I am working with a sequence to sequence language model, and after changing the code to pass custom word embedding weights to the Embeddings layer, I am receiving a OOM error when I try to train on the gpu.
Here is the relevant code:
def create_model(word_map, X_train, Y_train, vocab_size, max_length):
# define model
model = Sequential()
# get custom embedding weights as matrix
embedding_matrix = get_weights_matrix_from_word_map(word_map)
model.add(Embedding(len(word_map)+1, 300, weights=[embedding_matrix], input_length=max_length-1))
model.add(LSTM(50))
model.add(Dense(vocab_size, activation='softmax'))
print(model.summary())
# compile network
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=100, verbose=2)
return model
And here is the full error log from the server:
File "/home2/slp24/thesis/UpdatedLanguageModel_7_31.py", line 335, in create_model_2
model.fit(X_train, Y_train, batch_size=32, epochs=1, verbose=2) ## prev X, y
File "/opt/python-3.4.1/lib/python3.4/site-packages/keras/models.py", line 963, in fit
validation_steps=validation_steps)
File "/opt/python-3.4.1/lib/python3.4/site-packages/keras/engine/training.py", line 1682, in fit
self._make_train_function()
File "/opt/python-3.4.1/lib/python3.4/site-packages/keras/engine/training.py", line 990, in _make_train_function
loss=self.total_loss)
File "/opt/python-3.4.1/lib/python3.4/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/opt/python-3.4.1/lib/python3.4/site-packages/keras/optimizers.py", line 466, in get_updates
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/ops/math_ops.py", line 898, in binary_op_wrapper
y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/framework/ops.py", line 932, in convert_to_tensor
as_ref=False)
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/framework/ops.py", line 1022, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/ops/gradients_impl.py", line 100, in _IndexedSlicesToTensor
value.values, value.indices, value.dense_shape[0], name=name)
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/ops/gen_math_ops.py", line 5186, in unsorted_segment_sum
num_segments=num_segments, name=name)
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
op_def=op_def)
File "/opt/python-3.4.1/lib/python3.4/site-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[845246,300] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: training/Adam/mul_2/y = UnsortedSegmentSum[T=DT_FLOAT, Tindices=DT_INT32, Tnumsegments=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](training/Adam/gradients/embedding_1/Gather_grad/Reshape, training/Adam/gradients/embedding_1/Gather_grad/Reshape_1/_101, training/Adam/mul_2/strided_slice)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Edit:
So far I have tried
Adding batching, started with batch_size=32
I am currently working to decrease the number of output classes from 845,286. I think something went wrong when I calculated the custom embedding matrix, specifically when I was "connecting" the vocabulary token index's assigned during preprocessing and the y_categorical values assigned by Keras that the model uses...
Any help or guidance is greatly appreciated! I have searched many similar issued but have not been able to apply those fixes to my code thus far. Thank you
You're exceeding the memory size of your GPU.
You can:
Train/Predict with smaller batches
Or, if even a batch_size=1 is too much, you need a model with less parameters.
Hint, the length in that tensor (845246) is really really big. Is that the correct length?
I had the same problem with Google Colab GPU
The batch size was 64 and this error has appeared and after I reduced the batch size to 32 it worked properly
Related
I am trying to use pre-trained resnet and fine-tune it using triplet loss. The following code I came up with is a combination of tutorials I found on the topic:
import pathlib
import tensorflow as tf
import tensorflow_addons as tfa
with tf.device('/cpu:0'):
INPUT_SHAPE = (32, 32, 3)
BATCH_SIZE = 16
data_dir = pathlib.Path('/home/user/dataset/')
base_model = tf.keras.applications.ResNet50V2(
weights='imagenet',
pooling='avg',
include_top=False,
input_shape=INPUT_SHAPE,
)
# following two lines are added after edit, originally it was model = base_model
head_model = tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))(base_model.output)
model = tf.keras.Model(inputs=base_model.input, outputs=head_model)
datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=10,
zoom_range=0.1,
)
generator = datagen.flow_from_directory(
data_dir,
target_size=INPUT_SHAPE[:2],
batch_size=BATCH_SIZE,
seed=42,
)
model.compile(
optimizer=tf.keras.optimizers.Adam(0.001),
loss=tfa.losses.TripletSemiHardLoss(),
)
model.fit(
generator,
epochs=5,
)
Unfortunately after running the code I get the following error:
Found 4857 images belonging to 83 classes.
Epoch 1/5
Traceback (most recent call last):
File "ReID/external_process.py", line 35, in <module>
model.fit(
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1098, in fit
tmp_logs = train_function(iterator)
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 840, in _call
return self._stateless_fn(*args, **kwds)
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2829, in __call__
return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1843, in _filtered_call
return self._call_flat(
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1923, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 545, in call
outputs = execute.execute(
File "/home/user/videolytics/venv_python/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 1328 values, but the requested shape has 16
[[{{node TripletSemiHardLoss/PartitionedCall/Reshape}}]] [Op:__inference_train_function_13749]
Function call stack:
train_function
2020-10-23 22:07:09.094736: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Failed precondition: Python interpreter state is not initialized. The process may be terminated.
[[{{node PyFunc}}]]
The dataset directory has 83 subdirectories, one per class and each of this subdirectories contains images of given class. The dimension 1328 in the error output is the batch size (16) times number of classes (83), and the dimension 16 is the batch size (both dimensions change accordingly if I change the BATCH_SIZE.
To be honest I do not really understand the error, so any solution or even any kind of indight where is the problem is deeply appreciated.
The problem is that the TripletSemiHardLoss expects
labels y_true to be provided as 1-D integer Tensor with shape [batch_size] of multi-class integer labels
but the flow_from_directory by default generate categorical labels; using class_mode="sparse" should fix the problem.
I've been trying to save a Yolo v3 model and then Load it back from from an h5 file.
When saving I use the checkpoint (ModelCheckpoint) to save the model (with the parameter save_weights_only
set to False in order to save the WHOLE model).
However, when I tried to recover the same model by using the keras load_model function, I initially get a yolo_head function not found error.
I then tried to add the function as a parameter to the load function as in:
{"yolo_head":yolo_head}
Now, the issue becomes: "TypeError: list indices must be integers or slices, not list" because somehow, there's an error in the loss function (yolo_loss, line 444) when loaded dynamically.
Apparently, the binary code of the loss function is hard copied into the h5 file.
My question is this:
Is there a better/simpler YOLO loss function that I can use THAT DOES NOT refer to other functions or can be easily reloaded?
Thanks in advance,
EDIT 1: Additional Code Snippets,
Keras Checkpoint Callback definition:
checkpoint = ModelCheckpoint(
os.path.join(log_dir, "checkpoint.h5"),
monitor="val_loss",
save_weights_only=False,
save_best_only=True,
period=1,
)
Checkpoint added to model training:
history = model.fit_generator(
data_generator_wrapper(
lines[:num_train], batch_size, input_shape, anchors, num_classes
),
steps_per_epoch=max(1, num_train // batch_size),
validation_data=data_generator_wrapper(
lines[num_train:], batch_size, input_shape, anchors, num_classes
),
validation_steps=max(1, num_val // batch_size),
epochs=epoch1,
initial_epoch=0,
callbacks=[logging, checkpoint],
)
Trying to load the same file 'checkpoint.h5' after pre-training ended:
weights_path = os.path.join(log_dir, "checkpoint.h5")
model = load_model(weights_path, {"yolo_head":yolo_head, "tf":tf, "box_iou":box_iou,'<lambda>': lambda y_true, y_pred: y_pred})
Here is the error stack trace:
File "2_Training/Train_YOLO.py", line 206, in
model = load_model(weights_path, {"yolo_head":yolo_head, "tf":tf,
"box_iou":box_iou,'': lambda y_true, y_pred: y_pred})
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/engine/saving.py", line 419, in load_model
model = _deserialize_model(f, custom_objects, compile)
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/engine/saving.py", line 225, in _deserialize_model
model = model_from_config(model_config, custom_objects=custom_objects)
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/engine/saving.py", line 458, in model_from_config
return deserialize(config, custom_objects=custom_objects)
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/layers/init.py", line 55, in deserialize
printable_module_name='layer')
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object
list(custom_objects.items())))
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/engine/network.py", line 1032, in from_config
process_node(layer, node_data)
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/engine/network.py", line 991, in process_node
layer(unpack_singleton(input_tensors), **kwargs)
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/engine/base_layer.py", line 457, in call
output = self.call(inputs, **kwargs)
File "/Users/nkwedi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/keras/layers/core.py", line 687, in call
return self.function(inputs, **arguments)
File "/Users/nkwedi/Documents/MyProjects/Eroscope/EyeDetectionYOLO/2_Training/src/keras_yolo3/yolo3/model.py", line 444, in yolo_loss
anchors[anchor_mask[l]],
TypeError: list indices must be integers or slices, not list
The Solution for me was to use a Cloud Based Training Platform like Google Collab.
Here's a link to a workable Collab Notebook with GPU enabled:
YOLO v3 Google Collab Tutorial
I am having difficulty implementing the pre-trained Xception model for binary classification over new set of classes. The model is successfully returned from the following function:
#adapted from:
#https://github.com/fchollet/keras/issues/4465
from keras.applications.xception import Xception
from keras.layers import Input, Flatten, Dense
from keras.models import Model
def get_xception(in_shape,trn_conv):
#Get back the convolutional part of Xception trained on ImageNet
model = Xception(weights='imagenet', include_top=False)
#Here the input images have been resized to 299x299x3, so this is the
#same as Xception's native input
input = Input(in_shape,name = 'image_input')
#Use the generated model
output = model(input)
#Only train the top fully connected layers (keep pre-trained feature extractors)
for layer in model.layers:
layer.trainable = False
#Add the fully-connected layers
x = Flatten(name='flatten')(output)
x = Dense(2048, activation='relu', name='fc1')(x)
x = Dense(2048, activation='relu', name='fc2')(x)
x = Dense(2, activation='softmax', name='predictions')(x)
#Create your own model
my_model = Model(input=input, output=x)
my_model.compile(loss='binary_crossentropy', optimizer='SGD')
return my_model
This returns fine, however when I run this code:
model=get_xception(shp,trn_feat)
in_data=HDF5Matrix(str_trn,'/inputs')
labels=HDF5Matrix(str_trn,'/labels')
model.fit(in_data,labels,shuffle="batch")
I get the following error:
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/keras/engine/training.py", line 1576, in fit
self._make_train_function()
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/keras/engine/training.py", line 960, in _make_train_function
loss=self.total_loss)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 87, in wrapper
return func(*args, **kwargs)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/keras/optimizers.py", line 169, in get_updates
v = self.momentum * m - lr * g # velocity
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 705, in _run_op
return getattr(ops.Tensor, operator)(a._AsTensor(), *args)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 865, in binary_op_wrapper
return func(x, y, name=name)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 1088, in _mul_dispatch
return gen_math_ops._mul(x, y, name=name)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 1449, in _mul
result = _op_def_lib.apply_op("Mul", x=x, y=y, name=name)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/tsmith/.virtualenvs/keras/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[204800,2048]
[[Node: training/SGD/mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](SGD/momentum/read, training/SGD/Variable/read)]]
I have been tracing the function calls for hours now and still can't figure out what is happening. The system should be far above and beyond the requirements. System specs:
Ubuntu Version: 14.04.5 LTS
Tensorflow Version: 1.3.0
Keras Version: 2.0.7
28x dual core Inten Xeon processor (1.2 GHz)
4x NVidia GeForce 1080 (8Gb memory each)
Any clues as to what is going wrong here?
Per Yu-Yang, the simplest solution was to reduce the batch size, everything ran fine after that!
I´m having issues feeding one of my placeholders (keep_prob). The error says that I have to feed a float value, but I´m already doing it. I´ve been trying to solve it, but I can´t figure out a solution. My code is here:
Error while running a convolutional network using my own data in Tensorflow
And my error is:
File "<ipython-input-81-fd184c90091e>", line 4, in <module>
keep_prob = tf.placeholder(tf.float32)
File "c:\python36\lib\site-packages\tensorflow\python\ops\array_ops.py",
line 1530, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "c:\python36\lib\site-packages\tensorflow\python\ops\gen_array_ops.py",
line 1954, in _placeholder
name=name)
File "c:\python36\lib\site-
packages\tensorflow\python\framework\op_def_library.py", line 767, in
apply_op
op_def=op_def)
File "c:\python36\lib\site-packages\tensorflow\python\framework\ops.py",
line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "c:\python36\lib\site-packages\tensorflow\python\framework\ops.py",
line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for
placeholder tensor 'Placeholder_17' with dtype float
[[Node: Placeholder_17 = Placeholder[dtype=DT_FLOAT, shape=<unknown>,
_device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Thank you.
I suspect that your error comes not from the line that you think. I can't run your code, but looking at it, my guess is the following:
You are feeding the keep_prob value here, which is OK for training:
train_step.run(feed_dict={x: image_batch_eval, y_: label_batch_eval,
keep_prob: 0.5})
But you are not feeding the keep_prob value here, which you also have to do:
print('Precisión %g' % accuracy.eval(feed_dict={x: image_test_batch_eval,
y_: label_test_batch_eval}))
If you look at your code, the accuracy operation ultimately leeds up to this operation, which needs the placeholder:
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
So, if you are evaluating for example the validation or testing accuracy, then feed value 1.0 otherwise feed whatever you are feeding for training.
Give it a try?
Saving variables
Variables saved in 0.88 seconds
Saving metagraph
Metagraph saved in 35.81 seconds
Saving variables
Variables saved in 0.95 seconds
Saving metagraph
Metagraph saved in 33.20 seconds
Traceback (most recent call last):
Caused by op u'batch', defined at:
File "ava_train.py", line 155, in <module>
image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size, allow_smaller_final_batch=True)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 872, in batch
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 665, in _batch
dequeued = queue.dequeue_up_to(batch_size, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/data_flow_ops.py", line 510, in dequeue_up_to
self._queue_ref, n=n, component_types=self._dtypes, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 1402, in _queue_dequeue_up_to_v2
timeout_ms=timeout_ms, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
self._traceback = _extract_stack()
OutOfRangeError (see above for traceback): FIFOQueue '_1_batch/fifo_queue' is closed and has insufficient elements (requested 100, current size 0)
[[Node: batch = QueueDequeueUpToV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batch/fifo_queue, batch/n)]]
my code is here
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# process same as cifar10.distorted_inputs
log_dir = '../log'
model_dir = '../model'
max_num_epoch = 80
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
num_train_example = len(os.listdir('../images/'))
# Reads pfathes of images together with their labels
image_list, label_list = read_labeled_image_list('../raw.txt')
images = ops.convert_to_tensor(image_list, dtype=dtypes.string)
labels = ops.convert_to_tensor(label_list, dtype=dtypes.int32)
# Makes an input queue
# input_queue = tf.train.slice_input_producer([images, labels], num_epochs=max_num_epoch, shuffle=True)
input_queue = tf.train.slice_input_producer([images, labels], shuffle=True)
image, label = read_images_from_disk(input_queue)
image_size = 240
keep_probability = 0.8
weight_decay = 5e-5
image = preprocess(image, image_size, image_size, None)
batch_size = 100
epoch_size = 1000
embedding_size = 128
# Optional Image and Label Batching
image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size, allow_smaller_final_batch=True)
This is the output of training an image classification model based on 20w images. I set allow_smaller_final_batch=True in batch. After some epochs the OutOfRangeError occured.
I don't know the reason and thanks for the help.
Since you get a OutOfRangeError it could be that you are training for more epochs than max_num_epochs, which will result in the slice_input_producer throwing this exception.
One possible workaround would be to remove the num_epochs=max_num_epochs from your slice_input_producer since this will allow it to produce even after the maximum number of epochs has been reached.
I have battled with this particular error for days. I finally found the cause. You are getting this error because your file is corrupted somewhere. Try running this code on another train and test data