Comments (2)
FlorentF9
commented on July 16, 2024
From the top of my head it should be (N, 25000, 17), with N the number of inputs. So, (1, 25000, 17) for inference on a single series.
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jayanthchennamaneni
commented on July 16, 2024
Thanks for replying, I tried using in the way as you specified above but my time steps are reducing but not the features
my input shape was (1, 25000,17)--1series, 25000 time steps, 17 features
the output I get when I predict is (1, 4500, 1)
I tried using Basic dense auto encoders , I had no problem with the shape.
is it happening because of conv1D???
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Related Issues (20)
- Dimension Reduction HOT 7
- Assertion error HOT 5
- Heatmap HOT 3
- Nan: Predicted Value HOT 2
- ValueError: The name "reshape" is used 2 times in the model. All layer names should be unique. HOT 3
- CuDNNLSTM not found HOT 3
- Training and Validation Losses HOT 1
- ValueError: Input 0 is incompatible with layer AE: expected shape=(None, 5210, 6), found shape=(None, 6) HOT 3
- Problem with Autoencoder Dimensions HOT 2
- Heatmap use HOT 2
- how to load model.h5 HOT 2
- About the loss value HOT 2
- Heatmap issue HOT 4
- Loss interpretation
- Agglomerative Clustering without n_clusters HOT 1
- Dependency Problems with cudnn and Tensorflow HOT 1
- Practical Use
- Requirements are hard to find out HOT 1
- variable time step HOT 4
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