CODEWITHSUNDEEP
pythontensorflowkerasdeep-learning
OK 400
OK 400

Reputation: 831

Error when checking input: expected lstm_input to have 3 dimensions, but got array with shape (4, 1)

First of all, I know there are tons of questions similar like this; I've tried to do what the answers suggest, but seems like I do not know how to solve it. I have a Keras Functional API model:

lstm_input = keras.layers.Input(shape=(1,4), name='lstm_input')
x = keras.layers.LSTM(50, name='lstm_0')(lstm_input)
x = keras.layers.Dropout(0.2, name='lstm_dropout_0')(x)
x = keras.layers.Dense(64, name='dense_0')(x)
x = keras.layers.Activation('sigmoid', name='sigmoid_0')(x)
x = keras.layers.Dense(1, name='dense_1')(x)
output = keras.layers.Activation('linear', name='linear_output')(x)
model = keras.Model(inputs=lstm_input, outputs=output)

adam = keras.optimizers.Adam(lr=0.0005)
model.compile(optimizer=adam, loss='mse')

And when I try to fit it, it jumps this error:

ValueError: Error when checking input: expected lstm_input to have 3 dimensions, but got array with shape (4, 1)

This is my call to fit:

model.fit(X_aux['X_i'], X[i+1, 0])
# X_aux['X_i'].shape = (4, ) -- it's a numpy array

I've tried np.reshape([X_aux['X_i1']], (4,1)), where its new shape is (4, 1) but it does not work. How can I solve this?

Upvotes: 0

Views: 756

Answers (1)

Chompakorn CChaichot
Chompakorn CChaichot

Reputation: 309

Make sure your input_shape of X_aux['X-i'] is 3 dimensional.

The input of any RNN-based layer must be 3 dimensional where each axis is corresponded to batch_size, time_step, and feature dimension respectively.

The reason why reshaping to (4, 1) wouldn't help is that the reshaped tensor is still 2 dimension. You need 3.

Make sure you define batch_size, time_step, and feature dimension correctly and reshape X_aux['X-i'] and retrain the model again.

Upvotes: 1

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