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commit 8fdcb8541b6690ac2d81a1ebcadbcf31adf45910
Author: ThomasDelteil <thomas.delte...@gmail.com>
AuthorDate: Wed Apr 25 15:33:58 2018 -0700
Update rnn_layer.py
---
python/mxnet/gluon/rnn/rnn_layer.py | 22 ++++++++++------------
1 file changed, 10 insertions(+), 12 deletions(-)
diff --git a/python/mxnet/gluon/rnn/rnn_layer.py
b/python/mxnet/gluon/rnn/rnn_layer.py
index 21b41d9..59dd747 100644
--- a/python/mxnet/gluon/rnn/rnn_layer.py
+++ b/python/mxnet/gluon/rnn/rnn_layer.py
@@ -285,10 +285,10 @@ class RNN(_RNNLayer):
Inputs:
- **data**: input tensor with shape `(sequence_length, batch_size,
input_size)`
- when `layout` is "TNC". For other layouts dimensions are permuted
accordingly.
- Be aware that a `transpose` operation with a ndarray results in a
new allocation of
- memory. For optimal performance and when applicable, consider
transposing
- your layout to "TNC" before loading your data into a ndarray.
+ when `layout` is "TNC". For other layouts, dimensions are permuted
accordingly
+ using transpose() operator which adds performance overhead. Consider
creating
+ batches in TNC layout during data batching step.
+
- **states**: initial recurrent state tensor with shape
`(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,
shape will instead be `(2*num_layers, batch_size, num_hidden)`. If
@@ -388,10 +388,9 @@ class LSTM(_RNNLayer):
Inputs:
- **data**: input tensor with shape `(sequence_length, batch_size,
input_size)`
- when `layout` is "TNC". For other layouts dimensions are permuted
accordingly.
- Be aware that a `transpose` operation with a ndarray results in a
new allocation of
- memory. For optimal performance and when applicable, consider
transposing
- your layout to "TNC" before loading your data into a ndarray.
+ when `layout` is "TNC". For other layouts, dimensions are permuted
accordingly
+ using transpose() operator which adds performance overhead. Consider
creating
+ batches in TNC layout during data batching step.
- **states**: a list of two initial recurrent state tensors. Each has
shape
`(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,
shape will instead be `(2*num_layers, batch_size, num_hidden)`. If
@@ -488,10 +487,9 @@ class GRU(_RNNLayer):
Inputs:
- **data**: input tensor with shape `(sequence_length, batch_size,
input_size)`
- when `layout` is "TNC". For other layouts dimensions are permuted
accordingly.
- Be aware that a `transpose` operation with a ndarray results in a
new allocation of
- memory. For optimal performance and when applicable, consider
transposing
- your layout to "TNC" before loading your data into a ndarray.
+ when `layout` is "TNC". For other layouts, dimensions are permuted
accordingly
+ using transpose() operator which adds performance overhead. Consider
creating
+ batches in TNC layout during data batching step.
- **states**: initial recurrent state tensor with shape
`(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,
shape will instead be `(2*num_layers, batch_size, num_hidden)`. If
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