Esempio n. 1
0
def main(argv=None):
    if not gfile.Exists(FLAGS.train_dir):
        gfile.MakeDirs(FLAGS.train_dir)

    graph = tf.Graph()
    graph.device("/cpu:0")
    with graph.as_default():
        global_step = tf.Variable(0, trainable=False)
        images, labels = tf_model.inputs(training=True)
        logits = tf_model.inference(images)
        loss, accuracy = tf_model.loss(logits, labels)
        train_op = tf_model.train(loss, global_step)

        saver = tf.train.Saver(tf.all_variables())
        summary_op = tf.merge_all_summaries()

        sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
        with sess.as_default():
            if os.path.exists(FLAGS.train_dir + "checkpoint"):
                ckpt = tf.train.latest_checkpoint(FLAGS.train_dir)
                saver.restore(sess, ckpt)
            else:
                tf.initialize_all_variables().run()

            tf.train.start_queue_runners()

            summary_writer = tf.train.SummaryWriter(FLAGS.train_dir)

            for step in xrange(global_step.eval() + 1, FLAGS.max_steps):
                start_time = time.time()
                _, loss_value, accuracy_value = sess.run(
                    [train_op, loss, accuracy])
                duration = time.time() - start_time

                if step % 5 == 0:
                    examples_per_sec = FLAGS.batch_size / duration
                    sec_par_batch = float(duration)
                    format_str = (
                        "%s: step %d, loss = %.2f, accuracy = %.2f (%.1f examples/sec; %.3f sec/batch)"
                    )
                    print(format_str %
                          (datetime.now(), step, loss_value, accuracy_value,
                           examples_per_sec, sec_par_batch))

                    summary_str = summary_op.eval()
                    summary_writer.add_summary(summary_str, step)

                if step % 50 == 0 or (step + 1) == FLAGS.max_steps:
                    checkpoint_path = os.path.join(FLAGS.train_dir,
                                                   "model.ckpt")
                    saver.save(sess, checkpoint_path, global_step=step)

        sess.close()
def main(argv=None):
    if not gfile.Exists(FLAGS.eval_dir):
        gfile.MakeDirs(FLAGS.eval_dir)

    with tf.Graph().as_default():
        images, labels = tf_model.inputs(training=False)
        logits = tf_model.inference(images)
        logits = tf.squeeze(tf.argmax(logits, 1))

        saver = tf.train.Saver()

        graph_def = tf.get_default_graph().as_graph_def()

        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
                global_step = ckpt.model_checkpoint_path.split("/")[-1].split(
                    "-")[-1]

            coord = tf.train.Coordinator()
            threads = []
            for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
                threads.extend(
                    qr.create_threads(sess,
                                      coord=coord,
                                      daemon=True,
                                      start=True))

            accuracy = 0
            precision = np.zeros(FLAGS.num_classes)
            recall = np.zeros(FLAGS.num_classes)
            confusion = np.zeros((FLAGS.num_classes, FLAGS.num_classes))
            for i in xrange(0, FLAGS.num_iterate):
                batch_accuracy, batch_precision, batch_recall, batch_confusion = score(
                    logits, labels, num_classes=FLAGS.num_classes, sess=sess)
                accuracy += batch_accuracy
                precision += batch_precision
                recall += batch_recall
                confusion += batch_confusion

            print(accuracy / FLAGS.num_iterate)
            print(np.divide(precision, FLAGS.num_iterate))
            print(np.divide(recall, FLAGS.num_iterate))
            print(confusion)

            coord.request_stop()
            coord.join(threads, stop_grace_period_secs=10)
def main(argv=None):
    if not gfile.Exists(FLAGS.train_dir):
        gfile.MakeDirs(FLAGS.train_dir)

    graph = tf.Graph()
    graph.device("/cpu:0")
    with graph.as_default():
        global_step = tf.Variable(0, trainable=False)
        images, labels = tf_model.inputs(training=True)
        logits = tf_model.inference(images)
        loss, accuracy = tf_model.loss(logits, labels)
        train_op = tf_model.train(loss, global_step)

        saver = tf.train.Saver(tf.all_variables())
        summary_op = tf.merge_all_summaries()

        sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
        with sess.as_default():
            if os.path.exists(FLAGS.train_dir + "checkpoint"):
                ckpt = tf.train.latest_checkpoint(FLAGS.train_dir)
                saver.restore(sess, ckpt)
            else:
                tf.initialize_all_variables().run()

            tf.train.start_queue_runners()

            summary_writer = tf.train.SummaryWriter(FLAGS.train_dir)

            for step in xrange(global_step.eval()+1, FLAGS.max_steps):
                start_time = time.time()
                _, loss_value, accuracy_value = sess.run([train_op, loss, accuracy])
                duration = time.time() - start_time

                if step % 5 == 0:
                    examples_per_sec = FLAGS.batch_size / duration
                    sec_par_batch = float(duration)
                    format_str = ("%s: step %d, loss = %.2f, accuracy = %.2f (%.1f examples/sec; %.3f sec/batch)")
                    print (format_str % (datetime.now(), step, loss_value, accuracy_value, examples_per_sec, sec_par_batch))

                    summary_str = summary_op.eval()
                    summary_writer.add_summary(summary_str, step)

                if step % 50 == 0 or (step+1) == FLAGS.max_steps:
                    checkpoint_path = os.path.join(FLAGS.train_dir, "model.ckpt")
                    saver.save(sess, checkpoint_path, global_step=step)

        sess.close()
def main(argv=None):
    if not gfile.Exists(FLAGS.eval_dir):
        gfile.MakeDirs(FLAGS.eval_dir)

    with tf.Graph().as_default():
        images, labels = tf_model.inputs(training=False)
        logits = tf_model.inference(images)
        logits = tf.squeeze(tf.argmax(logits, 1))

        saver = tf.train.Saver()

        graph_def = tf.get_default_graph().as_graph_def()

        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
                global_step = ckpt.model_checkpoint_path.split("/")[-1].split("-")[-1]

            coord = tf.train.Coordinator()
            threads = []
            for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
                threads.extend(qr.create_threads(sess, coord=coord, daemon=True, start=True))

            accuracy = 0
            precision = np.zeros(FLAGS.num_classes)
            recall = np.zeros(FLAGS.num_classes)
            confusion = np.zeros((FLAGS.num_classes, FLAGS.num_classes))
            for i in xrange(0, FLAGS.num_iterate):
                batch_accuracy, batch_precision, batch_recall, batch_confusion = score(logits, labels, num_classes=FLAGS.num_classes, sess=sess)
                accuracy += batch_accuracy
                precision += batch_precision
                recall += batch_recall
                confusion += batch_confusion

            print(accuracy / FLAGS.num_iterate)
            print(np.divide(precision, FLAGS.num_iterate))
            print(np.divide(recall, FLAGS.num_iterate))
            print(confusion)

            coord.request_stop()
            coord.join(threads, stop_grace_period_secs=10)
Esempio n. 5
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def produce_likelihoods():
  with kaldi_helpers.kaldi_data('./t.ark') as kd:
    batch1 = kd.read_utterance(-1)
    u1 = batch1.next()
    u2 = batch1.next()
  print(np.shape(u1[1]))
  with kaldi_helpers.kaldi_data(FLAGS.occupances) as kd:
    logprioirs = kd.read_counts()
  
  with tf.Graph().as_default():
    val_images, val_labels = eval_inputs()
    images = tf.placeholder(tf.float32, shape=(None, 1320))
    labels = tf.placeholder(tf.int32, shape=(None))
    logits = tf_model.inference(images,
                               2048,
                               2048,
                               2048) 
    loss = tf_model.loss(logits, labels)                                
    saver = tf.train.Saver()
    sess = tf.Session()
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)    
    saver.restore(sess, '../../../data/tf_fbank_deltas_nocmvn/cnnmodel-31530')
    vi, vl = sess.run([val_images, val_labels])
    r = sess.run([logits], feed_dict={images: vi})
    l = r[0] - logprioirs
    with kaldi_helpers.kaldi_data('./t_like_u1.ark', 'w') as kd:
      kd.write_utterance([[u1[0], l]])
    r = sess.run([loss], feed_dict={images: vi, labels: vl})
    print(r)
    r = sess.run([loss], feed_dict={images: u1[1], labels: vl})
    print(r)
    l = vi
    # with kaldi_helpers.kaldi_data('./t_feats_tfr.ark', 'w') as kd:
    #  kd.write_utterance([[u1[0], l]])    
    coord.request_stop()
    # Wait for threads to finish.
    coord.join(threads)    
    sess.close()
Esempio n. 6
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def run_training():
  """Train MNIST for a number of steps."""
  # Tell TensorFlow that the model will be built into the default Graph.
  with tf.Graph().as_default():
    # with tf.variable_scope('training') as scope:
    # Input images and labels.
    images, labels = inputs(train=True, batch_size=FLAGS.batch_size,
                              num_epochs=FLAGS.num_epochs)
    # Eval inputs
    val_images, val_labels = eval_inputs()
    # Build a Graph that computes predictions from the inference model.
    logits = tf_model.inference(images,
                             FLAGS.hidden1,
                             FLAGS.hidden2,
                             FLAGS.hidden3)
    frame_accuracy = tf_model.evaluation(logits, labels)
    # Add to the Graph the loss calculation.
    loss = tf_model.loss(logits, labels)
    evaluation = tf_model.evaluation(logits, labels)
    ce_summ = tf.scalar_summary("cross entropy", loss)
    # with tf.variable_scope("hidden1", reuse=True):
    # weights_summ_h1=tf.histogram_summary("h1", weights))  
    lr = tf.Variable(float(FLAGS.learning_rate), name='lr')
    # Add to the Graph operations that train the model.
    train_op = tf_model.training(loss, lr)
    # Create a saver for writing training checkpoints.
    saver = tf.train.Saver()
    # The op for initializing the variables.
    init_op = tf.initialize_all_variables()
    # Create a session for running operations in the Graph.
    sess = tf.Session()
    summary_op = tf.merge_all_summaries()
    summary_writer = tf.train.SummaryWriter("./data/", sess.graph_def)
    # Initialize the variables (the trained variables and the
    # epoch counter).
    sess.run(init_op)
    # Start input enqueue threads.
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    # print(sess.run([images, labels]))
    try:
      epochs_loss = [1000]
      epochs_fac = [0]
      lrs = [float(FLAGS.learning_rate)]
      step = 0
      iter = 0
      train_batches = int(NUM_TRAIN_SAMPLES / FLAGS.batch_size)
      val_batches = int(NUM_VAL_SAMPLES / VAL_BATCH_SIZE)
      # summary_writer.add_graph(sess.graph_def)
      # tf.train.write_graph(sess.graph_def, './data_g/','graph.pbtxt')      
      while not coord.should_stop():
        start_time = time.time()
        # Run one step of the model.  The return values are
        # the activations from the `train_op` (which is
        # discarded) and the `loss` op.  To inspect the values
        # of your ops or variables, you may include them in
        # the list passed to sess.run() and the value tensors
        # will be returned in the tuple from the call.
        _, loss_value, fac_value = sess.run([train_op, loss, evaluation],
                feed_dict={lr: lrs[-1]})
        duration = time.time() - start_time
        # Print an overview fairly often.
        if step % 100 == 0:
          summary_str = sess.run([summary_op, loss])
          summary_writer.add_summary(summary_str[0], step)          
          print('Step %d: loss = %.2f (%.3f sec), fac = %.2f' % 
              (step, loss_value, duration, fac_value / FLAGS.batch_size))
        if step % int(NUM_TRAIN_SAMPLES / FLAGS.batch_size) == 0:
          saver.save(sess, FLAGS.train_dir + '/cnnmodel', global_step=step)
          it_loss = it_fac = 0
          print('Validating...')
          for i in range(val_batches):
            if i % 100 == 0: print('batch: %d' % (i))
            vi, vl = sess.run([val_images, val_labels])
            # print(vl.mean())
            loss_val_value, fac_value = sess.run([loss, frame_accuracy], feed_dict={images: vi, labels: vl})
            it_loss += loss_val_value
            it_fac += fac_value / VAL_BATCH_SIZE
          epoch_loss = it_loss / val_batches
          epochs_loss.append(epoch_loss)
          epoch_fac = it_fac / val_batches
          epochs_fac.append(epoch_fac)
          print('Iter %d: cv_loss = %.2f, cv_fac = %.2f' % (iter, epoch_loss, epoch_fac ))
          update_lrs(lrs, epochs_loss, epochs_fac)
          iter += 1
          print(epochs_loss)
          print(lrs)
        
        step += 1
       
    except tf.errors.OutOfRangeError:
      print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
    finally:
      # When done, ask the threads to stop.
      coord.request_stop()
    # Wait for threads to finish.
    coord.join(threads)
    sess.close()