Merge pull request #217 from tomtung/fasttext_example

Implement Example: FastText classifier on IMDb dataset

Author: zsdonghao

example/imdb_fasttext.py

@@ -0,0 +1,245 @@
+#!/usr/bin/env python
+
+__doc__ = """
+
+This demo implements FastText[1] for sentence classification. FastText is a
+simple model for text classification with performance often close to
+state-of-the-art, and is useful as a solid baseline.
+
+There are some important differences between this implementation and what
+is described in the paper. Instead of Hogwild! SGD[2], we use Adam optimizer
+with mini-batches. Hierarchical softmax is also not supported; if you have
+a large label space, consider utilizing candidate sampling methods provided
+by TensorFlow[3].
+
+After 5 epochs, you should get test accuracy close to 90.9%.
+
+[1] Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016).
+    Bag of Tricks for Efficient Text Classification. 
+    http://arxiv.org/abs/1607.01759
+
+[2] Recht, B., Re, C., Wright, S., & Niu, F. (2011).
+    Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.
+    In Advances in Neural Information Processing Systems 24 (pp. 693–701).
+
+[3] https://www.tensorflow.org/api_guides/python/nn#Candidate_Sampling
+
+"""
+
+import array
+import hashlib
+import time
+
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+
+
+# Hashed n-grams with 1 < n <= N_GRAM are included as features
+# in addition to unigrams.
+N_GRAM = 2
+
+# Size of vocabulary; less frequent works will be treated as "unknown"
+VOCAB_SIZE = 100000
+
+# Number of buckets used for hashing n-grams
+N_BUCKETS = 1000000
+
+# Size of the embedding vectors
+EMBEDDING_SIZE = 50
+
+# Number of epochs for which the model is trained
+N_EPOCH = 5
+
+# Size of training mini-batches
+BATCH_SIZE = 32
+
+# Path to which to save the trained model
+MODEL_FILE_PATH = 'model.npz'
+
+
+class FastTestEmbeddingInputLayer(tl.layers.Layer):
+    def __init__(
+            self, inputs, vocabulary_size, embedding_size,
+            name='fasttext_layer',
+            embeddings_initializer=tf.random_uniform_initializer(-0.1, 0.1),
+            embeddings_kwargs=None):
+        """FastText Embedding input layer for sentences.
+
+        :param inputs: input placeholder or tensor; zeros are paddings
+        :param vocabulary_size: and integer, the size of vocabulary
+        :param embedding_size: and integer, the dimension of embedding vectors
+        :param name: a string, the name of the layer
+        :param embeddings_initializer: the initializer of the embedding matrix
+        :param embeddings_kwargs: kwargs to get embedding matrix variable
+        """
+        super().__init__(name=name)
+
+        if inputs.get_shape().ndims != 2:
+            raise ValueError(
+                'inputs must be of size batch_size * batch_sentence_length')
+
+        self.inputs = inputs
+
+        print(f"  [TL] FastTestEmbeddingInputLayer {self.name}:"
+              f" ({vocabulary_size}, {embedding_size})")
+
+        with tf.variable_scope(name):
+            self.embeddings = tf.get_variable(
+                name='embeddings',
+                shape=(vocabulary_size, embedding_size),
+                initializer=embeddings_initializer,
+                **(embeddings_kwargs or {}),
+            )
+            word_embeddings = tf.nn.embedding_lookup(
+                self.embeddings, self.inputs,
+                name='word_embeddings',
+            )
+
+            # Masks used to ignore padding words
+            masks = tf.expand_dims(
+                tf.sign(self.inputs),
+                axis=-1,
+                name='masks',
+            )
+            sum_word_embeddings = tf.reduce_sum(
+                word_embeddings * tf.cast(masks, tf.float32),
+                axis=1,
+            )
+
+            # Count number of non-padding words in each sentence
+            # Used to commute average word embeddings in sentences
+            sentence_lengths = tf.count_nonzero(
+                self.inputs,
+                axis=1,
+                keep_dims=True,
+                dtype=tf.float32,
+                name='sentence_lengths',
+            )
+
+            sentence_embeddings = tf.divide(
+                sum_word_embeddings,
+                sentence_lengths,
+                name='sentence_embeddings'
+            )
+
+        self.outputs = sentence_embeddings
+        self.all_layers = [self.outputs]
+        self.all_params = [self.embeddings]
+        self.all_drop = {}
+
+
+class FastTextClassifier(object):
+    """Simple wrapper class for creating the graph of FastText classifier."""
+    def __init__(self, vocab_size, embedding_size, n_labels):
+        self.vocab_size = vocab_size
+        self.embedding_size = embedding_size
+        self.n_labels = n_labels
+
+        self.inputs = tf.placeholder(
+            tf.int32, shape=[None, None], name='inputs')
+        self.labels = tf.placeholder(
+            tf.int32, shape=[None], name='labels')
+
+        # Network structure
+        network = FastTestEmbeddingInputLayer(
+            self.inputs, self.vocab_size, self.embedding_size)
+        self.network = tl.layers.DenseLayer(network, self.n_labels)
+
+        # Training operation
+        cost = tl.cost.cross_entropy(
+            self.network.outputs,
+            self.labels,
+            name='cost'
+        )
+        self.train_op = tf.train.AdamOptimizer().minimize(cost)
+
+        # Predictions
+        self.prediction_probs = tf.nn.softmax(self.network.outputs)
+        self.predictions = tf.argmax(
+            self.network.outputs, axis=1, output_type=tf.int32)
+
+        # Evaluation
+        are_predictions_correct = tf.equal(self.predictions, self.labels)
+        self.accuracy = tf.reduce_mean(
+            tf.cast(are_predictions_correct, tf.float32))
+
+    def save(self, sess, filename):
+        tl.files.save_npz(self.network.all_params, name=filename, sess=sess)
+
+    def load(self, sess, filename):
+        tl.files.load_and_assign_npz(sess, name=filename, network=self.network)
+
+
+def augment_with_ngrams(unigrams, unigram_vocab_size, n_buckets, n=2):
+    """Augment unigram features with hashed n-gram features."""
+    def get_ngrams(n):
+        return list(zip(*[
+            unigrams[i:]
+            for i in range(n)
+        ]))
+
+    def hash_ngram(ngram):
+        bytes_ = array.array('L', ngram).tobytes()
+        hash_ = int(hashlib.sha256(bytes_).hexdigest(), 16)
+        return unigram_vocab_size + hash_ % n_buckets
+
+    return unigrams + [
+        hash_ngram(ngram)
+        for i in range(2, n + 1)
+        for ngram in get_ngrams(i)
+    ]
+
+
+def load_and_preprocess_imdb_data(n_gram=None):
+    """Load IMDb data and augment with hashed n-gram features."""
+    X_train, y_train, X_test, y_test = \
+        tl.files.load_imdb_dataset(nb_words=VOCAB_SIZE)
+
+    if n_gram is not None:
+        X_train = np.array([
+            augment_with_ngrams(x, VOCAB_SIZE, N_BUCKETS, n=n_gram)
+            for x in X_train
+        ])
+        X_test = np.array([
+            augment_with_ngrams(x, VOCAB_SIZE, N_BUCKETS, n=n_gram)
+            for x in X_test
+        ])
+
+    return X_train, y_train, X_test, y_test
+
+
+def train_test_and_save_model():
+    X_train, y_train, X_test, y_test = load_and_preprocess_imdb_data(N_GRAM)
+    classifier = FastTextClassifier(
+        vocab_size=VOCAB_SIZE + N_BUCKETS,
+        embedding_size=EMBEDDING_SIZE,
+        n_labels=2,
+    )
+
+    with tf.Session() as sess:
+        tl.layers.initialize_global_variables(sess)
+
+        for epoch in range(N_EPOCH):
+            start_time = time.time()
+            print(f'Epoch {epoch + 1}/{N_EPOCH}', end='')
+            for X_batch, y_batch in tl.iterate.minibatches(
+                    X_train, y_train, batch_size=BATCH_SIZE, shuffle=True):
+                sess.run(classifier.train_op, feed_dict={
+                    classifier.inputs: tl.prepro.pad_sequences(X_batch),
+                    classifier.labels: y_batch,
+                })
+
+            print(f'\t{time.time() - start_time:.2f}s')
+
+        test_accuracy = sess.run(classifier.accuracy, feed_dict={
+            classifier.inputs: tl.prepro.pad_sequences(X_test),
+            classifier.labels: y_test,
+        })
+        print(f'Test accuracy: {test_accuracy:.5f}')
+
+        classifier.save(sess, MODEL_FILE_PATH)
+
+
+if __name__ == '__main__':
+    train_test_and_save_model()