| import mxnet as mx |
| |
| def test_print_summary(): |
| data = mx.sym.Variable('data') |
| bias = mx.sym.Variable('fc1_bias', lr_mult=1.0) |
| conv1= mx.symbol.Convolution(data = data, name='conv1', num_filter=32, kernel=(3,3), stride=(2,2)) |
| bn1 = mx.symbol.BatchNorm(data = conv1, name="bn1") |
| act1 = mx.symbol.Activation(data = bn1, name='relu1', act_type="relu") |
| mp1 = mx.symbol.Pooling(data = act1, name = 'mp1', kernel=(2,2), stride=(2,2), pool_type='max') |
| fc1 = mx.sym.FullyConnected(data=mp1, bias=bias, name='fc1', num_hidden=10, lr_mult=0) |
| fc2 = mx.sym.FullyConnected(data=fc1, name='fc2', num_hidden=10, wd_mult=0.5) |
| sc1 = mx.symbol.SliceChannel(data=fc2, num_outputs=10, name="slice_1", squeeze_axis=0) |
| mx.viz.print_summary(sc1) |
| shape = {} |
| shape["data"]=(1,3,28,28) |
| mx.viz.print_summary(sc1, shape) |
| |
| if __name__ == "__main__": |
| test_print_summary() |
