Revision : https://s3.amazonaws.com/download.onnx/models/opset_8/vgg19.tar.gz
The model contains following two operators.
%vgg0_pool4_fwd = MaxPoolkernel_shape = [2, 2], pads = [0, 0, 0, 0], strides = [2, 2]
%vgg0_dense0_fwd = Gemm[alpha = 1, beta = 1, transA = 0, transB = 1](%vgg0_pool4_fwd, %vgg0_dense0_weight, %vgg0_dense0_bias)
The Tensor inputs for the Gemm operator has incompatible shapes.
%vgg0_pool4_fwd (Output of maxpool)- shape : [1L, 512L, 7L, 7L]
%vgg0_dense0_weight(Initializer)- shape : [4096L, 25088L]
The above two shapes are not compatible for matrix multiplication.
Note : The shape inference is done using onnx infer_shapes()
Revision : https://s3.amazonaws.com/download.onnx/models/opset_8/vgg19.tar.gz
The model contains following two operators.
%vgg0_pool4_fwd = MaxPoolkernel_shape = [2, 2], pads = [0, 0, 0, 0], strides = [2, 2]
%vgg0_dense0_fwd = Gemm[alpha = 1, beta = 1, transA = 0, transB = 1](%vgg0_pool4_fwd, %vgg0_dense0_weight, %vgg0_dense0_bias)
The Tensor inputs for the Gemm operator has incompatible shapes.
%vgg0_pool4_fwd (Output of maxpool)- shape : [1L, 512L, 7L, 7L]
%vgg0_dense0_weight(Initializer)- shape : [4096L, 25088L]
The above two shapes are not compatible for matrix multiplication.
Note : The shape inference is done using onnx infer_shapes()