Add the labs for Unit 2

labs/l2-advanced/cbow-starter.py

+# Skeleton code for lab L2 (advanced)
+
+# Do not use any imports other than the ones already in this file.
+
+import numpy as np
+
+
+# The next four functions are adapted from cbow-classifier.ipynb
+
+
+def load_data(filename, max_length=20):
+    items = []
+    with open(filename, 'rt', encoding='utf-8') as fp:
+        for line in fp:
+            sentence, label = line.rstrip().split('\t')
+            items.append((sentence.split()[:max_length], int(label)))
+    return items
+
+
+def make_vocab(data):
+    vocab = {'<pad>': 0}
+    for sentence, label in data:
+        for t in sentence:
+            if t not in vocab:
+                vocab[t] = len(vocab)
+    return vocab
+
+
+def vectorize(vocab, data):
+    max_length = max(len(s) for s, _ in data)
+    xs = []
+    ys = []
+    for sentence, label in data:
+        x = [0] * max_length
+        for i, w in enumerate(sentence):
+            if w in vocab:
+                x[i] = vocab[w]
+        xs.append(x)
+        ys.append(label)
+    return np.array(xs), np.array(ys)
+
+
+def accuracy(y_pred, y):
+    return np.mean((y_pred == y).astype(np.float))
+
+
+def minibatches(x, y, batch_size):
+    random_indices = np.random.permutation(x.shape[0])
+    for i in range(0, x.shape[0] - batch_size + 1, batch_size):
+        batch_indices = random_indices[i:i+batch_size]
+        yield x[batch_indices], y[batch_indices]
+
+
+# Efficient implementation of the softmax function; see
+# http://stackoverflow.com/a/39558290
+
+def softmax(X):
+    E = np.exp(X - np.max(X, axis=1, keepdims=True))
+    return E / E.sum(axis=1, keepdims=True)
+
+
+# Your code below
+
+class CBOW(object):
+
+    def __init__(self, num_embeddings, output_dim, embedding_dim=30):
+        # TODO: Replace the next lines with your own code
+        self.E = np.zeros((num_embeddings, embedding_dim))
+        self.W = np.zeros((embedding_dim, output_dim))
+        self.b = np.zeros((1, output_dim))
+
+    def forward(self, features):
+        # TODO: Replace the next line with your own code
+        return np.zeros((len(features), self.W.shape[1]))
+
+
+def train(vocab, train_x, train_y, n_epochs=20, batch_size=24, lr=1e-1):
+    # TODO: Replace the next line with your own code
+    return CBOW(len(vocab), 5)
+
+
+# MAIN METHOD
+
+
+def main():
+    import sys
+
+    train_data = load_data('sst-5-train.txt')
+    dev_data = load_data('sst-5-dev.txt')
+
+    vocab = make_vocab(train_data)
+    train_x, train_y = vectorize(vocab, train_data)
+    dev_x, dev_y = vectorize(vocab, dev_data)
+
+    accuracies = []
+    for _ in range(10):
+        model = train(vocab, train_x, train_y)
+        dev_output = model.forward(dev_x)
+        dev_y_pred = np.argmax(dev_output, axis=1)
+        accuracies.append(accuracy(dev_y_pred, dev_y))
+    print('Average accuracy: {:.4f}'.format(sum(accuracies) / len(accuracies)))
+
+
+if __name__ == '__main__':
+    main()