Feb-20-2020, 04:56 PM
1. W1 is the weight matrix and is the shape going between input and first hidden layer
2. Use np.shape(w1)
3. Input layer will be the number of features. For example, this is one of my model input layers, left it flexible for me so if I change data structure I don't have to chase down and change the input layer.
5. The function initialize_parameters_test_case() (look in your code) returns 3 values.
6. Same as #2
7. print type of logprobs to see what it is. That may help.
8. layer_sizes by itself should print the layer_sizes. predictions=np.round(A2) will round A2
9. Yes
10. https://www.geeksforgeeks.org/enumerate-in-python/
2. Use np.shape(w1)
3. Input layer will be the number of features. For example, this is one of my model input layers, left it flexible for me so if I change data structure I don't have to chase down and change the input layer.
layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())])4. You can create your own repository. Go to GitHub and sign up. Using them is not intuitive, but pretty easy once you get used to it. I suggest "A Practical Guid to Git and Github for Windows Users from Amazon.
5. The function initialize_parameters_test_case() (look in your code) returns 3 values.
6. Same as #2
7. print type of logprobs to see what it is. That may help.
8. layer_sizes by itself should print the layer_sizes. predictions=np.round(A2) will round A2
9. Yes
10. https://www.geeksforgeeks.org/enumerate-in-python/