omoindrot
commented
5 years ago It looks like your batch size is 0, which is weird.
Can you double check the shape of embeddings?
Closed Luoruizhi closed 5 years ago
omoindrot
commented
5 years ago It looks like your batch size is 0, which is weird.
Can you double check the shape of embeddings?
omoindrot
commented
5 years ago The error is:
InvalidArgumentError (see above for traceback): Feature: img_raw (data type: string) is required but could not be found.
This is because when you created you used the key "features_raw" so you need to modify your code like this:
features={
'label': tf.FixedLenFeature([], tf.int64),
'features_raw' : tf.FixedLenFeature([], tf.string),
})
image = tf.decode_raw(features['features_raw'], tf.uint8)
Luoruizhi
commented
5 years ago Thank you for your answer!I deleted the questions because I tried typesetting, please allow me to upload my questions again
I made an error in generating tfrecord, and the code is as follows:
writer=tf.python_io.TFRecordWriter(path="data/train.tfrecords")
for i in range(train_size):
features_raw=train_x[i].tostring()
example=tf.train.Example(
features=tf.train.Features(
feature={
"features_raw":tf.train.Feature(bytes_list=tf.train.BytesList(value=[features_raw])),
"label":tf.train.Feature(int64_list=tf.train.Int64List(value=train_label[i]))
}
)
)
writer.write(record=example.SerializeToString())
writer.close()import os
import tensorflow as tf
filename_queue = tf.train.string_input_producer(["train.tfrecords"])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'features_raw' : tf.FixedLenFeature([], tf.string),
})
image = tf.decode_raw(features['features_raw'], tf.uint8)
image = tf.reshape(image, [1664])
label = tf.cast(features['label'], tf.int32)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
coord=tf.train.Coordinator()
threads= tf.train.start_queue_runners(coor=coord)
for i in range(20):
example, l = sess.run([image,label])#
print('----------------------------')
print(example, l)
coord.request_stop()
coord.join(threads)InvalidArgumentError (see above for traceback): Feature: img_raw (data type: string) is required but could not be found.
Luoruizhi
commented
5 years ago I'm really sorry, but I have a new problem。 Traceback (most recent call last): File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1322, in _do_call return fn(*args) File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1307, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.CancelledError: Queue '_1_input_producer' is already closed. [[Node: input_producer/input_producer_Close = QueueCloseV2cancel_pending_enqueues=false]] [[Node: IteratorGetNext = IteratorGetNextoutput_shapes=[[?,1664], [?]], output_types=[DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"]] [[Node: IteratorGetNext/_69 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_7_IteratorGetNext", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 50, in
This is part of my code
Input：
def decode_from_tfrecords(filename_queue): filename_queue = tf.train.string_input_producer([filename_queue])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'features_raw' : tf.FixedLenFeature([], tf.string),
})
image = tf.decode_raw(features['features_raw'], tf.float32)
image = tf.reshape(image, [1664])
label = tf.cast(features['label'], tf.int64)
return image, label def train_input_fn(data_dir, params): """Train input function for the MNIST dataset.
Args:
data_dir: (string) path to the data directory
params: (Params) contains hyperparameters of the model (ex: `params.num_epochs`)
"""
# Import data
dataset = tf.data.TFRecordDataset(data_dir)
# Map the parser over dataset, and batch results by up to batch_size
dataset = dataset.map(decode_from_tfrecords)
dataset = dataset.shuffle(params.train_size) # whole dataset into the buffer
dataset = dataset.batch(params.batch_size)
dataset = dataset.repeat(params.num_epochs)
dataset = dataset.prefetch(1) # make sure you always have one batch ready to serve
return datasetModel：
def build_model(is_training, images, params): """Compute outputs of the model (embeddings for triplet loss).
Args:
is_training: (bool) whether we are training or not
images: (dict) contains the inputs of the graph (features)
this can be `tf.placeholder` or outputs of `tf.data`
params: (Params) hyperparameters
Returns:
output: (tf.Tensor) output of the model
"""
out = images
with tf.variable_scope('fc_1'):
out = tf.layers.dense(out, 1024,activation=tf.nn.relu)
with tf.variable_scope('fc_2'):
out = tf.layers.dense(out, params.embedding_size)
return outdef model_fn(features, labels, mode, params): """Model function for tf.estimator
Args:
features: input batch of images
labels: labels of the images
mode: can be one of tf.estimator.ModeKeys.{TRAIN, EVAL, PREDICT}
params: contains hyperparameters of the model (ex: `params.learning_rate`)
Returns:
model_spec: tf.estimator.EstimatorSpec object
"""
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
images = features
# assert images.shape[1:] == [params.image_size, params.image_size, 1], "{}".format(images.shape)
# -----------------------------------------------------------
# MODEL: define the layers of the model
with tf.variable_scope('model'):
# Compute the embeddings with the model
embeddings = build_model(is_training, images, params)
embedding_mean_norm = tf.reduce_mean(tf.norm(embeddings, axis=1))
tf.summary.scalar("embedding_mean_norm", embedding_mean_norm)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {'embeddings': embeddings}
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Define triplet loss
if params.triplet_strategy == "batch_all":
loss, fraction = batch_all_triplet_loss(labels, embeddings,
margin=params.margin,
squared=params.squared)
elif params.triplet_strategy == "batch_hard":
loss = batch_hard_triplet_loss(labels, embeddings, margin=params.margin,
squared=params.squared)
else:
raise ValueError("Triplet strategy not recognized: {}".format(params.triplet_strategy))
# -----------------------------------------------------------
# METRICS AND SUMMARIES
# Metrics for evaluation using tf.metrics (average over whole dataset)
# TODO: some other metrics like rank-1 accuracy?
with tf.variable_scope("metrics"):
eval_metric_ops = {"embedding_mean_norm": tf.metrics.mean(embedding_mean_norm)}
if params.triplet_strategy == "batch_all":
eval_metric_ops['fraction_positive_triplets'] = tf.metrics.mean(fraction)
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=eval_metric_ops)
# Summaries for training
tf.summary.scalar('loss', loss)
if params.triplet_strategy == "batch_all":
tf.summary.scalar('fraction_positive_triplets', fraction)
# tf.summary.image('train_image', images, max_outputs=1)
# Define training step that minimizes the loss with the Adam optimizer
optimizer = tf.train.AdamOptimizer(params.learning_rate)
global_step = tf.train.get_global_step()
if params.use_batch_norm:
# Add a dependency to update the moving mean and variance for batch normalization
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
train_op = optimizer.minimize(loss, global_step=global_step)
else:
train_op = optimizer.minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)I'd appreciate it if you could help me out. I am also very sorry to disturb you repeatedly！
omoindrot
commented
5 years ago Look at this tutorial: https://www.tensorflow.org/guide/datasets#parsing_tfexample_protocol_buffer_messages
def decode_from_tfrecords(serialized_example):
features = tf.parse_single_example(serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'features_raw' : tf.FixedLenFeature([], tf.string),
})
image = tf.decode_raw(features['features_raw'], tf.float32)
image = tf.reshape(image, [1664])
label = tf.cast(features['label'], tf.int64)
return image, label The code runs successfully.Thank you very much!
Hi, thanks for sharing your project,
In my own project, I trained the densenet network first, and then used the output as input for the new network, training a very simple DNN network.
def build_model(is_training, images, params): """Compute outputs of the model (embeddings for triplet loss).
But there were the following bugs: Traceback (most recent call last): File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1322, in _do_call return fn(*args) File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1307, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [64,64,64] vs. [0,0,0] [[Node: Mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ToFloat_1, add_2)]] [[Node: truediv_1/_77 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_168_truediv_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "train.py", line 50, in
estimator.train(lambda: train_input_fn('data/val.tfrecords', params))
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 363, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 843, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 859, in _train_model_default
saving_listeners)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 1059, in _train_with_estimatorspec
, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 567, in run
run_metadata=run_metadata)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1043, in run
run_metadata=run_metadata)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1134, in run
raise six.reraise(original_exc_info)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/six.py", line 693, in reraise
raise value
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1119, in run
return self._sess.run(args, *kwargs)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1191, in run
run_metadata=run_metadata)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 971, in run
return self._sess.run(args, **kwargs)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 900, in run
run_metadata_ptr)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1316, in _do_run
run_metadata)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [64,64,64] vs. [0,0,0]
[[Node: Mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ToFloat_1, add_2)]]
[[Node: truediv_1/_77 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_168_truediv_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'Mul', defined at: File "train.py", line 50, in
estimator.train(lambda: train_input_fn('data/val.tfrecords', params))
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 363, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 843, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 856, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 831, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/popzq/zsl/tensorflow-triplet-loss-master-zsl/model/model_fn.py", line 95, in model_fn
squared=params.squared)
File "/home/popzq/zsl/tensorflow-triplet-loss-master-zsl/model/triplet_loss.py", line 160, in batch_all_triplet_loss
triplet_loss = tf.multiply(mask, triplet_loss)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 337, in multiply
return gen_math_ops.mul(x, y, name)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 4759, in mul
"Mul", x=x, y=y, name=name)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/home/popzq/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1718, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Incompatible shapes: [64,64,64] vs. [0,0,0] [[Node: Mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ToFloat_1, add_2)]] [[Node: truediv_1/_77 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_168_truediv_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
I would appreciate it if you could help me！