Ref tensorflow#40: Simple example for text classification saving/rest…

…oring

examples/text_classification_save_restore.py

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+#  Copyright 2015 Google Inc. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import os
+import csv
+import numpy as np
+from sklearn import metrics
+
+import tensorflow as tf
+from tensorflow.models.rnn import rnn, rnn_cell
+import skflow
+
+### Training data
+
+# Download dbpedia_csv.tar.gz from
+# https://drive.google.com/folderview?id=0Bz8a_Dbh9Qhbfll6bVpmNUtUcFdjYmF2SEpmZUZUcVNiMUw1TWN6RDV3a0JHT3kxLVhVR2M
+# Unpack: tar -xvf dbpedia_csv.tar.gz
+
+def load_dataset(filename):
+    target = []
+    data = []
+    reader = csv.reader(open(filename), delimiter=',')
+    for line in reader:
+        target.append(int(line[0]))
+        data.append(line[2])
+    return data, np.array(target, np.float32)
+
+X_train, y_train = load_dataset('dbpedia_csv/train.csv')
+X_test, y_test = load_dataset('dbpedia_csv/test.csv')
+
+### Process vocabulary
+
+MAX_DOCUMENT_LENGTH = 10
+
+vocab_processor = skflow.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
+X_train = np.array(list(vocab_processor.fit_transform(X_train)))
+X_test = np.array(list(vocab_processor.transform(X_test)))
+
+n_words = len(vocab_processor.vocabulary_)
+print('Total words: %d' % n_words)
+
+### Models
+
+EMBEDDING_SIZE = 50
+
+def average_model(X, y):
+    word_vectors = skflow.ops.categorical_variable(X, n_classes=n_words,
+        embedding_size=EMBEDDING_SIZE, name='words')
+    features = tf.reduce_max(word_vectors, reduction_indices=1)
+    return skflow.models.logistic_regression(features, y)
+
+def rnn_model(X, y):
+    """Recurrent neural network model to predict from sequence of words
+    to a class."""
+    # Convert indexes of words into embeddings.
+    # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
+    # maps word indexes of the sequence into [batch_size, sequence_length,
+    # EMBEDDING_SIZE].
+    word_vectors = skflow.ops.categorical_variable(X, n_classes=n_words,
+        embedding_size=EMBEDDING_SIZE, name='words')
+    # Split into list of embedding per word, while removing doc length dim.
+    # word_list results to be a list of tensors [batch_size, EMBEDDING_SIZE].
+    word_list = skflow.ops.split_squeeze(1, MAX_DOCUMENT_LENGTH, word_vectors)
+    # Create a Gated Recurrent Unit cell with hidden size of EMBEDDING_SIZE.
+    cell = rnn_cell.GRUCell(EMBEDDING_SIZE)
+    # Create an unrolled Recurrent Neural Networks to length of
+    # MAX_DOCUMENT_LENGTH and passes word_list as inputs for each unit.
+    _, encoding = rnn.rnn(cell, word_list, dtype=tf.float32)
+    # Given encoding of RNN, take encoding of last step (e.g hidden size of the
+    # neural network of last step) and pass it as features for logistic
+    # regression over output classes.
+    return skflow.models.logistic_regression(encoding[-1], y)
+
+model_path = '/tmp/skflow_examples/text_classification'
+if os.path.exists(model_path):
+    classifier = skflow.TensorFlowEstimator.restore(model_path)
+    score = metrics.accuracy_score(classifier.predict(X_test), y_test)
+    print('Accuracy: {0:f}'.format(score))
+else:
+    classifier = skflow.TensorFlowEstimator(model_fn=rnn_model, n_classes=15,
+        steps=100, optimizer='Adam', learning_rate=0.01, continue_training=True)
+
+    # Continuesly train for 1000 steps & predict on test set.
+    while True:
+        try:
+            classifier.fit(X_train, y_train)
+        except KeyboardInterrupt:
+            classifier.save(model_path)
+            break
+