| """This file defines various models used in the test""" |
| import mxnet as mx |
| |
| def mlp2(): |
| data = mx.symbol.Variable('data') |
| out = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=1000) |
| out = mx.symbol.Activation(data=out, act_type='relu') |
| out = mx.symbol.FullyConnected(data=out, name='fc2', num_hidden=10) |
| return out |
| |
| |
| |
| def conv(): |
| data = mx.symbol.Variable('data') |
| 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') |
| |
| conv2= mx.symbol.Convolution(data = mp1, name='conv2', num_filter=32, kernel=(3,3), stride=(2,2)) |
| bn2 = mx.symbol.BatchNorm(data = conv2, name="bn2") |
| act2 = mx.symbol.Activation(data = bn2, name='relu2', act_type="relu") |
| mp2 = mx.symbol.Pooling(data = act2, name = 'mp2', kernel=(2,2), stride=(2,2), pool_type='max') |
| |
| fl = mx.symbol.Flatten(data = mp2, name="flatten") |
| fc2 = mx.symbol.FullyConnected(data = fl, name='fc2', num_hidden=10) |
| softmax = mx.symbol.SoftmaxOutput(data = fc2, name = 'sm') |
| return softmax |
| |
