I am new to machine learning and lstm. I am referring this link LSTM for multistep forecasting for
Encoder-Decoder LSTM Model With Multivariate Input
Here is my dataset description after reshaping the train and test set.
print(dataset.shape) print(train_x.shape, train_y.shape) print((test.shape) (2192, 15) (1806, 14, 14) (1806, 7, 1) (364, 15)
In above I have
Here is my lstm model description:
def build_model(train, n_input): # prepare data train_x, train_y = to_supervised(train, n_input) # define parameters verbose, epochs, batch_size = 2, 100, 16 n_timesteps, n_features, n_outputs = train_x.shape, train_x.shape, train_y.shape # reshape output into [samples, timesteps, features] train_y = train_y.reshape((train_y.shape, train_y.shape, 1)) # define model model = Sequential() model.add(LSTM(200, activation='relu', input_shape=(n_timesteps, n_features))) model.add(RepeatVector(n_outputs)) model.add(LSTM(200, activation='relu', return_sequences=True)) model.add(TimeDistributed(Dense(100, activation='relu'))) model.add(TimeDistributed(Dense(1))) model.compile(loss='mse', optimizer='adam') # fit network model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose) return model
On evaluating the model, I am getting the output as:
Epoch 98/100 - 8s - loss: 64.6554 Epoch 99/100 - 7s - loss: 64.4012 Epoch 100/100 - 7s - loss: 63.9625
According to my understanding: (Please correct me if I am wrong)
Here my model accuracy is 63.9625 (by seeing the last epoch 100). Also, this is not stable since there is a gap between epoch 99 and epoch 100.
Here is my some basic doubt:
1) Please suggest to me how epoch and batch size above defined is related to gaining model accuracy. How its increment and decrement affect model accuracy?
2) Is my above-defined epoch, batch, n_input is correct for the model?
3) How I can increase my model accuracy. Is the above dataset size is good enough for this model?
Please suggest me as I am not able to link all this parameter and kindly help me in understanding how to achieve more accuracy by the above factor. Thanks!!