Solve the problem of fetching prediction and make data dim configurable #772
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kuke
changed the title
pkuyym
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Mar 26, 2018
| @@ -32,6 +32,11 @@ def parse_args(): | |||
| default=1, | |||
| help='The minimum sequence number of a batch data. ' | |||
| '(default: %(default)d)') | |||
| parser.add_argument( | |||
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@zhxfl Seems this value should be calculated by data reader.
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There is a contradiction: After the data reader really begins to read data, it knows the actual dimensions of frame and label data. But before that, the network has been parsed and the right dimensions of feeding data are needed in advance. So the better way is to set this dimension manually.
| @@ -47,6 +52,11 @@ def parse_args(): | |||
| type=int, | |||
| default=1024, | |||
| help='Hidden size of lstmp unit. (default: %(default)d)') | |||
| parser.add_argument( | |||
| '--class_num', | |||
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Explained in last comment.
fluid/DeepASR/model_utils/model.py
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| @@ -20,6 +21,7 @@ def stacked_lstmp_model(hidden_dim, | |||
| label data respectively. And in inference, only `feature` is needed. | |||
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| Args: | |||
| frame_dim(int): The frame dimension of feature data. | |||
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Fix #771