Ordering of batch normalization and dropout?

Question

The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general answer is also the

Answer 1

Conv - Activation - DropOut - BatchNorm - Pool --> Test_loss: 0.04261355847120285

Conv - Activation - DropOut - Pool - BatchNorm --> Test_loss: 0.050065308809280396

Conv - Activation - BatchNorm - Pool - DropOut --> Test_loss: 0.04911309853196144

Conv - Activation - BatchNorm - DropOut - Pool --> Test_loss: 0.06809622049331665

Conv - BatchNorm - Activation - DropOut - Pool --> Test_loss: 0.038886815309524536

Conv - BatchNorm - Activation - Pool - DropOut --> Test_loss: 0.04126095026731491

Conv - BatchNorm - DropOut - Activation - Pool --> Test_loss: 0.05142546817660332

Conv - DropOut - Activation - BatchNorm - Pool --> Test_loss: 0.04827788099646568

Conv - DropOut - Activation - Pool - BatchNorm --> Test_loss: 0.04722036048769951

Conv - DropOut - BatchNorm - Activation - Pool --> Test_loss: 0.03238215297460556

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Trained on the MNIST dataset (20 epochs) with 2 convolutional modules (see below), followed each time with

model.add(Flatten())
model.add(layers.Dense(512, activation="elu"))
model.add(layers.Dense(10, activation="softmax"))

The Convolutional layers have a kernel size of (3,3), default padding, the activation is elu. The Pooling is a MaxPooling of the poolside (2,2). Loss is categorical_crossentropy and the optimizer is adam.

The corresponding Dropout probability is 0.2 or 0.3, respectively. The amount of feature maps is 32 or 64, respectively.

Edit: When I dropped the Dropout, as recommended in some answers, it converged faster but had a worse generalization ability than when I use BatchNorm and Dropout.