#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Maziar Raissi
"""
import autograd.numpy as np
def stochastic_update_Adam(w,grad_w,mt,vt,lrate,iteration):
beta1 = 0.9;
beta2 = 0.999;
epsilon = 1e-8;
mt = mt*beta1 + (1.0-beta1)*grad_w;
vt = vt*beta2 + (1.0-beta2)*grad_w**2;
mt_hat = mt/(1.0-beta1**iteration);
vt_hat = vt/(1.0-beta2**iteration);
scal = 1.0/(np.sqrt(vt_hat) + epsilon);
w = w - lrate*mt_hat*scal;
return w,mt,vt
def Normalize(X, X_m, X_s):
return (X-X_m)/(X_s)
def Denormalize(X, X_m, X_s):
return X_s*X + X_m
def fetch_minibatch(Y,N_batch):
N = Y.shape[0]
idx = np.random.choice(N, N_batch, replace=False)
Y_batch = Y[idx,:]
return Y_batch
def activation(x):
return np.tanh(x)
