I am making a sequential neural network for classification, with 3 dense layers, which will be trained on a simple synthetic dataset. The description of dataset is as follows:
- Data and class labels are integers. They are 2000 each.
- There is only a single feature column (populated by np.arange(2000) * 3)
- There is only a single label which indicates last digit of number (populated by np.arange(2000) *3 % 10).
After making the model, I am encountering the following error when calling model.fit():
ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 1500 but received input with shape (100, 1)
I have uploaded the commented Jupyter Notebook for this code on Google Collab: https://colab.research.google.com/drive/14v92NTBxIEIFJh2BhybfqhawHYIBvKnm?usp=sharing
Any suggestion about how to fix this error and get reasonable accuracy on training set?