deeplearning.ai

using functions/modules

will include class later

will improve/add functionality

easily understandable

Let us look at all the hyper-parameters and their functionality:

1. layers_dims - contains dimensions of each layer (including input(x) and output(y))

                        specification - a) size - input + hidden + output (=hidden + 2)
                        
                                        b) type - list [X,hidden1,hidden2,...,Y]
   

2. activation_list - contains list of activations for each input layer

                        specification - a) size   - layers_dims - 1             # because output/prediction layer needs no activation further
                        
                                        b) type   - list ["g(X)","g(hidden1)","g(hidden2)",...]  # g(a) means activation for a
                                                                                                 # contains list of strings
                                                                                                 
                                        c) values - "relu","sigmoid","tanh","softmax"(untested),"linear"(buggy) 
   

3. cost_cache - contains name of the cost function to be used

                        specification - a) type   - string
                        
                                        b) values - "cel"(CrossEntropyLoss),"msel"(MSELoss),"softmax"(untested,Cost for softmax activation)   
   

4. learning_rate - most important parameter (self-explanatory)

                        specification - a) type   - int
   

5. mini_batch_size - Size of mini-batch for mini-batch-gradient-descent (self-explanatory)

                        specification - a) type   - int
   

6. num_epochs - number of forward and backward passes you want to give to reduce cost (self-explanatory)

                        specification - a) type   - int
   

7. print_cost - important, if you want to witness that your cost is decreasing

                        specification - a) type   - boolean
   

8. beta1 - hyper-parameter of momentum-optimization and adam-optimization

                        specification - a) type   - int
   

9. beta2 - hyper-parameter of RMSprop-optimization and adam-optimization

                        specification - a) type   - int
   

10. A_type - Different types of activations requires different types of weight initialization to tackle the problem of vanishing and exploding gradient

                        specification - a) type   - string
                        
                                        b) values - "relu" , "xavier" , ""
    

11. optimizer - Type of optimization you want to use !

                        specification - a) type   - string
                        
                                        b) values - "adam" , "momentum" , "rmsprop" , ""
    

12. Reg_type - Type of Regularization you want to use !

                        specification - a) type   - string
                        
                                        b) values - "l2" , "l1" , ""
    

13. lambd - hyper-parameter for Regularization effect

                        specification - a) type   - int
    

In this Library , it is compulsory to assign - X , Y , layers_dims ! Others default settings are stated below (however you can customize it by passing the values in the arguments while calling train.model() ) -

1. activation_list - ["relu","relu",...,"sigmoid]
2. cost_cache - "cel"
3. learning_rate - 0.0007
4. mini_batch_size - m (number of training example, it can also be achieved by setting the value to zero if you are passing in argument)
5. num_epochs - 10000
6. print_cost -True
7. beta1 - 0.9
8. beta2 - 0.999
9. A_type - ""
10. optimizer - "" (Normal gradient descent)
11. Reg_type - "" (No regularization
12. lambd - 0.1

The above 12 things are customizable but if you don't want to, you can opt-out of passing it as argument