Unrecognized keyword arguments passed to LSTM: {'batch_input_shape'

[Ineedsomehelpah]

model = Sequential()
model.add(LSTM(4, batch_input_shape=(1, X_train.shape[1], X_train.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=1, shuffle=False)

ValueError: Unrecognized keyword arguments passed to LSTM: {'batch_input_shape': (1, 1, 7)}

My version :
TensorFlow version: 2.17.0
Keras version: 3.4.1

I've seen similar issue raised on stackoverflow. I was able to run the code 2 weeks ago without error. What new keyword argument should I use?

https://stackoverflow.com/questions/78805181/valueerror-unrecognized-keyword-arguments-passed-to-lstm-batch-input-shape

[Grvzard]

That's the usage in Keras 2. In Keras 3, you can refer to:

keras/keras/src/layers/rnn/rnn.py

Lines 105 to 110 in 933579d

	To enable statefulness:
	- Specify `stateful=True` in the layer constructor.
	- Specify a fixed batch size for your model, by passing
	If sequential model:
	`batch_input_shape=(...)` to the first layer in your model.

[newresu]

@Ineedsomehelpah

Just adding up to the previous comment. Note that:

1. Input layer was missing the description for the parameter batch_shape in 3.4.1. And there is still the need to update RNN's description if I understand correctly, otherwise it'd still fail.
2. . You could do:

import keras
from keras.models import Sequential
from keras.layers import LSTM, Dense, Input

X_train = keras.random.normal((50,20,3))
y_train = keras.random.normal((50, 1))

model = Sequential()
model.add(Input(batch_shape=X_train.shape))
model.add(LSTM(units=4, stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=100, batch_size=1,)

[sachinprasadhs]

Here is the code with slight modification that works with batch_input_shape

import keras
from keras.models import Sequential
from keras.layers import LSTM, Dense, Input, InputLayer

X_train = keras.random.normal((50,20,3))
y_train = keras.random.normal((50, 1))

model = Sequential()
model.add(InputLayer(batch_input_shape=X_train.shape))
model.add(LSTM(units=4, stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=100, batch_size=1,)

[Ineedsomehelpah]

Really appreciate all your feedback, it's working for me now. Thanks all

[sachinprasadhs]

Thanks for confirming, could you please close the issue as well.

[google-ml-butler]

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