Resolving "Input 0 of layer "bi_lstm_6" is incompatible with the layer" Error in TensorFlow 2.0

When working with recurrent neural networks (RNNs) in TensorFlow 2.0, you may encounter the following error:

Input 0 of layer "bi_lstm_6" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 16)

This error indicates that the input data provided to the RNN layer has an incorrect shape. RNNs expect input data to have three dimensions:

• Batch size: The number of samples in the batch.
• Sequence length: The length of each sequence in the batch.
• Feature dimension: The number of features in each sequence element.

In this case, the error message suggests that the input data has only two dimensions, indicating that the sequence length is missing.

Solution

To resolve this error, you need to reshape your input data to have three dimensions. This can be done using the tf.expand_dims() function. Here's an example:

# Original input data
data = tf.constant([[1, 2, 3], [4, 5, 6]])

# Reshape the data to add a sequence length dimension
data = tf.expand_dims(data, axis=1)

# New shape of the data
print(data.shape)  # Output: (2, 1, 3)

After reshaping the data, you should be able to pass it to the RNN layer without encountering the incompatibility error.

Additional Tips

Make sure that the sequence length dimension is the second dimension of the input data.

If you are using a Bidirectional LSTM (BiLSTM) layer, the sequence length dimension should be the same for both the forward and backward LSTMs.

You can use the tf.keras.layers.RNN layer to create RNN layers in TensorFlow 2.0. This layer automatically handles the reshaping of the input data, making it easier to work with RNNs.

Conclusion

The "Input 0 of layer "bi_lstm_6" is incompatible with the layer" error in TensorFlow 2.0 is typically caused by an incorrect shape of the input data. By reshaping the input data to have three dimensions using the tf.expand_dims() function, you can resolve this error and successfully use RNN layers in your TensorFlow 2.0 models.