How to get the probability of given predictions for Sequential model Sklearn

I have a Sequential, Convolutional neural network model which is being trained on sklearns handwritten digit dataset, Im now evaluating the model and attempting to create an Roc curve from scratch, This requires me the get the Tpr and Fpr for my test data so I can plot it according to given threshold(From what i understand). Im trying to get the prediction probability for each prediction but I cannot seem to figure out how, I have tried predict_proba() but I think i have the wrong model, Is there a way to do this, if not how Can i go about plotting my Roc; Do i have to make a conf. matrix for each prediction, this seems long winded?

I found an Roc curve function on another post Ill display below, im trying to obtain 'score'

score = np.array([0.9, 0.8, 0.7, 0.6, 0.55, 0.54, 0.53, 0.52, 0.51, 0.505, 0.4, 0.39, 0.38, 0.37, 0.36, 0.35, 0.34, 0.33, 0.30, 0.1])
y = np.array([1,1,0, 1, 1, 1, 0, 0, 1, 0, 1,0, 1, 0, 0, 0, 1 , 0, 1, 0])

# false positive rate
fpr = []
# true positive rate
tpr = []
# Iterate thresholds from 0.0, 0.01, ... 1.0
thresholds = np.arange(0.0, 1.01, .01)

# get number of positive and negative examples in the dataset
P = sum(y)
N = len(y) - P

# iterate through all thresholds and determine fraction of true positives
# and false positives found at this threshold
for thresh in thresholds:
    FP=0
    TP=0
    for i in range(len(score)):
        if (score[i] > thresh):
            if y[i] == 1:
                TP = TP + 1
            if y[i] == 0:
                FP = FP + 1
    fpr.append(FP/float(N))
    tpr.append(TP/float(P))

plt.scatter(fpr, tpr)
plt.show()