import torch
from torch.nn import Parameter
from torch.autograd import Variable
import torch.nn.functional as F
torch.manual_seed(9)

#### create RNN cell ###########
rnn_a = torch.nn.RNNCell(input_size=1,hidden_size=1,bias=False)

#### Print All Parameter in RnnCell ###########
for weight in rnn_a.parameters():
    print(weight)

#### Create Input (batch,sequence,feature vector) ###########
random_input = Variable(torch.FloatTensor(1, 3, 1).normal_(), requires_grad=False)  #batch,sequence,feature
random_input[0,0,0] = 1
random_input[:, 0, 0]
h0 = Variable(torch.zeros(1,1),requires_grad=False)
print(random_input)
print(h0)

#### Show Hidden_value in time step1 compare with manual calculate ###########
h1 = rnn_a(random_input[:,0,:], h0)
print(h1)
print("ht = tanh(0.3116) = ",F.tanh(torch.tensor(0.3116)))

#### Show Hidden_value in time step2 compare with manual calculate ###########
h2 = rnn_a(random_input[:,1,:], h1)
print(h2)
print("Manual calculate")
print( F.tanh(torch.tensor((0.3116)*(-0.7775) + (-0.3960) * (0.3019))))