Multiple iterations of neural network loop in python

Question

I have a working neural network loop so I can run neural networks using a predetermined number of nodes in my hidden layer ('nodes_list'). I then calculate the area under the ROC curve for each number of nodes and put this in a list ('roc_outcomes') for plotting purposes. However, I would like to loop over this loop 5 times to get an average area under the ROC curve for each of the three models (model 1: 20 nodes in hidden layer, model 2: 28 nodes in hidden layer, model 3: 38 nodes in hidden layer). This works fine when I am only trying it on one model, but when I iterate over more than one model instead of iterating over model 1 5 times, then model 2 5 times, then model 3 5 times....it iterates over model 1, then model 2, then model 3, and it does this 5 times. The purpose of this nested loop is for me to iterate over each neural network model 5 times, put the area under the ROC curve for each iteration into a list, calculate a mean of that list, and put the mean into a new list. Ultimately, I would like to have a list of three numbers (1 for each model) that is the mean area under the ROC curve for the 5 iterations of that model. Hopefully, I explained this well. Please ask for any clarification.

Here is my code:

nodes_list = [20, 28, 38]  # list with number of nodes in hidden layer per model
roc_outcomes = [] # list of ROC AUC

for i in np.arange(1,6):

    for nodes in nodes_list:
        # Add first layer
        model.add(Dense(units=n_cols, activation='relu', input_shape=(n_cols,)))
        # Add hidden layer
        model.add(Dense(units=nodes, activation='relu'))
        # Add output layer
        model.add(Dense(units=2, activation='softmax'))

        # Compile model
        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
        # Fit model
        model.fit(X, y, validation_split=0.33, epochs=epochs, callbacks=early_stopping_monitor, verbose=True)

        # Get predicted probabilities
        pred_prob = model.predict_proba(X)[:,1]

        # Calculate area under the curve (logit_roc_auc)
        logit_roc_auc = roc_auc_score(y[:,1], pred_prob) 
        # Append roc scores to the roc_outcomes list
        roc_outcomes.append(logit_roc_auc)

    # Get the mean of that list
    mean_roc = np.mean(roc_outcomes)
    # Append to another list
    mean_roc_outcomes = []
    mean_roc_outcomes.append(mean_roc)

I'm actually quite interested in this. I know this post is from two years ago, but I am curious if you can still recall the actual use of this. I understand that you want the means of different scores, but did this end up helping your actual prediction power of the model that you actually decided using? — bioinformatics_student
– bioinformatics_student, Commented Oct 7, 2020 at 20:37

darrahts · Accepted Answer · 2018-08-14 15:47:27Z

2

Construct your loops like this:

for nodes in node_list:
    for i in range(0,5):
        #do your stuff

example:

myList = ['a', 'b', 'c']
for item in myList:
    for i in range(0,5):
        print(item, end=", ")

output:

a, a, a, a, a, b, b, b, b, b, c, c, c, c, c,

answered Aug 14, 2018 at 15:47

darrahts

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