In [6]:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Activation
from keras.layers.convolutional import ZeroPadding2D
from keras.layers import Dropout
from keras.optimizers import Adadelta
from keras.optimizers import SGD
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.utils import np_utils
import numpy as np
import time
import matplotlib.pyplot as plt
import keras
In [7]:
x = np.array([0, 1, 2, 3, 4])
y = x * 2 + 1
model = keras.models.Sequential()
model.add(keras.layers.Dense(1, input_shape=(1,)))
model.compile('SGD', 'mse')
model.fit(x[:2], y[:2], epochs=1000, verbose=0)
print('Expected:', y[2:])
print('Predicted:', model.predict(x[2:]).flatten())
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