CODEWITHSUNDEEP
pythontensorflowkeras
Arpith S
Arpith S

Reputation: 121

Feeding input into keras Sequential model

I'm working on time series prediction with Keras. I have 10 timesteps in my input data which is of the shape (2688, 10, 1) i.e train_x.shape and train_y.shape is (2688, 10, 1). I am getting the following error while I try to feed it into the model.

ValueError: Error when checking target: expected activation_1 to have 2 dimensions, but got array with shape (2688, 10, 1)

The input shape that I am giving to the first lstm layer: input_shape=(1, time_steps) **

Am i reshaping train_y properly?

**

    time_steps=10
    train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 1))
    train_y = np.reshape(train_y, (train_y.shape[0], train_y.shape[1], 1))

    # lstm model
    model = Sequential()
    model.add(LSTM(128, input_shape=(time_steps, 1), return_sequences=True))
    model.add(LSTM(64))
    model.add(Dense(1))
    model.add(Activation('linear'))

    model.compile(loss='mse', optimizer='adam')
    history = model.fit(train_x, train_y, epochs=10, validation_data=(test_x, 
    test_y), batch_size=64, verbose=1)

Upvotes: 1

Views: 851

Answers (2)

Fasty
Fasty

Reputation: 804

train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 1))
train_y = np.reshape(train_y, (train_y.shape[0], train_y.shape[1], 1)

I think the error is here, you should have given the time steps between shape[0] and shape[1] i.e...

train_x = np.reshape(train_x, (train_x.shape[0], 10,train_x.shape[1]))
train_y = np.reshape(train_y, (train_y.shape[0],10, train_y.shape[1]))

Here the value '10' denotes the time steps!

Upvotes: 1

Hasitha Amarathunga
Hasitha Amarathunga

Reputation: 2005

if you expected shape (2688, 10, 1) then it cant be input_shape=(1, time_steps). It should input_shape=(time_steps,1)

Upvotes: 0

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