import keras
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score

#乱数発生
x = np.random.randn(100)
y = 5.2*x + np.random.randn(100)*0.8

#４個のニューロンからなる2層を構築
model = Sequential()

#活性化関数はlinearとする
model.add(Dense(4, input_dim = 1, activation = 'linear', name = 'layer_1'))
model.add(Dense(1, activation = 'linear', name = 'layer_2'))
print (model.summary())
model.compile(optimizer = 'sgd', loss = 'mse', metrics = ['accuracy'])

#学習前の重みと閾値の出力
print("Weights and biases of the layers before training the model: \n")
for layer in model.layers:
  print(layer.name)
  print("Weights")
  print("Shape: ",layer.get_weights()[0].shape,'\n',layer.get_weights()[0])
  print("Bias")
  print("Shape: ",layer.get_weights()[1].shape,'\n',layer.get_weights()[1],'\n')

#ニューラルネットワークの訓練
model.fit(x,y, batch_size = 1, epochs = 100, shuffle = False)
yhat = model.predict(x)

#学習後の重みと閾値の出力
print("Weights and biases of the layers after training the model with new weights and biases: \n")
for layer in model.layers:
  print(layer.name)
  print("Weights")
  print("Shape: ",layer.get_weights()[0].shape,'\n',layer.get_weights()[0])
  print("Bias")
  print("Shape: ",layer.get_weights()[1].shape,'\n',layer.get_weights()[1],'\n')

print("R2値=", r2_score(y, yhat))

plt.figure(figsize = (8,8))
plt.xlabel('Input Variable (x)')
plt.ylabel('Output Variable (y)')
plt.plot(x,y,'o',x,model.predict(x),'k')
plt.show()