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ResNet-18 convolutional neural network consisting of 18 blocks of convolutional layers, average pooling with batch normalization concatenated with a convolutional skip connection, and one fully connected layer. The activation function used is a rectified linear unit. Conv2D, 2D convolution layer; dense, dense (fully connected) layer; merge, merge (sum) layer; norm, batch normalization layer; ReLU rectified linear unit.
