Add basic LSTM tutorial

code/imdb.py

@@ -0,0 +1,92 @@
+import cPickle
+import gzip
+import os
+import sys
+import time
+
+import numpy
+
+import theano
+import theano.tensor as T
+
+def prepare_data(seqs, labels, maxlen=None):
+    # x: a list of sentences
+    lengths = [len(s) for s in seqs]
+
+    if maxlen != None:
+        new_seqs = []
+        new_labels = []
+        new_lengths = []
+        for l, s, y in zip(lengths, seqs, labels):
+            if l < maxlen:
+                new_seqs.append(s)
+                new_labels.append(y)
+                new_lengths.append(l)
+        lengths = new_lengths
+        labels = new_labels
+        seqs = new_seqs
+
+        if len(lengths) < 1:
+            return None, None, None
+
+    n_samples = len(seqs)
+    maxlen = numpy.max(lengths)
+
+    x = numpy.zeros((maxlen, n_samples)).astype('int64')
+    x_mask = numpy.zeros((maxlen, n_samples)).astype('float32')
+    for idx, s in enumerate(seqs):
+        x[:lengths[idx],idx] = s
+        x_mask[:lengths[idx],idx] = 1.
+
+    return x, x_mask, labels
+
+def load_data(path="/data/lisatmp3/chokyun/tweets_sa/imdb/aclImdb/imdb.pkl", n_words=100000, valid_portion=0.1):
+    ''' Loads the dataset
+
+    :type dataset: string
+    :param dataset: the path to the dataset (here IMDB)
+    '''
+
+    #############
+    # LOAD DATA #
+    #############
+
+    print '... loading data'
+
+    # Load the dataset
+    f = open(path, 'rb')
+    train_set = cPickle.load(f)
+    test_set = cPickle.load(f)
+    f.close()
+
+    # split training set into validation set
+    train_set_x, train_set_y = train_set
+    n_samples = len(train_set_x)
+    sidx = numpy.random.permutation(n_samples)
+    n_train = int(numpy.round(n_samples * (1. - valid_portion)))
+    valid_set_x = [train_set_x[s] for s in sidx[n_train:]]
+    valid_set_y = [train_set_y[s] for s in sidx[n_train:]]
+    train_set_x = [train_set_x[s] for s in sidx[:n_train]]
+    train_set_y = [train_set_y[s] for s in sidx[:n_train]]
+
+    train_set = (train_set_x, train_set_y)
+    valid_set = (valid_set_x, valid_set_y)
+
+    def remove_unk(x):
+        return [[1 if w >= n_words else w for w in sen] for sen in x]
+
+    test_set_x, test_set_y = test_set
+    valid_set_x, valid_set_y = valid_set
+    train_set_x, train_set_y = train_set
+
+    train_set_x = remove_unk(train_set_x)
+    valid_set_x = remove_unk(valid_set_x)
+    test_set_x = remove_unk(test_set_x)
+
+    train = (train_set_x, train_set_y)
+    valid = (valid_set_x, valid_set_y)
+    test = (test_set_x, test_set_y)
+
+    return train, valid, test
+
+