Python tensorflow.initialize_local_variables函数代码示例

  def testOneThreadDynamicPad(self):
    with self.test_session() as sess:
      batch_size = 10
      num_batches = 3
      zero64 = tf.constant(0, dtype=tf.int64)
      examples = tf.Variable(zero64)
      counter = examples.count_up_to(num_batches * batch_size)
      string = tf.tile(["string"], tf.to_int32(tf.pack([counter])))
      tf.initialize_all_variables().run()
      tf.initialize_local_variables().run()
      batched = tf.train.batch(
          [counter, string], batch_size=batch_size, dynamic_pad=True)
      threads = tf.train.start_queue_runners()

      for i in range(num_batches):
        results = sess.run(batched)
        expected_results = np.arange(i * batch_size, (i + 1) * batch_size)
        max_len = expected_results[-1]
        self.assertAllEqual(results[0], expected_results)
        expected_strings = [
            [b"string"] * rep + [b""] * (max_len - rep)
            for rep in expected_results]
        self.assertAllEqual(results[1], expected_strings)

      # Reached the limit.
      with self.assertRaises(tf.errors.OutOfRangeError):
        sess.run(batched)
      for thread in threads:
        thread.join()

 def testNoLimit(self):
   with self.test_session():
     seven = tf.constant(7)
     seven_forever = tf.train.limit_epochs(seven)
     tf.initialize_local_variables().run()
     for _ in range(100):
       self.assertEqual(7, seven_forever.eval())

    def __init__(self, model_def_file, class_labels_file):
        logging.info('Loading net and associated files...')

        with tf.Graph().as_default(), tf.device('cpu:0'):
            self.sess = tf.Session()
            self.image_buffer = tf.placeholder(tf.string)
            image = tf.image.decode_jpeg(self.image_buffer, channels=3)
            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
            image = self.eval_image(image, 299, 299)
            image = tf.sub(image, 0.5)
            image = tf.mul(image, 2.0)
            images = tf.expand_dims(image, 0)

            # Run inference.
            logits, predictions = inception_model.inference(
                images, NUM_CLASSES + 1)

            # Transform output to topK result.
            self.values, self.indices = tf.nn.top_k(
                predictions, NUM_TOP_CLASSES)

            variable_averages = tf.train.ExponentialMovingAverage(
                inception_model.MOVING_AVERAGE_DECAY)
            variables_to_restore = variable_averages.variables_to_restore()
            tf.initialize_all_variables().run(session=self.sess)
            tf.initialize_local_variables().run(session=self.sess)
            saver = tf.train.Saver(variables_to_restore)
            saver.restore(self.sess, model_def_file)
            # Required to get the filename matching to run.

            self.label_names = ['none']
            with open(class_labels_file) as f:
                for line in f.read().decode("utf-8").splitlines():
                    self.label_names.append(line)

    def _testRemoveSqueezableDimensions(
        self, predictions_have_static_shape, predictions_have_extra_dim, labels_have_static_shape, labels_have_extra_dim
    ):
        assert not (predictions_have_extra_dim and labels_have_extra_dim)
        predictions_value = (0, 1, 1, 0, 0, 1, 0)
        labels_value = (0, 0, 1, 1, 0, 0, 0)

        input_predictions_value = [[p] for p in predictions_value] if predictions_have_extra_dim else predictions_value
        input_labels_value = [[l] for l in labels_value] if labels_have_extra_dim else labels_value

        with tf.Graph().as_default() as g:
            feed_dict = {}
            if predictions_have_static_shape:
                predictions = tf.constant(input_predictions_value, dtype=tf.int32)
            else:
                predictions = tf.placeholder(dtype=tf.int32, name="predictions")
                feed_dict[predictions] = input_predictions_value
            if labels_have_static_shape:
                labels = tf.constant(input_labels_value, dtype=tf.int32)
            else:
                labels = tf.placeholder(dtype=tf.int32, name="labels")
                feed_dict[labels] = input_labels_value

            squeezed_predictions, squeezed_labels = tf.contrib.framework.remove_squeezable_dimensions(
                predictions, labels
            )
            with self.test_session(g):
                tf.initialize_local_variables().run()
                self.assertAllClose(predictions_value, squeezed_predictions.eval(feed_dict=feed_dict))
                self.assertAllClose(labels_value, squeezed_labels.eval(feed_dict=feed_dict))

def read_data_int64(input_fname):
    import pdb
    with tictoc():
        input_fname_queue = tf.train.string_input_producer([input_fname], num_epochs=1)
        reader = tf.TFRecordReader()
        _, serialized_example = reader.read(input_fname_queue)
        features = {'bit_features' : tf.VarLenFeature(tf.int64)}
        parsed_example = tf.parse_single_example(serialized_example, features)
        bit_features = parsed_example['bit_features']
        bit_features = tf.sparse_tensor_to_dense(bit_features)
        bit_features = tf.reshape(bit_features, [-1, 62])

        with tf.Session() as sess:
            tf.initialize_all_variables().run()
            tf.initialize_local_variables().run()
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)

            try:
                i = 0
                while not coord.should_stop():
                    x = bit_features.eval()
                    if i % 10000 == 0: print("substance {}".format(i))
                    i += 1
            except tf.errors.OutOfRangeError:
                pass
            finally:
                coord.request_stop()

            coord.join(threads)

def cnn_train(config, data_len, embed, pf_r1, pf_r2):
    config.data_len = data_len
    tf.reset_default_graph()
    with tf.Session() as session:
        # build model
        with tf.variable_scope("cnn_ch", reuse=None):
            m_train = ch_model(config)
        with tf.variable_scope("cnn_ch", reuse=True):
            m_valid = ch_model(config)

        doc_datas, pf_r1s, pf_r2s, labels = read_batch(config.csv_file, config, True)
        doc_datas_v, pf_r1s_V, pf_r2s_v, labels_v = read_batch(config.csv_file, config, False)


        for item in tf.all_variables():
            print "var: ", item
        for item in tf.local_variables():
            print "local:", item

        loss, _ = m_train.inference(doc_datas, pf_r1s, pf_r2s, labels)
        loss_v, acc_v = m_valid.inference(doc_datas_v, pf_r1s_V, pf_r2s_v, labels_v)
        train_op = m_train.train(loss)

        tf.initialize_all_variables().run()
        tf.initialize_local_variables().run()
        m_train.assign_word_embed(session, embed)

        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord, sess=session)
        
        epoch = 0
        step = 0
        min_cost = sys.maxint
        try:
            while not coord.should_stop():
                _, f_l = session.run([train_op, loss])
                step += 1
                if step == config.data_len // config.batch_size:
                    cost = 0.0
                    acc = 0.0
                    for i in range(step):
                        v_l, acc_l = session.run([loss_v, acc_v])
                        cost += v_l
                        acc += acc_l
                    cost /= step
                    acc /= step
                    if cost < min_cost:
                        min_cost = cost
                        print "save model as cost:", cost
                        m_train.saver.save(session, config.model_path)
                    print "epoch: ", epoch, "loss: ", cost, "acc: ", acc, "step:", step
                    step = 0
                    epoch += 1
        except tf.errors.OutOfRangeError:
            print("Done training")
        finally:
            coord.request_stop()
        coord.join(threads)

def main(_):
    if FLAGS.train_data:
        num_labels, num_features, train_data, train_labels = extract_data(FLAGS.train_data, feature_limit=FEATURE_LIMIT)
    else:
        num_labels, num_features = 2, FEATURE_LIMIT
        train_data, train_labels = [], []
    print "labels", num_labels, "features", num_features

    if FLAGS.test_data:
        _, _, test_data, test_labels = extract_data(FLAGS.test_data, feature_limit=FEATURE_LIMIT)
    else:
        test_data, test_labels = [], []

    train_size = len(train_data)
    model = LinearModel(num_features, num_labels, FLAGS.learning_rate)

    # Create local session to train and test
    with tf.Session(graph=model.graph) as s:
        ckpt = tf.train.get_checkpoint_state(FLAGS.models)
        if ckpt and ckpt.model_checkpoint_path:
            model.saver.restore(s, ckpt.model_checkpoint_path)
            print "Model loaded from", ckpt.model_checkpoint_path
        else:
            model.init.run()
            print "Initialized"

        if test_data:
            print 'testing'
            correct = 0
            total = 0
            tf.initialize_local_variables()
            for i in range(len(test_data) // BATCH_SIZE):
                offset = i * BATCH_SIZE
                batch_data = transform(test_data[offset:(offset + BATCH_SIZE)], num_features)
                batch_labels = test_labels[offset:(offset + BATCH_SIZE)]
                c = s.run(
                    [model.correct_sum],
                    feed_dict={model.x: batch_data, model.y_: batch_labels})
                correct += c[0]
                total += BATCH_SIZE
                print correct, total, "accuracy:", float(correct) / total
            return

        # Iterate and train.
        average_loss = 0
        for step in xrange(FLAGS.train_steps * len(train_data) // BATCH_SIZE):
            offset = (step * BATCH_SIZE) % train_size
            batch_data = transform(train_data[offset: (offset + BATCH_SIZE)], num_features)
            batch_labels = train_labels[offset: (offset + BATCH_SIZE)]
            _, loss_val = s.run([model.optimizer, model.cross_entropy],
                                feed_dict={model.x: batch_data, model.y_: batch_labels})
            average_loss += loss_val
            if step > 0 and step % K == 0:
                print "Average loss at step: ", model.global_step.eval(), " loss: ", average_loss / K
                average_loss = 0
                checkpoint_path = os.path.join(FLAGS.models, "pe.ckpt")
                model.saver.save(s, checkpoint_path, global_step=model.global_step)

 def test_empty_labels_and_scores_gives_nan_auc(self):
   with self.test_session():
     labels = tf.constant([], shape=[0], dtype=tf.bool)
     scores = tf.constant([], shape=[0], dtype=tf.float32)
     score_range = [0, 1.]
     auc, update_op = tf.contrib.metrics.auc_using_histogram(labels, scores,
                                                             score_range)
     tf.initialize_local_variables().run()
     update_op.run()
     self.assertTrue(np.isnan(auc.eval()))

 def testLimit(self):
   with self.test_session():
     love_me = tf.constant("Love Me")
     love_me_two_times = tf.train.limit_epochs(love_me, num_epochs=2)
     tf.initialize_all_variables().run()
     tf.initialize_local_variables().run()
     self.assertEqual(b"Love Me", love_me_two_times.eval())
     self.assertEqual(b"Love Me", love_me_two_times.eval())
     with self.assertRaises(tf.errors.OutOfRangeError):
       love_me_two_times.eval()

  def _check_auc(self,
                 nbins=100,
                 desired_auc=0.75,
                 score_range=None,
                 num_records=50,
                 frac_true=0.5,
                 atol=0.05,
                 num_updates=10):
    """Check auc accuracy against synthetic data.

    Args:
      nbins:  nbins arg from contrib.metrics.auc_using_histogram.
      desired_auc:  Number in [0, 1].  The desired auc for synthetic data.
      score_range:  2-tuple, (low, high), giving the range of the resultant
        scores.  Defaults to [0, 1.].
      num_records:  Positive integer.  The number of records to return.
      frac_true:  Number in (0, 1).  Expected fraction of resultant labels that
        will be True.  This is just in expectation...more or less may actually
        be True.
      atol:  Absolute tolerance for final AUC estimate.
      num_updates:  Update internal histograms this many times, each with a new
        batch of synthetic data, before computing final AUC.

    Raises:
      AssertionError: If resultant AUC is not within atol of theoretical AUC
        from synthetic data.
    """
    score_range = [0, 1.] or score_range
    with self.test_session():
      labels = tf.placeholder(tf.bool, shape=[num_records])
      scores = tf.placeholder(tf.float32, shape=[num_records])
      auc, update_op = tf.contrib.metrics.auc_using_histogram(labels,
                                                              scores,
                                                              score_range,
                                                              nbins=nbins)
      tf.initialize_local_variables().run()
      # Updates, then extract auc.
      for _ in range(num_updates):
        labels_a, scores_a = synthetic_data(desired_auc, score_range,
                                            num_records, self.rng, frac_true)
        update_op.run(feed_dict={labels: labels_a, scores: scores_a})
      labels_a, scores_a = synthetic_data(desired_auc, score_range, num_records,
                                          self.rng, frac_true)
      # Fetch current auc, and verify that fetching again doesn't change it.
      auc_eval = auc.eval()
      self.assertAlmostEqual(auc_eval, auc.eval(), places=5)

    msg = ('nbins: %s, desired_auc: %s, score_range: %s, '
           'num_records: %s, frac_true: %s, num_updates: %s') % (nbins,
                                                                 desired_auc,
                                                                 score_range,
                                                                 num_records,
                                                                 frac_true,
                                                                 num_updates)
    np.testing.assert_allclose(desired_auc, auc_eval, atol=atol, err_msg=msg)

def run_eval(dataset, hps, logdir, mode, num_eval_steps):
    with tf.variable_scope("model"):
        hps.num_sampled = 0
        hps.keep_prob = 1.0
        model = LM(hps, "eval", "/cpu:0")

    if hps.average_params:
        print("Averaging parameters for evaluation.")
        saver = tf.train.Saver(model.avg_dict)
    else:
        saver = tf.train.Saver()

    # Use only 4 threads for the evaluation
    config = tf.ConfigProto(allow_soft_placement=True,
                            intra_op_parallelism_threads=20,
                            inter_op_parallelism_threads=1)

    sess = tf.Session(config=config)
    sw = tf.train.SummaryWriter(logdir + '/' + mode, sess.graph)
    ckpt_loader = CheckpointLoader(saver, model.global_step, logdir + "/train")

    with sess.as_default():
        while ckpt_loader.load_checkpoint():
            global_step = ckpt_loader.last_global_step
            data_iterator = dataset.iterate_once(hps.batch_size * hps.num_gpus,
                                                 hps.num_steps)
            tf.initialize_local_variables().run()
            loss_nom = 0.0
            loss_den = 0.0

            for i, (x, y, w) in enumerate(data_iterator):
                if i >= num_eval_steps:
                    break

                loss = sess.run(model.loss, {model.x: x, model.y: y, model.w: w})
                loss_nom += loss
                loss_den += w.mean()

                loss = loss_nom / loss_den
                sys.stdout.write("%d: %.3f (%.3f) ... " % (i, loss, np.exp(loss)))
                sys.stdout.flush()
            sys.stdout.write("\n")

            log_perplexity = loss_nom / loss_den
            print("Results at %d: log_preplexity = %.3f perplexity = %.3f" % (
                global_step, log_perplexity, np.exp(log_perplexity)))

            summary = tf.Summary()
            summary.value.add(tag='eval/log_perplexity', simple_value=log_perplexity)
            summary.value.add(tag='eval/perplexity', simple_value=np.exp(log_perplexity))
            sw.add_summary(summary, global_step)
            sw.flush()

def test_input_fname_producer(input_fname):
    import pdb
    pdb.set_trace()

    with tf.Session() as sess:
        queue = tf.train.string_input_producer(
            [input_fname], num_epochs=None, shuffle=False)
        dequeue = queue.dequeue()
        tf.initialize_all_variables().run()
        tf.initialize_local_variables().run()
        threads = tf.train.start_queue_runners()
        output = dequeue.eval()
        for thread in threads:
            thread.join()

  def _testTwoThreadsHelper(self, use_dict):
    with self.test_session() as sess:
      batch_size = 10
      num_batches = 3
      zero64 = tf.constant(0, dtype=tf.int64)
      examples = tf.Variable(zero64)
      counter = examples.count_up_to(num_batches * batch_size)
      sparse_counter = tf.SparseTensor(
          indices=tf.reshape(zero64, [1, 1]),
          values=tf.pack([tf.cast(counter, tf.float32)]),
          shape=[1])
      if use_dict:
        batched = tf.train.shuffle_batch(
            {"c": counter, "s": sparse_counter, "S": "string"},
            batch_size=batch_size, capacity=32,
            min_after_dequeue=16, seed=141421)
        batched_fetch = [batched["c"], batched["s"], batched["S"]]
      else:
        batched = tf.train.shuffle_batch(
            [counter, sparse_counter, "string"],
            batch_size=batch_size, capacity=32,
            min_after_dequeue=16, seed=141421)
        batched_fetch = batched
      tf.initialize_all_variables().run()
      tf.initialize_local_variables().run()
      threads = tf.train.start_queue_runners()

      all_counts = []
      for i in range(num_batches):
        results = sess.run(batched_fetch)
        self.assertEqual(len(results[0]), batch_size)
        all_counts.extend(results[0])
        self.assertAllEqual(
            results[1].indices,
            np.vstack((np.arange(batch_size), np.zeros(batch_size))).T)
        self.assertAllEqual(results[0], results[1].values)
        self.assertAllEqual(results[1].shape, [batch_size, 1])
        self.assertAllEqual(results[2], [b"string"] * batch_size)
      # Results scrambled, but include all the expected numbers.
      deltas = [all_counts[i + 1] - all_counts[i]
                for i in range(len(all_counts) - 1)]
      self.assertFalse(all(d == deltas[0] for d in deltas))
      self.assertItemsEqual(all_counts, range(num_batches * batch_size))

      # Reached the limit.
      with self.assertRaises(tf.errors.OutOfRangeError):
        sess.run(batched_fetch)
      for thread in threads:
        thread.join()

  def test_batch_text_lines(self):
    gfile.Glob = self._orig_glob
    filename = self._create_temp_file("A\nB\nC\nD\nE\n")

    batch_size = 3
    queue_capacity = 10
    name = "my_batch"

    with tf.Graph().as_default() as g, self.test_session(graph=g) as session:
      inputs = tf.contrib.learn.io.read_batch_examples(
          [filename], batch_size, reader=tf.TextLineReader,
          randomize_input=False, num_epochs=1, queue_capacity=queue_capacity,
          read_batch_size=10, name=name)
      session.run(tf.initialize_local_variables())

      coord = tf.train.Coordinator()
      threads = tf.train.start_queue_runners(session, coord=coord)

      self.assertAllEqual(session.run(inputs), [b"A", b"B", b"C"])
      self.assertAllEqual(session.run(inputs), [b"D", b"E"])
      with self.assertRaises(errors.OutOfRangeError):
        session.run(inputs)

      coord.request_stop()
      coord.join(threads)

  def testMultipleUpdatesWithWeightedValues(self):
    with self.test_session() as sess:
      # Create the queue that populates the predictions.
      preds_queue = tf.FIFOQueue(4, dtypes=tf.float32, shapes=(1, 1))
      _enqueue_vector(sess, preds_queue, [0])
      _enqueue_vector(sess, preds_queue, [1])
      _enqueue_vector(sess, preds_queue, [2])
      _enqueue_vector(sess, preds_queue, [1])
      predictions = preds_queue.dequeue()

      # Create the queue that populates the labels.
      labels_queue = tf.FIFOQueue(4, dtypes=tf.float32, shapes=(1, 1))
      _enqueue_vector(sess, labels_queue, [0])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [1])
      _enqueue_vector(sess, labels_queue, [2])
      labels = labels_queue.dequeue()

      # Create the queue that populates the missing labels.
      weights_queue = tf.FIFOQueue(4, dtypes=tf.int64, shapes=(1, 1))
      _enqueue_vector(sess, weights_queue, [1])
      _enqueue_vector(sess, weights_queue, [1])
      _enqueue_vector(sess, weights_queue, [0])
      _enqueue_vector(sess, weights_queue, [0])
      weights = weights_queue.dequeue()

      accuracy, update_op = tf.contrib.metrics.streaming_accuracy(
          predictions, labels, weights)

      sess.run(tf.initialize_local_variables())
      for _ in range(4):
        sess.run(update_op)
      self.assertEqual(1.0, accuracy.eval())