ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=4

Question

I am trying for multi-class classification and here are the details of my training input and output:

train_input.shape= (1, 95000, 360) (95000 length input array with each element being an array of 360 length)

train_output.shape = (1, 95000, 22) (22 Classes are there)

model = Sequential()

model.add(LSTM(22, input_shape=(1, 95000,360)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(train_input, train_output, epochs=2, batch_size=500)

The error is:

ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=4 in line: model.add(LSTM(22, input_shape=(1, 95000,360)))

Please help me out, I am not able to solve it through other answers.

Answer 1

I solved the problem by making

input size: (95000,360,1) and output size: (95000,22)

and changed the input shape to (360,1) in the code where model is defined:

model = Sequential()
model.add(LSTM(22, input_shape=(360,1)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(ml2_train_input, ml2_train_output_enc, epochs=2, batch_size=500)

Answer 2

input_shape is supposed to be (timesteps, n_features). Remove the first dimension.

input_shape = (95000,360)

Same for the output.

Answer 3

Well, I think the main problem out there is with the return_sequences parameter in the network.This hyper parameter should be set to False for the last layer and true for the other previous layers.