added simple rnn demo

Author: culurciello

rnn-example/rnn-simple.py

@@ -0,0 +1,74 @@
+#! /usr/local/bin/python3
+
+# RNN example "abba" detector
+#
+# E. Culurciello, April 2017
+#
+
+import sys
+import os
+import time
+import numpy as np
+import argparse
+from tqdm import tqdm
+from pathlib import Path
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+# create a fake dataset of symbols a,b:
+data_size = 256
+seq_len = 4 # abba sequence to be detected only!
+data = np.random.randint(0, 2, data_size) # 0=1, 1=b, for example
+label = np.zeros(data_size, dtype=int)
+print('dataset is:', data, 'with length:', len(data))
+for i in range(3, data_size-1):
+   if (data[i-3]==0 and data[i-2]==1 and data[i-1]==1 and data[i]==0):
+      label[i] += 1
+
+print('labels is:', label, 'total number of example sequences:', np.sum(label))
+
+
+# create model:
+model = nn.RNN(1,1,1)
+criterion = nn.L1Loss()
+
+# test model:
+# inp = Variable(torch.randn(seq_len).view(seq_len,1,1))
+# h0 = Variable(torch.randn(seq_len).view(seq_len,1,1))
+# print(inp, h0)
+# output, hn = model(inp, h0)
+# print('model test:', output,hn)
+
+
+def train():
+   model.train()
+   hidden = Variable(torch.zeros(1,1,1))
+   for i in tqdm(range(0, data_size-seq_len, seq_len)):
+      X_batch = Variable(torch.from_numpy(data[i:i+seq_len]).view(seq_len,1,1).float())
+      y_batch = Variable(torch.from_numpy(label[i:i+seq_len]).view(seq_len,1,1).float())
+      model.zero_grad()
+      output, hidden = model(X_batch, hidden)
+      loss = criterion(output, y_batch)
+      loss.backward(retain_variables=True)
+      print('in/label/out:', data[i:i+seq_len], label[i:i+seq_len], output.data.view(1,4).numpy())
+      # # print(X_batch, y_batch)
+      if (data[i]==0 and data[i+1]==1 and data[i+2]==1 and data[i+3]==0):
+         print('RIGHT')
+      print(loss.data.numpy())
+
+
+def test():
+   model.eval()
+   hidden = Variable(torch.zeros(1,1,1))
+   for i in range(0, data_size-seq_len, seq_len):
+      X_batch = Variable(torch.from_numpy(data[i:i+seq_len]).view(seq_len,1,1).float())
+      y_batch = Variable(torch.from_numpy(label[i:i+seq_len]).view(seq_len,1,1).float())
+      output, hidden = model(X_batch, hidden)
+      print('in/label/out:', data[i:i+seq_len], label[i:i+seq_len], output.data.view(1,4).numpy())
+      if (data[i]==0 and data[i+1]==1 and data[i+2]==1 and data[i+3]==0):
+         print('RIGHT')
+
+# train model:
+train()
+test()