simple keras

simple_keras.py

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# dataset 
trainX = np.array([1, 2 ,3 ,4 , 5 , 6 , 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])
trainY = np.array([3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72])

# create a model
model = Sequential()
model.add(Dense(8, input_dim=1, activation='relu'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, nb_epoch=1500, batch_size=2, verbose=2)

# try to predict 
dataPrediction = model.predict(np.array([64]))
print (int(dataPrediction[0][0])), '<--- Predicted number'
print (64*3,' <-- Correct answer \n')

dataPrediction = model.predict(np.array([56]))
print (int(dataPrediction[0][0])), '<--- Predicted number'
print (56*3,' <-- Correct answer \n')

dataPrediction = model.predict(np.array([345]))
print (int(dataPrediction[0][0])), '<--- Predicted number'
print (345*3,' <-- Correct answer \n')

dataPrediction = model.predict(np.array([456]))
print (int(dataPrediction[0][0])), '<--- Predicted number'
print (456*3,' <-- Correct answer \n')