70cf20f6cf5e21c3b6165067483b925db61c161c,deepmedic/neuralnet/ops.py,,applyDropout,#Any#Any#Any#Any#Any#Any#,29

Before Change

        inputImgAfterDropout = inputTrain
        inputImgAfterDropoutInference = inputInference
        inputImgAfterDropoutTesting = inputTesting
    return (inputImgAfterDropout, inputImgAfterDropoutInference, inputImgAfterDropoutTesting)


def applyBn(rollingAverageForBatchNormalizationOverThatManyBatches, inputTrain, inputVal, inputTest, inputShapeTrain) :
    numberOfChannels = inputShapeTrain[1]

After Change

        keep_prob = (1-dropoutRate)
        
        random_tensor = keep_prob
        random_tensor += tf.random_uniform(shape=inputTrainShape, minval=0., maxval=1., seed=rng.randint(999999), dtype="float32")
        // 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
        dropoutMask = tf.floor(random_tensor)
    
        // tf.nn.dropout(x, keep_prob) scales kept values UP, so that at inference you dont need to scale then. 
        inputImgAfterDropoutTrain = inputTrain * dropoutMask
        inputImgAfterDropoutVal = inputVal * keep_prob
        inputImgAfterDropoutTest = inputTest * keep_prob
    else :
        inputImgAfterDropoutTrain = inputTrain
        inputImgAfterDropoutVal = inputVal
        inputImgAfterDropoutTest = inputTest
    return (inputImgAfterDropoutTrain, inputImgAfterDropoutVal, inputImgAfterDropoutTest)


def applyBn(rollingAverageForBatchNormalizationOverThatManyBatches, inputTrain, inputVal, inputTest, inputShapeTrain) :
    numOfChanns = inputShapeTrain[1]

Instances