TypeError: unsupported operand type(s) for +: ‘NoneType’ and ‘int’ in Keras

The error message TypeError: unsupported operand type(s) for +: 'NoneType' and 'int' typically occurs when there’s an attempt to perform arithmetic operations involving a None type object and an integer.

This error often arises in Keras when there’s an issue with the shape of the input data or model configuration. Specifically, it might be related to how the input shape is specified in the model.

Here are a few steps to troubleshoot and potentially resolve this issue:

1. Check Input Shape: Ensure that the input shape specified in your model matches the shape of your input data. If you’re using a recurrent neural network (RNN) or any other type of sequence model, the input shape should include the sequence length and the number of features for each time step.
2. Verify Data Preparation: Make sure your data preparation code is correctly constructing the input sequences (x_train) and corresponding output sequences (y_train). The input sequences should have the shape (batch_size, time_steps, num_features).
3. Inspect Model Layers: Double-check the configuration of your Keras model, especially the first layer. Ensure that the input shape specified in the first layer matches the shape of your input data.
4. Debugging: Add print statements or use a debugger to inspect the shapes of tensors and variables at different stages of your code. This can help identify where the NoneType object is coming from.

Here’s an example of how you might define a simple RNN model in Keras:

from keras.models import Sequential
from keras.layers import SimpleRNN, Dense

model = Sequential()
model.add(SimpleRNN(units=64, input_shape=(3, 1)))  # Assuming input sequences of length 3 with 1 feature
model.add(Dense(units=1))  # Output layer

model.compile(optimizer='adam', loss='mse')

In this example, input_shape=(3, 1) indicates that each input sequence has a length of 3 time steps and each time step has 1 feature.