P = df[1]
Y = df[2]
###
# Create and train NN
# create recurrent neural network with 1 input, 2 hidden layers with
# 2 neurons each and 1 output
# the NN has a recurrent connection with delay of 1 timestep in the hidden
# layers and a recurrent connection with delay of 1 and 2 timesteps from the output
# to the first layer
net = prn.CreateNN([1, 2, 2, 1], dIn=[0], dIntern=[1], dOut=[1, 2])
###
# Prepare input Data for gradient calculation
data, net = prn.prepare_data(P, Y, net)
###
# Calculate derivative vector (gradient vector)
# Real Time Recurrent Learning
t0_rtrl = time.time()
J, E, e = prn.RTRL(net, data)
g_rtrl = 2 * np.dot(J.transpose(),
e) # calculate g from Jacobian and error vector
t1_rtrl = time.time()
# Back Propagation Through Time
t0_bptt = time.time()
g_bptt, E = prn.BPTT(net, data)
t1_bptt = time.time()
P = df['P'].values Y = df['Y'].values ### #Create and train NN #create recurrent neural network with 1 input, 2 hidden layers with #2 neurons each and 1 output #the NN has a recurrent connection with delay of 1 timestep in the hidden # layers and a recurrent connection with delay of 1 and 2 timesteps from the output # to the first layer net = prn.CreateNN([1,2,2,1],dIn=[0],dIntern=[1],dOut=[1,2]) ### #Prepare input Data for gradient calculation data,net = prn.prepare_data(P,Y,net) ### #Calculate derivative vector (gradient vector) #Real Time Recurrent Learning t0_rtrl = time.time() J,E,e = prn.RTRL(net,data) g_rtrl = 2 * np.dot(J.transpose(),e) #calculate g from Jacobian and error vector t1_rtrl = time.time() #Back Propagation Through Time t0_bptt = time.time() g_bptt,E = prn.BPTT(net,data) t1_bptt = time.time()