sravanbabuiitm opened a new issue #11829: Diff behavior of GlobalAvgPool1D compared to keras URL: https://github.com/apache/incubator-mxnet/issues/11829 I m trying to understand the functionality of https://mxnet.incubator.apache.org/api/python/gluon/nn.html#mxnet.gluon.nn.GlobalAvgPool1D The documentation doesnt seem complete for this, but I have noted discrepancy compared to the behavior of same api in keras. model = Sequential() model.add(Embedding(vocab_size, embedding_dims, weights=[embedding_matrix], input_length=max_len_doc, trainable=False)) model.add(GlobalAveragePooling1D()) model.add(Dense(n_labels, activation='sigmoid')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() Embedding layer followed by global average pooling layer summed along the column/features. Build model... _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_2 (Embedding) (None, 65, 300) 4239000 _________________________________________________________________ global_average_pooling1d_2 ( (None, 300) 0 _________________________________________________________________ dense_2 (Dense) (None, 3) 903 ================================================================= Total params: 4,239,903 Trainable params: 903 Non-trainable params: 4,239,000 ____________________________________________ In Gluon, I have tried the same operation : embedding = nn.Embedding(20,5,sparse_grad=True, weight_initializer = mx.init.Uniform()) embedding.initialize() pooling = gluon.nn.GlobalAvgPool1D() print(embedding(mx.nd.array([[1,3,7]]))) print(pooling(embedding(mx.nd.array([[1,3,7]])))) Output : [[[ 0.05667423 -0.02676572 0.0301794 0.02835616 -0.0437789 ] [ 0.01422429 0.01972841 -0.05923161 -0.01276907 -0.06303393] [ 0.05422723 0.04757585 0.00387447 -0.01476508 0.06609883]]] <NDArray 1x3x5 @cpu(0)> [[[ 0.00893304] [-0.02021638] [ 0.03140226]]] <NDArray 1x3x1 @cpu(0)> I was expecting to see 1X1X5 as output. It isn't even summing along the rows since when I did the following : np.sum([ 0.05667423, -0.02676572, 0.0301794, 0.02835616, -0.0437789 ]) output : 0.04466516999999998 can you add more documentation to the API and also on how it is different from libraries offering same API's ?
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