Understanding epoch, batch size, accuracy and performance gain in lstm forecasting model

Question

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 section.

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 n_input=14, n_out=7.

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[1], train_x.shape[2], train_y.shape[1]
    # reshape output into [samples, timesteps, features]
    train_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 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 are my questions:

How epoch and batch size above defined is related to gaining model accuracy? How its increment and decrement affect model accuracy?
Is my above-defined epoch, batch, n_input is correct for the model?
How can I increase my model accuracy? Is the above dataset size is good enough for this model?

I am not able to link all this parameter and kindly help me in understanding how to achieve more accuracy by the above factor.

chileshe chileshe · Accepted Answer · 2019-04-20T06:39:05

Having a very large epoch size will not necessarily improve your accuracy. Epoch sizes can increase the accuracy up to a certain limit beyond which you begin to overfit your model. Having a very low one will also result in underfitting. See this. So looking at the huge difference between epoch 99 and epoch 100, you can already tell that you are overfitting the model. As a rule of thumb, when you notice the accuracy stops increasing, that is the ideal number of epochs you should have usually between 1 and 10. 100 seems too much already.

Batch size does not affect your accuracy. This is just used to control the speed or performance based on the memory in your GPU. If you have huge memory, you can have a huge batch size so training will be faster.

What you can do to increase your accuracy is: 1. Increase your dataset for the training. 2. Try using Convolutional Networks instead. Find more on convolutional networks from this youtube channel or in a nutshell, CNN's help you identify what features to focus on in training your model. 3. Try other algorithms.

Understanding epoch, batch size, accuracy and performance gain in lstm forecasting model

2 Answers