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

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)

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

input_shape = (95000,360)

Same for the output.

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.