Keras Functional API issue with Input layer and first LSTM layer

Question

I am trying to create a Functional API as opposed to a Sequential API. I have built the model previously using the Sequential API, and it worked just fine. It is an LSTM, and I am having trouble with the batch_size going from the Input to the LSTM layer. The Sequential API was built as follows:

new_model = Sequential()
new_model.add(LSTM(n_neurons, batch_input_shape=(batch_size,train_X.shape[1], train_X.shape[2]), activation='tanh', stateful=True, return_sequences=True))
new_model.add(Dropout(0))
new_model.add(LSTM(n_neurons, batch_input_shape=(batch_size,train_X.shape[1], train_X.shape[2]), activation='tanh', stateful=True))
new_model.add(Dropout(0))
new_model.add(Dense(n_neurons1, activation='tanh'))
new_model.add(Dropout(0.1))
new_model.add(Dense(nm))
new_model.compile(loss='mse', optimizer=optimizer)

The above snippet works fine. The Functional API I am trying to get to work is as follows:

inp = Input(shape = (train_X.shape[1], train_X.shape[2]), batch_size = batch_size)
L1 = LSTM(n_neurons, batch_input_shape=(batch_size,train_X.shape[1], train_X.shape[2]), activation='tanh', stateful=True, return_sequences=True)(inp)
D1 = Dropout(0)(L1)
L2 = LSTM(n_neurons, batch_input_shape=(batch_size,train_X.shape[1], train_X.shape[2]), activation='tanh', stateful=True, return_sequences=True)(D1)
D2 = Dropout(0)(L2)
F1 = Dense(n_neurons1, activation='tanh')(D2)
D3 = Dropout(0.1)(F1)
out = Dense(nm)
new_model = Model(inp,out)
new_model.compile(loss='mse', optimizer=optimizer)

I get an error saying "Input() got an unexpected keyword argument 'batch_size", even though I know batch_size is an argument for the Input layer. Then, if I get rid of the argument, I get an error with the first LSTM layer saying:

"If a RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors:

• If using a Sequential model, specify the batch size by passing a batch_input_shape argument to your first layer.
• If using the functional API, specify the batch size by passing a batch_shape argument to your Input layer."

I have already tried updating tensorflow but that did not fix the Input() issue. Where do I go from here?

Answer 1

You describe passing a batch_size parameter via the functional API and getting an error suggesting "passing a batch_shape argument to your Input layer."

If you try changing batch_size = batch_size in your input layer to

batch_shape = (batch_size,train_X.shape[1], train_X.shape[2])

does that solve it?