example/sparse/linear_classification/linear_model.py

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import mxnet as mx
from weighted_softmax_ce import *

def linear_model(num_features, positive_cls_weight):
    # data with csr storage type to enable feeding data with CSRNDArray
    x = mx.symbol.Variable("data", stype='csr')
    norm_init = mx.initializer.Normal(sigma=0.01)
    # weight with row_sparse storage type to enable sparse gradient updates
    weight = mx.symbol.Variable("weight", shape=(num_features, 2),
                                init=norm_init, stype='row_sparse')
    bias = mx.symbol.Variable("bias", shape=(2,))
    dot = mx.symbol.sparse.dot(x, weight)
    pred = mx.symbol.broadcast_add(dot, bias)
    y = mx.symbol.Variable("softmax_label")
    model = mx.sym.Custom(pred, y, op_type='weighted_softmax_ce_loss',
                          positive_cls_weight=positive_cls_weight, name='out')
    return mx.sym.MakeLoss(model)