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From nick blog it is clear that in dropout layer of CNN model we drop some nodes on the basis of bernoulli. But how to verify it, i.e. how to check which node is not selected. In DropConnect we leave some weights so I think with the help of model.get_weights() we can verify, but how in the case of dropout layer. 

model = Sequential()
model.add(Conv2D(2, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(4, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(8, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.binary_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

Another question is that it is mention in keras that dropout rate should float b/w 0 to 1. But for above model when I take dropout rate = 1.25, then also my model is working, how this happens?

ebeneditos
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Hitesh
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1 Answers1

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Concerning your second question, if you see Keras code, in the call method form Dropout class:

def call(self, inputs, training=None):
    if 0. < self.rate < 1.:
        noise_shape = self._get_noise_shape(inputs)

        def dropped_inputs():
            return K.dropout(inputs, self.rate, noise_shape,
                             seed=self.seed)
        return K.in_train_phase(dropped_inputs, inputs,
                                training=training)
    return inputs

This means that if the rate is not between 0 and 1, it will do nothing.

ebeneditos
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  • ok, got it, that it not give error but it will do nothing. and i want to check it, for better understanding of cnn model, and to show students. – Hitesh Apr 13 '18 at 11:08