conv2D Batch Normalization Activation (2 layers)
时间: 2024-06-04 14:08:34 浏览: 13
Conv2D Batch Normalization Activation (2 layers) refers to a common neural network architecture for image classification tasks.
It involves two Conv2D layers, each followed by Batch Normalization and an activation function. Conv2D is a type of layer in a neural network that applies a series of filters to an input image. Batch Normalization is a technique used to normalize the input to each layer, making training more stable and efficient. Activation functions are used to introduce non-linearity into the output of a layer.
In this architecture, the output of the first Conv2D layer is passed through Batch Normalization and an activation function, and then fed into the second Conv2D layer. The output of the second Conv2D layer is again passed through Batch Normalization and an activation function before being fed into the next layer or output.
This architecture has shown to be effective in improving the accuracy of image classification tasks, by reducing overfitting and improving the stability of the training process.
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