def GetGradient(data_x_win,model,perturbation):
y_pred = model.output
y_true = K.variable(np.array(df_YY_actual.iloc[0]))
#ten_x_scale = K.variable(np.array(data_x_win[0]))
loss = keras.losses.mean_squared_error(y_true, y_pred)
grads = K.gradients(loss,model.input)[0]
x_adv = K.sign(grads)
sess =K.get_session()
init = tf.compat.v1.global_variables_initializer()
sess.run(init)
x_adv_0 = x_adv[0]
adv = []
if len(Y) != len(data_x_win):
print("WARNING!!!! Unequal length of X and Y")
#len(df_x_scale)
for i in range(len(data_x_win)):
adv_i = sess.run(x_adv_0, feed_dict={model.input:[data_x_win[i]],y_true:np.array(df_YY_actual.iloc[i])})
if i%1000 == 0:
print(i)
print(datetime.datetime.now().isoformat())
df_grd_i = pd.DataFrame(adv_i,columns = header)
adv.append(np.array(df_grd_i))
return adv
def _signed_sqrt(x):
'''Calculate element-wise signed square-root.
Args:
x: input tensor.
Returns:
Element-wise signed square-root tensor.
'''
return keras_backend.sign(x) * keras_backend.sqrt(keras_backend.abs(x) + 1e-9)
def loss(y_true, y_pred, quantile=quantile):
from tensorflow.python.keras import backend as K
e = y_pred - y_true
Ie = (K.sign(e) + 1) / 2
return K.mean(e * (Ie - quantile), axis=-1)
def _signed_sqrt(x):
sign = Backend.sign(x)
sqrt = Backend.sqrt(Backend.abs(x) + 1e-9)
return sign * sqrt
def scale_noise(self, noise):
return K.sign(noise) * K.sqrt(K.abs(noise))
def signed_sqrt(x):
return sign(x) * sqrt(abs(x) + 1e-9)
