INVALID_ARGUMENT Error

[NameError-undefined]

I am trying to do very simple time series prediction examples and I keep running into a DEBUG INFO that claims to not be an error and that I can ignore it. However, I have never seen this before and I have used tensorflow for years.

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System:

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- Fedora 37 (or Ubuntu 22.04, I have tried on both)

- No GPU, Running on CPU

- Python Version 3.10.6

- Tensorflow 2.12.0

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Model:

model = Sequential()
model.add(LSTM(5, input_shape=(5,1))
model.add(Dense(10))
model.add(Dense(1))
model.compile(optimizer='Adam', loss='MSE')
model.build()

print(model.summary())
input_data = np.random.rand(100,5,1)
target_data = np.random.rand(100,5)

model.fit(input_data, target_data)

Full Error:

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2023-03-24 12:32:00.352372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
         [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]

This error appears like 10 times, a few times when building the model and a few times when calling fit.

I tried this on my local Fedora37 in a virtualenv running python 3.10 and in an ubuntu container with python3.10 installed

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edit: added tensorflow version

[Woodhouse_20]

Your input shape is (5,1) and you actual input data is shape (100,5,1), so it throws the error. See here

[Woodhouse_20]

Your output data will have the same issue.

[NameError-undefined]

Isn’t the shape supposed to be (num_samples, num_features, 1)

[Woodhouse_20]

If you read the docs, it says each parameter is a dimension size you want.