| import mxnet as mx |
| |
| from negativesample import NegativeSamplingDataIter |
| import randomproj |
| import crossentropy |
| |
| def CosineLoss(a, b, label): |
| a = mx.symbol.L2Normalization(a) |
| b = mx.symbol.L2Normalization(b) |
| dot = a * b |
| dot = mx.symbol.sum_axis(dot, axis=1) |
| dot = mx.symbol.Flatten(dot) |
| cosine = 1 - dot |
| return mx.symbol.MAERegressionOutput(data=cosine, label=label) |
| |
| def SparseRandomProjection(indexes, values, input_dim, output_dim, ngram=1): |
| return mx.symbol.Custom(indexes=indexes, values=values, vocab_size=input_dim, |
| output_dim=output_dim, op_type='SparseRandomProjection') |
| |
| def SparseBagOfWordProjection(data, vocab_size, output_dim, ngram=1): |
| return mx.symbol.Custom(indexes=data, vocab_size=vocab_size, |
| output_dim=output_dim, op_type='SparseBOWProj') |
| |
| def CrossEntropyLoss(data, label): |
| return mx.symbol.Custom(data=data, label=label, |
| op_type='CrossEntropyLoss') |
| |
