Python cifar10.inputs方法代码示例

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import inputs [as 别名]
def loss(logits, labels):
#      """Add L2Loss to all the trainable variables.
#      Add summary for "Loss" and "Loss/avg".
#      Args:
#        logits: Logits from inference().
#        labels: Labels from distorted_inputs or inputs(). 1-D tensor
#                of shape [batch_size]
#      Returns:
#        Loss tensor of type float.
#      """
#      # Calculate the average cross entropy loss across the batch.
    labels = tf.cast(labels, tf.int64)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=logits, labels=labels, name='cross_entropy_per_example')
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
    tf.add_to_collection('losses', cross_entropy_mean)

  # The total loss is defined as the cross entropy loss plus all of the weight
  # decay terms (L2 loss).
    return tf.add_n(tf.get_collection('losses'), name='total_loss')
  
### 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import inputs [as 别名]
def evaluate():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default() as g:
    # Get images and labels for CIFAR-10.
    eval_data = FLAGS.eval_data == 'test'
    images, labels = cifar10.inputs(eval_data=eval_data)

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

    # Calculate predictions.
    top_k_op = tf.nn.in_top_k(logits, labels, 1)

    # Restore the moving average version of the learned variables for eval.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)

    # Build the summary operation based on the TF collection of Summaries.
    summary_op = tf.summary.merge_all()

    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)

    while True:
      eval_once(saver, summary_writer, top_k_op, summary_op)
      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs) 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import inputs [as 别名]
def evaluate():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default():
    # Get images and labels for CIFAR-10.
    eval_data = FLAGS.eval_data == 'test'
    images, labels = cifar10.inputs(eval_data=eval_data)

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

    # Calculate predictions.
    top_k_op = tf.nn.in_top_k(logits, labels, 1)

    # Restore the moving average version of the learned variables for eval.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)

    # Build the summary operation based on the TF collection of Summaries.
    summary_op = tf.summary.merge_all()

    graph_def = tf.get_default_graph().as_graph_def()
    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir,
                                            graph_def=graph_def)

    while True:
      eval_once(saver, summary_writer, top_k_op, summary_op)
      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs) 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import inputs [as 别名]
def evaluate(eval_dir):
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default() as g:
    # Get images and labels for CIFAR-10.
    eval_data = FLAGS.eval_data == 'test'
    images, labels = cifar10.inputs(eval_data=eval_data)
    phase = tf.Variable(False, name='is_train', dtype=bool, trainable=False)

    # Build a Graph that computes the logits predictions from the
    # inference model.
    if not FLAGS.vanilla:
      logits = cifar10.inference(images, phase, vd.conv2d)
    else:
      logits = cifar10.inference(images, phase, None)


    # Calculate predictions.
    top_k_op = tf.nn.in_top_k(logits, labels, 1)

    # Restore the moving average version of the learned variables for eval.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)

    # Build the summary operation based on the TF collection of Summaries.
    summary_op = tf.summary.merge_all()

    summary_writer = tf.summary.FileWriter(eval_dir, g)

    while True:
      eval_once(saver, summary_writer, top_k_op, summary_op)
      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs) 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import inputs [as 别名]
def evaluate():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default() as g:
    # Get images and labels for CIFAR-10.
    images, labels = cifar10.inputs(eval_data=FLAGS.eval_data)

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

    logits = tf.cast(logits, "float32")
    labels = tf.cast(labels, "int32")

    # Calculate predictions.
    top_k_op = tf.nn.in_top_k(logits, labels, 1)

    # Restore the moving average version of the learned variables for eval.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)

    # Build the summary operation based on the TF collection of Summaries.
    summary_op = tf.summary.merge_all()

    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)

    while True:
      eval_once(saver, summary_writer, top_k_op, summary_op)
      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs) 

# 需要导入模块: import cifar10 [as 别名]
# 或者: from cifar10 import inputs [as 别名]
def main(_):
    with tf.Graph().as_default() as g:
        with tf.device("/cpu:0"):
            images_eval_train, _ = inputs(batch_size=FLAGS.finetune_batch_size,
                                          validation=FLAGS.validation,
                                          shuffle=True)
            images_eval_test, labels_eval_test = inputs(batch_size=FLAGS.eval_batch_size,
                                                        train=False,
                                                        validation=FLAGS.validation,
                                                        shuffle=False, num_epochs=1)

        with tf.device(FLAGS.device):
            with tf.variable_scope("CNN") as scope:
                # Build graph of finetuning BN stats
                finetune_op = build_finetune_graph(images_eval_train)
                scope.reuse_variables()
                # Build eval graph
                n_correct, m = build_eval_graph(images_eval_test, labels_eval_test)

        init_op = tf.global_variables_initializer()
        saver = tf.train.Saver(tf.global_variables())
        sess = tf.Session()
        sess.run(init_op)
        ckpt = tf.train.get_checkpoint_state(FLAGS.log_dir)
        print("Checkpoints:", ckpt)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
        sess.run(tf.local_variables_initializer()) 
        coord = tf.train.Coordinator()
        tf.train.start_queue_runners(sess=sess, coord=coord)
        print("Finetuning...")
        for _ in range(FLAGS.finetune_iter):
            sess.run(finetune_op)
            
        sum_correct_examples= 0
        sum_m = 0
        try:
            while not coord.should_stop():
                _n_correct, _m = sess.run([n_correct, m])
                sum_correct_examples += _n_correct
                sum_m += _m
        except tf.errors.OutOfRangeError:
            print('Done evaluation -- epoch limit reached')
        finally:
            # When done, ask the threads to stop.
            coord.request_stop()
        print("Test: num_test_examples:{}, num_correct_examples:{}, accuracy:{}".format(
              sum_m, sum_correct_examples, sum_correct_examples/float(sum_m)))