TensorBoard 运行，我可以看到绘图，但是损失似乎没有更新。我可以在每一步打印批量损失，我不确定为什么 TensorBoard 没有反映损失。
我正在尝试按照以下示例了解 TensorFlow Slim ：
已尝试
- 我试图添加一个 FileWriter，即使本教程似乎没有。TensorFlow-Slim 是否仍需要显式 FileWriter？我看到 slim.learning.train中有一个 summary_writer 参数，但它是必需的还是 tf.summary.scalar 被设置为默认值？无论如何，它似乎不会影响图表
- 将 设置
trace_every_n_steps为各种值（1、2、5） - 我还删除并重新生成了生成的点（checkpoint、events.out.fevents.、graph.pbtxt、model.ckpt-0.data/index/meta 等）
代码
我的代码主要来自 教程中的在不同标签集上微调模型。
明确地：
import os
from preprocessing import inception_preprocessing
import numpy as np
import tensorflow as tf
from datasets import flowers
from nets import inception
from tensorflow.contrib import slim
import matplotlib.pyplot as plt
image_size = inception.inception_v1.default_image_size
checkpoints_dir = './tmp/checkpoints'
flowers_data_dir = './tmp/data/tf_records'
if not tf.gfile.Exists(checkpoints_dir):
tf.gfile.MakeDirs(checkpoints_dir)
def load_batch(dataset, batch_size=32, height=299, width=299, is_training=False):
"""Loads a single batch of data.
Args:
dataset: The dataset to load.
batch_size: The number of images in the batch.
height: The size of each image after preprocessing.
width: The size of each image after preprocessing.
is_training: Whether or not we're currently training or evaluating.
Returns:
images: A Tensor of size [batch_size, height, width, 3], image samples that have been preprocessed.
images_raw: A Tensor of size [batch_size, height, width, 3], image samples that can be used for visualization.
labels: A Tensor of size [batch_size], whose values range between 0 and dataset.num_classes.
"""
data_provider = slim.dataset_data_provider.DatasetDataProvider(
dataset, common_queue_capacity=32,
common_queue_min=8)
image_raw, label = data_provider.get(['image', 'label'])
# Preprocess image for usage by Inception.
image = inception_preprocessing.preprocess_image(image_raw, height, width, is_training=is_training)
# Preprocess the image for display purposes.
image_raw = tf.expand_dims(image_raw, 0)
image_raw = tf.image.resize_images(image_raw, [height, width])
image_raw = tf.squeeze(image_raw)
# Batch it up.
images, images_raw, labels = tf.train.batch(
[image, image_raw, label],
batch_size=batch_size,
num_threads=1,
capacity=2 * batch_size)
return images, images_raw, labels
def get_init_fn():
"""Returns a function run by the chief worker to warm-start the training."""
checkpoint_exclude_scopes = ["InceptionV1/Logits", "InceptionV1/AuxLogits"]
exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]
variables_to_restore = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
if not excluded:
variables_to_restore.append(var)
return slim.assign_from_checkpoint_fn(
os.path.join(checkpoints_dir, 'inception_v1.ckpt'),
variables_to_restore)
train_dir = './tmp/inception_finetuned/'
with tf.Graph().as_default():
tf.logging.set_verbosity(tf.logging.INFO)
dataset = flowers.get_split('train', flowers_data_dir)
images, _, labels = load_batch(dataset, height=image_size, width=image_size)
# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(inception.inception_v1_arg_scope()):
logits, _ = inception.inception_v1(images, num_classes=dataset.num_classes, is_training=True)
# Specify the loss function:
one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)
slim.losses.softmax_cross_entropy(logits, one_hot_labels)
total_loss = slim.losses.get_total_loss()
# Create some summaries to visualize the training process:
tf.summary.scalar('losses/Total_Loss', total_loss)
# Specify the optimizer and create the train op:
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train_op = slim.learning.create_train_op(total_loss, optimizer)
train_writer = tf.summary.FileWriter(train_dir)
# train_writer.add_summary()
# Run the training:
final_loss = slim.learning.train(
train_op,
logdir=train_dir,
init_fn=get_init_fn(),
number_of_steps=2,
summary_writer=train_writer,
trace_every_n_steps=2)
print('Finished training. Last batch loss %f' % final_loss)
有关的
有大量与 TensorBoard 问题相关的问题，但在这些情况下，TensorBoard 根本不运行、不显示任何内容或给出某种错误。在我的情况下，TensorBoard 似乎运行没有错误，我可以看到生成的损失图，它似乎没有获取多个值。