What's the difference between `tf.train.batch()` and `tf.data.Datasets.from_tensor_slices.batch()`?
Recently, I have tried to use ENAS code to automatically design a network on my own datasets.
The code first load data as numpy in main.py
, then assign data to model.py
for example
# main.py
images, labels = read_data(path)
Then in the model.py
it inits the self.x_train
and self.y_train
as follows:
# model.py
class Model(object):
...
with tf.device("/cpu:0"):
# training data
self.num_train_examples = np.shape(images["train"])[0]
self.num_train_batches = (
self.num_train_examples + self.batch_size - 1) // self.batch_size
x_train, y_train = tf.train.shuffle_batch(
[images["train"], labels["train"]], # images['train'] and labels['train'] are both numpy。array
batch_size=self.batch_size,
capacity=50000,
enqueue_many=True,
num_threads=16,
allow_smaller_final_batch=True,
)
Then in the main.py
, the part of running graph is as follows:
# main.py
with tf.train.SingularMonitoredSession(
config=config, hooks=hooks, checkpoint_dir=FLAGS.output_dir) as sess:
start_time = time.time()
while True:
#####################################
###### calculate child ops ########
#####################################
run_ops = [
child_ops["loss"],
child_ops["lr"],
child_ops["grad_norm"],
child_ops["train_acc"],
child_ops["train_op"],
]
loss, lr, gn, tr_acc, _ = sess.run(run_ops)
global_step = sess.run(child_ops["global_step"])
print(sess.run(child_ops['y_train']))
if FLAGS.child_sync_replicas:
actual_step = global_step * FLAGS.num_aggregate
else:
actual_step = global_step
epoch = actual_step // ops["num_train_batches"] # ops["num_train_batches"]
print('Epoch:{}, step:{}'.format(epoch, actual_step))
curr_time = time.time()
What confused me is that the code doesn't define operations, such as self.x_train_next=self.x_train.get_next()
or tf.train.Coordinator()
to load the next iter data in any .py files.
So the follows are my question:
1.Does tf.train.shuffle_batch automatically load the next batch?
2.What's the difference between tf.train.batch()
and tf.data.Datasets.from_tensor_slices.batch()
?
3.The original code uses CIFAR10, when I try to use my own dataset, the image size can only set less than 160*160, otherwise it will raise ValueError: GraphDef cannot be larger than 2GB
. I had tried to use placeholder or TFRecord to load data, but I don't know when the next batch data is loaded, I have no idea how to change the code. So is there any suggestion to load data?
Many thanks!
python tensorflow
add a comment |
Recently, I have tried to use ENAS code to automatically design a network on my own datasets.
The code first load data as numpy in main.py
, then assign data to model.py
for example
# main.py
images, labels = read_data(path)
Then in the model.py
it inits the self.x_train
and self.y_train
as follows:
# model.py
class Model(object):
...
with tf.device("/cpu:0"):
# training data
self.num_train_examples = np.shape(images["train"])[0]
self.num_train_batches = (
self.num_train_examples + self.batch_size - 1) // self.batch_size
x_train, y_train = tf.train.shuffle_batch(
[images["train"], labels["train"]], # images['train'] and labels['train'] are both numpy。array
batch_size=self.batch_size,
capacity=50000,
enqueue_many=True,
num_threads=16,
allow_smaller_final_batch=True,
)
Then in the main.py
, the part of running graph is as follows:
# main.py
with tf.train.SingularMonitoredSession(
config=config, hooks=hooks, checkpoint_dir=FLAGS.output_dir) as sess:
start_time = time.time()
while True:
#####################################
###### calculate child ops ########
#####################################
run_ops = [
child_ops["loss"],
child_ops["lr"],
child_ops["grad_norm"],
child_ops["train_acc"],
child_ops["train_op"],
]
loss, lr, gn, tr_acc, _ = sess.run(run_ops)
global_step = sess.run(child_ops["global_step"])
print(sess.run(child_ops['y_train']))
if FLAGS.child_sync_replicas:
actual_step = global_step * FLAGS.num_aggregate
else:
actual_step = global_step
epoch = actual_step // ops["num_train_batches"] # ops["num_train_batches"]
print('Epoch:{}, step:{}'.format(epoch, actual_step))
curr_time = time.time()
What confused me is that the code doesn't define operations, such as self.x_train_next=self.x_train.get_next()
or tf.train.Coordinator()
to load the next iter data in any .py files.
So the follows are my question:
1.Does tf.train.shuffle_batch automatically load the next batch?
2.What's the difference between tf.train.batch()
and tf.data.Datasets.from_tensor_slices.batch()
?
3.The original code uses CIFAR10, when I try to use my own dataset, the image size can only set less than 160*160, otherwise it will raise ValueError: GraphDef cannot be larger than 2GB
. I had tried to use placeholder or TFRecord to load data, but I don't know when the next batch data is loaded, I have no idea how to change the code. So is there any suggestion to load data?
Many thanks!
python tensorflow
add a comment |
Recently, I have tried to use ENAS code to automatically design a network on my own datasets.
The code first load data as numpy in main.py
, then assign data to model.py
for example
# main.py
images, labels = read_data(path)
Then in the model.py
it inits the self.x_train
and self.y_train
as follows:
# model.py
class Model(object):
...
with tf.device("/cpu:0"):
# training data
self.num_train_examples = np.shape(images["train"])[0]
self.num_train_batches = (
self.num_train_examples + self.batch_size - 1) // self.batch_size
x_train, y_train = tf.train.shuffle_batch(
[images["train"], labels["train"]], # images['train'] and labels['train'] are both numpy。array
batch_size=self.batch_size,
capacity=50000,
enqueue_many=True,
num_threads=16,
allow_smaller_final_batch=True,
)
Then in the main.py
, the part of running graph is as follows:
# main.py
with tf.train.SingularMonitoredSession(
config=config, hooks=hooks, checkpoint_dir=FLAGS.output_dir) as sess:
start_time = time.time()
while True:
#####################################
###### calculate child ops ########
#####################################
run_ops = [
child_ops["loss"],
child_ops["lr"],
child_ops["grad_norm"],
child_ops["train_acc"],
child_ops["train_op"],
]
loss, lr, gn, tr_acc, _ = sess.run(run_ops)
global_step = sess.run(child_ops["global_step"])
print(sess.run(child_ops['y_train']))
if FLAGS.child_sync_replicas:
actual_step = global_step * FLAGS.num_aggregate
else:
actual_step = global_step
epoch = actual_step // ops["num_train_batches"] # ops["num_train_batches"]
print('Epoch:{}, step:{}'.format(epoch, actual_step))
curr_time = time.time()
What confused me is that the code doesn't define operations, such as self.x_train_next=self.x_train.get_next()
or tf.train.Coordinator()
to load the next iter data in any .py files.
So the follows are my question:
1.Does tf.train.shuffle_batch automatically load the next batch?
2.What's the difference between tf.train.batch()
and tf.data.Datasets.from_tensor_slices.batch()
?
3.The original code uses CIFAR10, when I try to use my own dataset, the image size can only set less than 160*160, otherwise it will raise ValueError: GraphDef cannot be larger than 2GB
. I had tried to use placeholder or TFRecord to load data, but I don't know when the next batch data is loaded, I have no idea how to change the code. So is there any suggestion to load data?
Many thanks!
python tensorflow
Recently, I have tried to use ENAS code to automatically design a network on my own datasets.
The code first load data as numpy in main.py
, then assign data to model.py
for example
# main.py
images, labels = read_data(path)
Then in the model.py
it inits the self.x_train
and self.y_train
as follows:
# model.py
class Model(object):
...
with tf.device("/cpu:0"):
# training data
self.num_train_examples = np.shape(images["train"])[0]
self.num_train_batches = (
self.num_train_examples + self.batch_size - 1) // self.batch_size
x_train, y_train = tf.train.shuffle_batch(
[images["train"], labels["train"]], # images['train'] and labels['train'] are both numpy。array
batch_size=self.batch_size,
capacity=50000,
enqueue_many=True,
num_threads=16,
allow_smaller_final_batch=True,
)
Then in the main.py
, the part of running graph is as follows:
# main.py
with tf.train.SingularMonitoredSession(
config=config, hooks=hooks, checkpoint_dir=FLAGS.output_dir) as sess:
start_time = time.time()
while True:
#####################################
###### calculate child ops ########
#####################################
run_ops = [
child_ops["loss"],
child_ops["lr"],
child_ops["grad_norm"],
child_ops["train_acc"],
child_ops["train_op"],
]
loss, lr, gn, tr_acc, _ = sess.run(run_ops)
global_step = sess.run(child_ops["global_step"])
print(sess.run(child_ops['y_train']))
if FLAGS.child_sync_replicas:
actual_step = global_step * FLAGS.num_aggregate
else:
actual_step = global_step
epoch = actual_step // ops["num_train_batches"] # ops["num_train_batches"]
print('Epoch:{}, step:{}'.format(epoch, actual_step))
curr_time = time.time()
What confused me is that the code doesn't define operations, such as self.x_train_next=self.x_train.get_next()
or tf.train.Coordinator()
to load the next iter data in any .py files.
So the follows are my question:
1.Does tf.train.shuffle_batch automatically load the next batch?
2.What's the difference between tf.train.batch()
and tf.data.Datasets.from_tensor_slices.batch()
?
3.The original code uses CIFAR10, when I try to use my own dataset, the image size can only set less than 160*160, otherwise it will raise ValueError: GraphDef cannot be larger than 2GB
. I had tried to use placeholder or TFRecord to load data, but I don't know when the next batch data is loaded, I have no idea how to change the code. So is there any suggestion to load data?
Many thanks!
python tensorflow
python tensorflow
asked Nov 24 '18 at 2:06
marsggbomarsggbo
12
12
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