| """ |
| LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner. |
| Gradient-based learning applied to document recognition. |
| Proceedings of the IEEE (1998) |
| """ |
| import mxnet as mx |
| |
| def get_symbol(num_classes=10, add_stn=False, **kwargs): |
| data = mx.symbol.Variable('data') |
| if(add_stn): |
| data = mx.sym.SpatialTransformer(data=data, loc=get_loc(data), target_shape = (28,28), |
| transform_type="affine", sampler_type="bilinear") |
| # first conv |
| conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=20) |
| tanh1 = mx.symbol.Activation(data=conv1, act_type="tanh") |
| pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max", |
| kernel=(2,2), stride=(2,2)) |
| # second conv |
| conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=50) |
| tanh2 = mx.symbol.Activation(data=conv2, act_type="tanh") |
| pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max", |
| kernel=(2,2), stride=(2,2)) |
| # first fullc |
| flatten = mx.symbol.Flatten(data=pool2) |
| fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500) |
| tanh3 = mx.symbol.Activation(data=fc1, act_type="tanh") |
| # second fullc |
| fc2 = mx.symbol.FullyConnected(data=tanh3, num_hidden=num_classes) |
| # loss |
| lenet = mx.symbol.SoftmaxOutput(data=fc2, name='softmax') |
| return lenet |
