keras.py

import keras
from keras.layers import Dense, Activation
from keras.models import Sequential
import matplotlib.pyplot as plt 
import numpy as np

xx = ipdata = np.linspace(1,2,100)
yy = xx*4 + np.random.randn(*xx.shape) * 0.3


modeldef = Sequential()
modeldef.add(Dense(1, input_dim=1, activation='linear'))

modeldef.compile(optimizer='sgd', loss='mse', metrics=['mse'])

weight = modeldef.layers[0].get_weights()
wInit = weight[0][0][0]
bInit = weight[1][0]
print('Linear regression model is initialized with weights w: %.2f, b: %.2f' % (wInit, bInit)) 


modeldef.fit(xx,yy, batch_size=1, epochs=30, shuffle=False)

weight = modeldef.layers[0].get_weights()
wFinal = weight[0][0][0]
bFinal = weight[1][0]
print('Linear regression model is trained to have weight w: %.2f, b: %.2f' % (wFinal, bFinal))

pred = modeldef.predict(ipdata)

plt.plot(data, pred, 'b', ipdata , yy, 'k.')
plt.show()

Line 31 should be plt.plot(ipdata, pred, 'b', ipdata , yy, 'k.') not plt.plot(data, pred, 'b', ipdata , yy, 'k.')