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Dropout layer, Why it is used?

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    What is the use of dropout layer? How it prevents the model by getting over fit? This post was edited by Jainew Nanda at July 21, 2021 10:13 PM IST
      July 21, 2021 10:09 PM IST
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  • 319 1
    Dropout is used to avoid the model from getting overfitted.
      August 9, 2021 11:57 PM IST
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  • 131 2
    Dropout Layers can be an easy and effective way to prevent overfitting in your models. A dropout layer randomly drops some of the connections between layers. This helps to prevent overfitting, because if a connection is dropped, the network is forced to Luckily, with keras it's really easy to add a dropout layer
      August 11, 2021 5:13 PM IST
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    The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over all inputs is unchanged.

    Note that the Dropout layer only applies when training is set to True such that no values are dropped during inference. When using model.fit, training will be appropriately set to True automatically, and in other contexts, you can set the kwarg explicitly to True when calling the layer.

    (This is in contrast to setting trainable=False for a Dropout layer. trainable does not affect the layer's behavior, as Dropout does not have any variables/weights that can be frozen during training.

    This post was edited by Viaan Prakash at December 18, 2021 11:41 AM IST
      December 18, 2021 11:40 AM IST
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