state_dict() # take trainable params weight = state_dict["linear1.weight"] bias = state_dict["linear1.bias"] # calculate output of the model manually calculated_linear_output = weight * data + bias # find output of the torch model output = model(data) # check if two outputs are ...
The proposed architecture is entirely different from conventional networks such as the segmentation network (SegNet) [43], outer residual skip network (OR-Skip-Net) [44], and U-shaped network (U-Net) [45], which are deep neural networks with larger number of trainable parameters. The shallow...
Each block has the same number of convolutions (2 convolutions) Booster block. 4 pooling layers The number of trainable parameters is 1.72 M. Both internal and external connectivities are used. Dense connectivity is used. 16 convolution layers (3 × 3) including 6 layers of booster block (...