While trying to run my Keras code on GPU (CUDA installed), I am not able to execute the following statement, as has been suggested on many online references.
set THEANO_FLAGS="mode=FAST_RUN,device=gpu,floatX=float32" & python theanogpu_example.py
I am getting the following error.
ValueError: Invalid value ("FAST_RUN,device=gpu,floatX=float32") for configurati
on variable "mode". Valid options are ('Mode', 'DebugMode', 'FAST_RUN', 'NanGuar
dMode', 'FAST_COMPILE', 'DEBUG_MODE')
I have tried the other mode suggested as well from inside the code.
import theano
theano.config.device = 'gpu'
theano.config.floatX = 'float32'
I get the following error.
Exception: Can't change the value of this config parameter after initialization!
Apart from knowing how to make it run, I would also take this opportunity to ask a simpler question. How to know in Windows what is my device i.e. whether 'gpu' or 'gpu1' or 'gpu0'? I have tried all 3 for my case but it hasn't yielded result.
Any suggestions will be appreciated.
The best way is using THEANO_FLAGS before run code, because the config variables cannot be changed after importing Theano, try this:
import os
os.environ['THEANO_FLAGS'] = "device=cuda,force_device=True,floatX=float32"
import theano
Related
I need to use spark in transfer learning to train images ,the error is:
"nnot import name 'resnet50' from 'keras.applications' (/usr/local/lib/python3.7/dist-packages/keras/applications/init.py) "
i try to solve this question since one week, this one is coming from sparkdl, if you add to this file (sparkdl/transformers/keras_applications.py)
**
from tensorflow.keras.applications
**, it will be return normal, but this time you will see another error like
AttributeError: module 'tensorflow' has no attribute 'Session'
i tried on different IDE (Pycharm, Vs Code) but i got the same errors. there are different explications on Stackoverflow. but i'm totally confused now
Why log10() is failing to be recognized when called within a function definition in another script? I'm running Python3 in Anaconda (Jupyter and Spyder).
I've had success with log10() in Jupyter (oddly without even calling "import math"). I've had success with defining functions in a .py file and calling those functions within a separate script. I should be able to perform a simple log10.
I created a new function (in Spyder) and saved it in a file "test_log10.py":
def test_log10(input):
import math
return math.log10(input)
In a separate script (Jupyter notebook) I run :
import test_log10
test_log10.test_log10(10)
I get the following error:
"NameError: name 'log10' is not defined"
What am I missing?
Since I'm not using the environment of Jupyther and alike, I don't know how to correct it in these system, perhaps there is some configuration file over there,check the documentation.
But exactly on the issue, when this happens its because python has not "linked" well something at the import, so I suggest a workaround with the libs in the next way:
import numpy as np
import math
and when you are using functions from math, simply add the np. before, i.e.:
return math.log10(input)
to
return np.math.log10(input)
Exactly I don't know why the mismatch, but this worked for me.
I'm trying to run inference using tf.lite on an mnist keras model that I optimized by doing post-training-quantization according to this
RuntimeError: There is at least 1 reference to internal data
in the interpreter in the form of a numpy array or slice. Be sure to
only hold the function returned from tensor() if you are using raw
data access.
It happens after I resize either the images to be in 4 dimensions, or the interpreter itself as seen in the commented line; since the error before this was something like "expected 4 dimensions but found 3". Here is the code:
import tensorflow as tf
tf.enable_eager_execution()
import numpy as np
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
%matplotlib inline
mnist_train, mnist_test = tf.keras.datasets.mnist.load_data()
images, labels = tf.cast(mnist_test[0], tf.float32)/255.0, mnist_test[1]
images = np.reshape(images,[images.shape[0],images.shape[1],images.shape[2],1])
mnist_ds = tf.data.Dataset.from_tensor_slices((images, labels)).batch(1, drop_remainder = True)
interpreter = tf.lite.Interpreter(model_path="C:\\Users\\USER\\Documents\\python\\converted_quant_model_cnn_5_100.tflite")
#tf.lite.Interpreter.resize_tensor_input(interpreter, input_index="index" , tensor_size=([1,28,28,1]) )
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
for img, label in mnist_ds.take(1):
break
#print(img.get_shape)
interpreter.set_tensor(input_index, img)
interpreter.invoke()
predictions = interpreter.get_tensor(output_index)
I was facing the same issue while running inference on a tflite model.
When traced back, I ended up reading the function in which this runtime error occurs.
The functions responsible for this raising this error are:
def _ensure_safe(self)
and
def _safe_to_run(self)
The function "_safe_to_run()" is called from within the function "_ensure_safe()".
_safe_to_run() function either returns True of False. When it return False the above runtime error occurs.
It returns False when there exist numpy array buffers. This means it is not safe to run tflite calls that may destroy (or alter) internally allocated memory.
So for "_ensure_safe()" function to not raise this runtime error we have to make sure that no numpy arrays pointing to internal buffers are active.
Also, for more clarity note that the function "_ensure_safe()" should be called from any function that will call a function on _interpreter that may reallocate memory. Thus when you call the function
interpreter.allocate_tensors()
as you have mentioned in the code above, the first thing that this "interpreter.allocate_tensors()" function does internally is call the "_ensure_safe()" funciton as the "interpreter.allocate_tensors()" involves altering the internal allocated memory (in this case altering means "allocating" as the name suggests). The other example where "_ensure_safe()" is also called is when "invoke()" function is called. And there are many such functions, but you get the idea.
Now that the root cause and working is known, to overcome this runtime error i.e to have no numpy arrays pointing to internal buffers, we have to clear them.
To clear them:
a). Either shutdown you jupyter notebook and restart the kernel, as this will clear all numpy arrays/slices
b). Or simply load the model again i.e run this line again in you jupyter notebook:
interpreter = tf.lite.Interpreter(model_path="C:\\Users\\USER\\Documents\\python\\converted_quant_model_cnn_5_100.tflite")
This hopefully solves your problem, I assure you it did for me.
If both of these options does not, then in the above explanation I have pointed out "why" this error occurs. So if you find out other ways of "having no numpy arrays pointing to internal buffers", do share.
Reference: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/python/interpreter.py
Just to add what solved it for me. I am using scripts, so it is not related to Jupyter Notebooks.
My problem was that I was using predictions = interpreter.tensor(output_index)
instead predictions = interpreter.get_tensor(output_index).
However, the problem appeared as the same error commented in this thread.
I copy the interpreter.tensor object, then it works, hope it helps you!
change
interpreter.set_tensor(input_index, test2)
interpreter.invoke()
output = interpreter.tensor(output_h1)
result_h1 = np.reshape(output(), (224,224))
to
import copy
interpreter.set_tensor(input_index, test2)
interpreter.invoke()
output = interpreter.tensor(output_h1)
result_h1 = np.reshape(copy.copy(output()), (224,224))
I am using scripts, and for me the problem was multiple instances of the same script running at the same time. Killing the instances solved the issue
What solved the issue for me was to avoid calling
interpreter.get_signature_runner()
to get the signature details if the intent is to use set_tensor and invoke for inference.
accuracy = tf.streaming_accuracy (y_pred,y_true,name='acc')
recall = tf.streaming_recall (y_pred,y_true,name='acc')
precision = tf.streaming_precision(y_pred,y_true,name='acc')
confusion = tf.confuson_matrix(Labels, y_pred,num_classes=10,dtype=tf.float32,name='conf')
For the above code, I have received the same error in past few days.
Isn't the syntax same as it is in the API documentation for tensorflow?
try to use this instead (in a fresh python file, I would suggest create a /tmp/temp.py and run that)
from tensorflow.contrib.metrics import streaming_accuracy
and if this doesn't work then
either there is an installation problem (In which case reinstall)
or you are importing the wrong tensorflow module.
I have some experience with writing machine learning programs in python, but I'm new to TensorFlow and am checking it out. My dev environment is a lubuntu 14.04 64-bit virtual machine. I've created a python 3.5 conda environment from miniconda and installed TensorFlow 0.12 and its dependencies. I began trying to run some example code from TensorFlow's tutorials and encountered this warning when calling fit() in the boston.py example for input functions: source.
WARNING:tensorflow:Rank of input Tensor (1) should be the same as
output_rank (2) for column. Will attempt to expand dims. It is highly
recommended that you resize your input, as this behavior may change.
After some searching in Google, I found other people encountered this same warning:
https://github.com/tensorflow/tensorflow/issues/6184
https://github.com/tensorflow/tensorflow/issues/5098
Tensorflow - Boston Housing Data Tutorial Errors
However, they also experienced errors which prevent code execution from completing. In my case, the code executes with the above warning. Unfortunately, I couldn't find a single answer in those links regarding what caused the warning and how to fix the warning. They all focused on the error. How does one remove the warning? Or is the warning safe to ignore?
Cheers!
Extra info, I also see the following warnings when running the aforementioned boston.py example.
WARNING:tensorflow:*******************************************************
WARNING:tensorflow:TensorFlow's V1 checkpoint format has been
deprecated. WARNING:tensorflow:Consider switching to the more
efficient V2 format: WARNING:tensorflow:
'tf.train.Saver(write_version=tf.train.SaverDef.V2)'
WARNING:tensorflow:now on by default.
WARNING:tensorflow:*******************************************************
and
WARNING:tensorflow:From
/home/kade/miniconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py:1053
in predict.: calling BaseEstimator.predict (from
tensorflow.contrib.learn.python.learn.estimators.estimator) with x is
deprecated and will be removed after 2016-12-01. Instructions for
updating: Estimator is decoupled from Scikit Learn interface by moving
into separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion: est = Estimator(...) -> est =
SKCompat(Estimator(...))
UPDATE (2016-12-22):
I've tracked the warning to this file:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/feature_column_ops.py
and this code block:
except NotImplementedError:
with variable_scope.variable_scope(
None,
default_name=column.name,
values=columns_to_tensors.values()):
tensor = column._to_dense_tensor(transformed_tensor)
tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
variable = [
contrib_variables.model_variable(
name='weight',
shape=[tensor.get_shape()[1], num_outputs],
initializer=init_ops.zeros_initializer(),
trainable=trainable,
collections=weight_collections)
]
predictions = math_ops.matmul(tensor, variable[0], name='matmul')
Note the line: tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
The method signature is: _reshape_real_valued_tensor(input_tensor, output_rank, column_name=None)
The value 2 is hardcoded as the value of output_rank, but the boston.py example is passing in an input_tensor of rank 1. I will continue to investigate.
If you specify the shape of your tensor explicitly:
tf.constant(df[k].values, shape=[df[k].size, 1])
the warning should go away.
After I specify the shape of the tensor explicitly.
continuous_cols = {k: tf.constant(df[k].values, shape=[df[k].size, 1]) for k in CONTINUOUS_COLUMNS}
It works!