the number of neurons are that are output by each layer. What the size of the input vect…

Same answer as on the two layer case: if you copied over your functions from the Step by Step exercise, that will fail in exactly this way. The deep “init” routine they used here is more sophisticated: the simple function that they had us build in Step by Step gives really lousy convergence on this …

Also note that all the code we are writing here is “general”: it can handle any number of layers and any combination of numbers of neurons in each layer. You just use what the layers_dim variable tells you to drive everything. So it is not “a problem” that different test cases use different numbers …

I really appreciate your advice, and will try to find a way to make my code as flexible and not “hard-wired” as it surely is presently. I’m rather eager to see how a proper solution makes it “just work,” which I take at this point to mean just what we’ve been discussing with regard to dimensional a…

I really don’t think the problem in this particular instance is “hard-coding”. If you had the dimensions wrong, the error you get would be different. My guess is (as I said the my initial reply) that you’re still using your hand copied initialize_parameters_deep routine from the Step by Step exercis…

Hi, I’ve been trying to track down what could be hard-copied from the Step-by-Step code. However, in trying to flush this out, I’ve noticed a peculiarity with the first part of the 2nd assignment for two_layer_model_test(two_layer_model). First, let me display the decline in costs from the plot o…

Hello. I’m concerned that I gave too many details yesterday to quickly digest a description about a discrepancy with respect to the model two_layer_model parameters, so I will now summarize the main issue that I have so far been unable to solve. Note that the code shown here is from the original n…

I did not go through your long post, but I think I have some advices for you. and the function two_layer_model_test in public_tests.py takes two_layer_model as its target parameter, and then overwrites layers_dims with n_x = 10 , n_h = 4 , and n_y = 1 after initial iteration 0: This is a separat…

There are two completely separate test cases, right? They don’t have to use the same parameters in all test cases. Look again at the code in that test cell: First it runs a test by directly invoking two_layer_model using the parameters as already defined by previous cells. Then it invokes two_laye…

Hello, Nobu and Paul. I just wanted to thank you for the clarification. It allowed me to finish the course.

That’s great to hear. Congratulations! Do you plan to continue on with Course 2? There are plenty of interesting topics yet to be covered. Regards, Paul

Week 4 - 2nd Assignment - layer_dims?

Course Q&A Deep Learning Specialization Neural Networks and Deep Learning

paulinpaloalto June 2, 2024, 10:37pm 24

There is a thread about how to get a clean copy, which is also linked from the DLS FAQ Thread.

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Week 4 - 2nd Assignment - layer_dims?

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