Predicting next numbers in sequence Keras - Python

Question

I'm new to python and neural networks. I have a simple network written in Keras that can predict the next number in a linear sequence:

import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

data = [[i for i in range(6)]];
data = np.array(data, dtype=int);
target = [[i for i in range(10, 16)]];
target = np.array(target, dtype=int);

model = Sequential();
model.add(Dense(1, input_dim=1))
model.add(Dense(1));
model.compile(loss='mean_absolute_error', optimizer = 'adam', metrics = ['accuracy']);
model.summary();
for i in range (10000):
    dataIterator = 0;
    for targetValue in target:
        model.train_on_batch(data[dataIterator], targetValue)
        dataIterator = dataIterator + 1;

predict = model.predict([28]);
print(predict);

Gives me output:

[[38.0199]]

And that is to be expected. I'm not sure if my code has some stupid errors in it and would appreciate any feedback and explanations. I used Dense because I'm not sure what LSTM exacly does. Another thing is that my model requires input to have 2 dimensions when it's specified:

input_dim=1

I don't understand why. Next, I would like to create a network that can predict the next numbers in a sequence like [1, 4, 9, 16, 25]. This one doesn't.

Note that this is my first program written in Python and first use of neural networks :). Thanks in advance!

UPDATE 1

According to tip of using scale i came up with something like this:

import numpy as np

from keras.models import Sequential
from pandas import Series
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import StandardScaler

data = [[i for i in range (1,30)]];
data = np.array(data, dtype=int);
target = np.power(data, 2);
target = np.array(target, dtype=int);
target = target.reshape((len(target[0]), 1))
data = data.reshape((len(data[0]), 1))

scale = StandardScaler()

dataTest = [[i for i in range (2,4)]];
dataTest = np.array(dataTest, dtype=int);
dataTest = dataTest.reshape((len(dataTest[0]), 1))

model = Sequential();
model.add(Dense(1, input_dim=1))
model.add(Dense(1));
model.compile(loss='mean_absolute_error', optimizer = 'adam');
model.fit(scale.fit_transform(data), target, batch_size=1, epochs=200, verbose=1)

print(model.predict(scale.transform(dataTest)));

Despite this predictions are way off ideal. For given test data, output:

[[27.616932]
 [28.265278]]

I'm out of ideas at the moment :(. Not feeling it at all.

Answer 1

And that is to be expected. I'm not sure if my code has some stupid errors in it, would appreciate any feedback and explanations. I used Dense because im not sure what LSTM exactly does.

LSTM , which stands for Long-short term memory , are a special kind of RNN , capable of learning long-term dependencies. Also, its use mainly sequential processing over time. For instance, you can use LSTM when you want to predict the Google Stock prices .

Since you have to predict next numbers in sequence like [1, 4, 9, 16, 25] that's means it's a regression learning system model, which belongs to supervised learning. When you're using regression models, there is no accuracy . The accuracy from regression models it's called COD or Coefficient of determination or R squared score .

The metric that you use- metrics=['accuracy'] corresponds to a classification problem. If you want to do regression , remove metrics=['accuracy'] . That is, just use

model.compile(optimizer = 'adam',loss = 'mean_absolute_error')