uncertainty_leak_issue

leak.py

from keras.layers import Dense, Dropout
from keras.models import Sequential
import numpy as np
import keras.backend as K

def model():
    # create model
    model = Sequential()

    model.add(Dense(100, input_dim=36, kernel_initializer='uniform', activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, kernel_initializer='normal'))

    return model

m = model()
m.compile(loss='mse', optimizer='adam', metrics=['mean_absolute_error'])


def predict_with_uncertainty(mdl, x, n_iter=30):
    f = K.function([mdl.layers[0].input, K.learning_phase()],
                   [mdl.layers[-1].output])

    result = np.zeros((n_iter, x.shape[0]))

    for iter in range(n_iter):
        result[iter] = np.array(f([x, 1])[0]).reshape(-1)

    return result

while True:
    predict_with_uncertainty(m, np.zeros((1000, 36)))