我正在处理转换到数据集API的问题，我想我还没有足够的API经验来了解如何处理以下情况。我们目前使用排队和批处理进行图像增强。我的任务是检查新的数据集API并使用它而不是队列转换现有的实现。
我们想要做的是获取对所有路径的引用，并从该引用处理所有操作。正如您在数据集初始化中看到的，我已将parse_fn映射到数据集本身，然后继续读取文件并从文件名中提取初始值。然而，当我接着调用迭代器next_batch方法然后将这些值传递给get_summary时，我现在在形状周围出现错误。我一直在尝试一些不断改变错误的事情，所以我觉得我应该看看是否有人在SO上看到我可能认为这一切都错了，应该走另一条路。在使用数据集API时，有什么事情是绝对错误的吗？
我不应该再以这种方式打电话吗？我注意到我看到他们将获得批处理的大部分示例，将变量传递给op，然后在变量中捕获它并将其传递给sess.run，但是我还没有找到一种简单的方法来执行此操作。但是我们的设置没有错误，所以这是我采取的方法（但它仍然是错误的）。我将继续尝试追查问题并在此处发布我应该找到什么，但如果有人看到了什么请告诉我。谢谢！
当前错误：
...在get_summary摘要中，acc = sess.run（[self._summary_op， self._accuracy]，feed_dict = feed_dict）ValueError：无法提供值 形状（32，）为Tensor'ph_input_labels：0'，其形状为'（？，1）
下面是调用get_summary方法并触发错误的块：
def perform_train():
if __name__ == '__main__':
#Get all our image paths
filenames = data_layer_train.get_image_paths()
next_batch, iterator = preproc_image_fn(filenames=filenames)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with sess.graph.as_default():
# Set the random seed for tensorflow
tf.set_random_seed(cfg.RNG_SEED)
classifier_network = c_common.create_model(len(products_to_class_dict), is_training=True)
optimizer, global_step_var = c_common.create_optimizer(classifier_network)
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
# Init tables and dataset iterator
sess.run(tf.tables_initializer())
sess.run(iterator.initializer)
cur_epoch = 0
blobs = None
try:
epoch_size = data_layer_train.get_steps_per_epoch()
num_steps = num_epochs * epoch_size
for step in range(num_steps):
timer_summary.tic()
if blobs is None:
#Now populate from our training dataset
blobs = sess.run(next_batch)
# *************** Below is where it is erroring *****************
summary_train, acc = classifier_network.get_summary(sess, blobs["images"], blobs["labels"], blobs["weights"])
...
相信错误发生在preproc_image_fn：
def preproc_image_fn(filenames, images=None, labels=None, image_paths=None, cells=None, weights=None):
def _parse_fn(filename, label, weight):
augment_instance = False
paths=[]
selected_cells=[]
if vals.FIRST_ITER:
#Perform our check of the path to see if _data_augmentation is within it
#If so set augment_instance to true and replace the substring with an empty string
new_filename = tf.regex_replace(filename, "_data_augmentation", "")
contains = tf.equal(tf.size(tf.string_split([filename], "")), tf.size(tf.string_split([new_filename])))
filename = new_filename
if contains is True:
augment_instance = True
core_file = tf.string_split([filename], '\\').values[-1]
product_id = tf.string_split([core_file], ".").values[0]
label = search_tf_table_for_entry(product_id)
weight = data_layer_train.get_weights(product_id)
image_string = tf.read_file(filename)
img = tf.image.decode_image(image_string, channels=data_layer_train._channels)
img.set_shape([None, None, None])
img = tf.image.resize_images(img, [data_layer_train._target_height, data_layer_train._target_width])
#Previously I was returning the below, but I was getting an error from the op when assigning feed_dict stating that it didnt like the dictionary
#retval = dict(zip([filename], [img])), label, weight
retval = img, label, weight
return retval
num_files = len(filenames)
filenames = tf.constant(filenames)
#*********** Setup dataset below ************
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels, weights))
dataset=dataset.map(_parse_fn)
dataset = dataset.repeat()
dataset = dataset.batch(32)
iterator = dataset.make_initializable_iterator()
batch_features, batch_labels, batch_weights = iterator.get_next()
return {'images': batch_features, 'labels': batch_labels, 'weights': batch_weights}, iterator
def search_tf_table_for_entry(self, product_id):
'''Looks up keys in the table and outputs the values. Will return -1 if not found '''
if product_id is not None:
return self._products_to_class_table.lookup(product_id)
else:
if not self._real_eval:
logger().info("class not found in training {} ".format(product_id))
return -1
我创建模型并使用之前使用过的占位符：
...
def create_model(self):
weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)
biases_regularizer = weights_regularizer
# Input data.
self._input_images = tf.placeholder(
tf.float32, shape=(None, self._image_height, self._image_width, self._num_channels), name="ph_input_images")
self._input_labels = tf.placeholder(tf.int64, shape=(None, 1), name="ph_input_labels")
self._input_weights = tf.placeholder(tf.float32, shape=(None, 1), name="ph_input_weights")
self._is_training = tf.placeholder(tf.bool, name='ph_is_training')
self._keep_prob = tf.placeholder(tf.float32, name="ph_keep_prob")
self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, tf.float32))
...
self.create_summaries()
def create_summaries(self):
val_summaries = []
with tf.device("/cpu:0"):
for var in self._act_summaries:
self._add_act_summary(var)
for var in self._train_summaries:
self._add_train_summary(var)
self._summary_op = tf.summary.merge_all()
self._summary_op_val = tf.summary.merge(val_summaries)
def get_summary(self, sess, images, labels, weights):
feed_dict = {self._input_images: images, self._input_labels: labels,
self._input_weights: weights, self._is_training: False}
summary, acc = sess.run([self._summary_op, self._accuracy], feed_dict=feed_dict)
return summary, acc
答案 0 :(得分：1)
由于错误说明：
无法为Tensor＆＃39; ph_input_labels：0＆＃39;提供形状值（32，），其形状为＆＃39;（？，1）
我的猜测是labels get_summary的形状为[32]。你能把它重塑成（32,1）吗？或者可能在_parse_fn之前重塑标签？