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stt_layer_conv.py
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stt_layer_conv.py
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import mxnet as mx
def conv(net,
channels,
filter_dimension,
stride,
weight=None,
bias=None,
act_type="relu",
no_bias=False,
name=None
):
# 2d convolution's input should have the shape of 4D (batch_size,1,seq_len,feat_dim)
if weight is None or bias is None:
# ex) filter_dimension = (41,11) , stride=(2,2)
net = mx.sym.Convolution(data=net, num_filter=channels, kernel=filter_dimension, stride=stride, no_bias=no_bias,
name=name)
elif weight is None or bias is not None:
net = mx.sym.Convolution(data=net, num_filter=channels, kernel=filter_dimension, stride=stride, bias=bias,
no_bias=no_bias, name=name)
elif weight is not None or bias is None:
net = mx.sym.Convolution(data=net, num_filter=channels, kernel=filter_dimension, stride=stride, weight=weight,
no_bias=no_bias, name=name)
else:
net = mx.sym.Convolution(data=net, num_filter=channels, kernel=filter_dimension, stride=stride, weight=weight,
bias=bias, no_bias=no_bias, name=name)
net = mx.sym.Activation(data=net, act_type=act_type)
return net