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Update of Elman_RNN.py to pytorch 1.7.0 #6

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Update of Elman_RNN.py to pytorch 1.7.0 #6

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46bd19a
Update of Elman_RNN.py to pytorch 1.7.0
benelot Jan 12, 2021
d8a7f85
Remove context_state copying (useless in new version)
benelot Jan 12, 2021
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35 changes: 14 additions & 21 deletions elman_rnn/Elman_RNN.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,12 @@

import torch
from torch.autograd import Variable
import numpy as np
import torch.nn as nn
import pylab as pl
import torch.nn.init as init


torch.manual_seed(1)

dtype = torch.FloatTensor
dtype = torch.float32
input_size, hidden_size, output_size = 7, 6, 1
epochs = 200
seq_length = 20
Expand All @@ -17,54 +16,48 @@
data = np.sin(data_time_steps)
data.resize((seq_length + 1, 1))

x = Variable(torch.Tensor(data[:-1]).type(dtype), requires_grad=False)
y = Variable(torch.Tensor(data[1:]).type(dtype), requires_grad=False)
x = torch.tensor(data[:-1], dtype=dtype, requires_grad=False)
y = torch.tensor(data[1:], dtype=dtype, requires_grad=False)

w1 = torch.normal(0.0, 0.4, size=(input_size, hidden_size), dtype=dtype, requires_grad=True)
w2 = torch.normal(0.0, 0.3, size=(hidden_size, output_size), dtype=dtype, requires_grad=True)

w1 = torch.FloatTensor(input_size, hidden_size).type(dtype)
init.normal(w1, 0.0, 0.4)
w1 = Variable(w1, requires_grad=True)
w2 = torch.FloatTensor(hidden_size, output_size).type(dtype)
init.normal(w2, 0.0, 0.3)
w2 = Variable(w2, requires_grad=True)

def forward(input, context_state, w1, w2):
xh = torch.cat((input, context_state), 1)
context_state = torch.tanh(xh.mm(w1))
out = context_state.mm(w2)
return (out, context_state)

criterion = nn.MSELoss()
for i in range(epochs):
total_loss = 0
context_state = Variable(torch.zeros((1, hidden_size)).type(dtype), requires_grad=True)
context_state = torch.zeros((1, hidden_size), dtype=dtype)
for j in range(x.size(0)):
input = x[j:(j+1)]
target = y[j:(j+1)]
(pred, context_state) = forward(input, context_state, w1, w2)
loss = (pred - target).pow(2).sum()/2
loss = criterion(pred, target)
total_loss += loss
loss.backward()
loss.backward(retain_graph=True)
w1.data -= lr * w1.grad.data
w2.data -= lr * w2.grad.data
w1.grad.data.zero_()
w2.grad.data.zero_()
context_state = Variable(context_state.data)
if i % 10 == 0:
print("Epoch: {} loss {}".format(i, total_loss.data[0]))
print("Epoch: {} loss {}".format(i, total_loss.item()))


context_state = Variable(torch.zeros((1, hidden_size)).type(dtype), requires_grad=False)
context_state = torch.zeros((1, hidden_size), dtype=dtype)
predictions = []

for i in range(x.size(0)):
input = x[i:i+1]
(pred, context_state) = forward(input, context_state, w1, w2)
context_state = context_state
predictions.append(pred.data.numpy().ravel()[0])


pl.scatter(data_time_steps[:-1], x.data.numpy(), s=90, label="Actual")
pl.scatter(data_time_steps[1:], predictions, label="Predicted")
pl.legend()
pl.show()