My Tensorflow non-stacked LSTM model code works well for this:
# reshape input to be [samples, time steps, features]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(1, LOOK_BACK)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=EPOCHS, batch_size=1, verbose=2)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
If I try to sequentially add more layers with using this:
# create and fit the LSTM network
model = Sequential()
batch_size = 1
model.add(LSTM(4, batch_input_shape=(batch_size, LOOK_BACK, 1), stateful=True, return_sequences=True))
model.add(LSTM(4, batch_input_shape=(batch_size, LOOK_BACK, 1), stateful=True))
This will error out:
ValueError: Input 0 of layer "lstm_6" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 4)
Another similar SO post any tips appreciated not alot of wisdom here.
