I was working on Computer Vision in Python. I have to use the AdaBoost algorithm for a purpose.
OpenCV has a function :
cv2.Boost([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]])
which I used like this: model = cv2.Boost(X,cv2.CV_ROW_SAMPLE,Y)
where X is the training data and Y is the response
This is the error which shows up: module 'cv2.cv2' has no attribute 'Boost'
I tried searching for relevant documentation which indexes the function Boost but this error keeps showing up which make no sense
I'm using OpenCV version 3.2.0 and Python version 3.6.1
Related
I'm trying to measure the available space on each of my GPUs using torch.cuda module. However it is returning me the following error.
module 'torch.cuda' has no attribute 'memory_summary'
My code is below
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
print(torch.cuda.get_device_name(i))
a = torch.cuda.memory_summary(torch.device('cuda:{}'.format(i)))
print(a)
Similarly memory_stats, mem_get_info and memory_reserved all are failing.
torch.cuda.memory_summary is introduced in Pytorch 1.4.0. So if your torch install is older than that you won't be able to use it.
I'm trying to use "statsmodels.api" to work with time series data and trying to fit a simple ARIMA model using
sm.tsa.arima_model.ARIMA(dta,(4,1,1)).fit()
but I got the following error
module 'statsmodels.tsa.api' has no attribute 'arima_model'
I'm using 'statsmodels' version 0.9.0 with 'spyder' version 3.2.8 I'd be pleased to get your help thanks
The correct path is :
import statsmodels.api as sm
sm.tsa.ARIMA()
You can view it using a shell that allows autocomplete like ipython.
It is also viewable in the example provided by statsmodels such as this one.
And more informations about package structure may be found here.
I'm building a CNN text classifier using TensorFlow which I want to load in tensorflow-serving and query using the serving apis. When I call the Predict() method on the grcp stub I receive this error: AttributeError: 'grpc._cython.cygrpc.Channel' object has no attribute 'unary_unary'
What I've done to date:
I have successfully trained and exported a model suitable for serving (i.e., the signatures are verified and using tf.Saver I can successfully return a prediction). I can also load the model in tensorflow_model_server without error.
Here is a snippet of the client code (simplified for readability):
with tf.Session() as sess:
host = FLAGS.server
channel = grpc.insecure_channel('localhost:9001')
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'predict_text'
request.model_spec.signature_name = 'predict_text'
x_text = ["space"]
# restore vocab processor
# then create a ndarray with transform_fit using the vocabulary
vocab = learn.preprocessing.VocabularyProcessor.restore('/some_path/model_export/1/assets/vocab')
x = np.array(list(vocab.fit_transform(x_text)))
# data
temp_data = tf.contrib.util.make_tensor_proto(x, shape=[1, 15], verify_shape=True)
request.inputs['input'].CopyFrom(tf.contrib.util.make_tensor_proto(x, shape=[1, 15], verify_shape=True))
# get classification prediction
result = stub.Predict(request, 5.0)
Where I'm bending the rules: I am using tensorflow-serving-apis in Python 3.5.3 when pip install is not officially supported. Various posts (example: https://github.com/tensorflow/serving/issues/581) have reported that using tensorflow-serving with Python 3 has been successful. I have downloaded tensorflow-serving-apis package from pypi (https://pypi.python.org/pypi/tensorflow-serving-api/1.5.0)and manually pasted into the environment.
Versions: tensorflow: 1.5.0, tensorflow-serving-apis: 1.5.0, grpcio: 1.9.0rc3, grcpio-tools: 1.9.0rcs, protobuf: 3.5.1 (all other dependency version have been verified but are not included for brevity -- happy to add if they have utility)
Environment: Linux Mint 17 Qiana; x64, Python 3.5.3
Investigations:
A github issue (https://github.com/GoogleCloudPlatform/google-cloud-python/issues/2258) indicated that a historical package triggered this error was related to grpc beta.
What data or learning or implementation am I missing?
beta_create_PredictionService_stub() is deprecated. Try this:
from tensorflow_serving.apis import prediction_service_pb2_grpc
...
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
Try to use grpc.beta.implementations.insecure_channel instead of grpc.insecure_channel.
See example code here.
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!
The following works in rpy2 2.0.6:
robjects.r('M = lm(...)')
M = robjects.r('M')
coefficients = M.r['coefficients'][0]
But after I upgraded to rpy2 2.3.8, the above fails with the message
AttributeError: 'ListVector' object has no attribute 'r'
What do I need to change to make this work in 2.3.8?
I am not certain that the code snippet you provide worked with rpy2-2.0.x
The documentation, section Introduction, is showing how to extract coefficients from linear models:
http://rpy.sourceforge.net/rpy2/doc-2.1/html/introduction.html#linear-models