I am doing Machine Learning Course from Coursera by University of Washington. In which I am using iPython's graphlab. During practise when I execute below command:
sales = graphlab.SFrame('home_data.gl/')
I am getting the error:
InvalidProductKey Traceback (most recent call last)
<ipython-input-3-c5971b60b216> in <module>()
----> 1 sales=graphlab.SFrame('home_data.gl/')
/opt/conda/lib/python2.7/site-packages/graphlab/data_structures/sframe.pyc in __init__(self, data, format, _proxy)
865 self.__proxy__ = _proxy
866 else:
--> 867 self.__proxy__ = UnitySFrameProxy(glconnect.get_client())
868 _format = None
869 if (format == 'auto'):
/opt/conda/lib/python2.7/site-packages/graphlab/connect/main.pyc in get_client()
138 """
139 if not is_connected():
--> 140 launch()
141 assert is_connected(), ENGINE_START_ERROR_MESSAGE
142 return __CLIENT__
/opt/conda/lib/python2.7/site-packages/graphlab/connect/main.pyc in launch(server_addr, server_bin, server_log, auth_token, server_public_key)
90 if server:
91 server.try_stop()
---> 92 raise e
93 server.set_log_progress(True)
94 # start the client
InvalidProductKey: Product key not found.
(Note the ipython notebook and home_data.gl are in same folder.)
You will need to set the product key for graphlab using the command
graphlab.product_key.set_product_key('PRODUCT KEY HERE')
Related
Recently, I have been trying to parallelize some Sage (Sage 9.4 on a MacBook Pro running OSX 11.2.3) code using Dask. The problem I run into is that while I can run Dask inside Sage, it will break whenever I include any code that isn't "pure python." In particular, it keeps throwing an ImportError. Here is a basic example of what I am running into
import time
from dask import delayed
from dask.distributed import Client
from time import sleep
client = Client(n_workers=4)
def Hello():
1+1 #this line breaks things by adding a sage operation
#if I remove it the code runs fine
return 'Hello World'
z = delayed(Hello)()
z.compute()
This code throws the following error
Traceback
ImportError Traceback (most recent call last)
<timed eval> in <module>
~/.sage/local/lib/python3.9/site-packages/dask/base.py in compute(self, **kwargs)
284 dask.base.compute
285 """
--> 286 (result,) = compute(self, traverse=False, **kwargs)
287 return result
288
~/.sage/local/lib/python3.9/site-packages/dask/base.py in compute(*args, **kwargs)
566 postcomputes.append(x.__dask_postcompute__())
567
--> 568 results = schedule(dsk, keys, **kwargs)
569 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
570
~/.sage/local/lib/python3.9/site-packages/distributed/client.py in get(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)
2669 should_rejoin = False
2670 try:
-> 2671 results = self.gather(packed, asynchronous=asynchronous, direct=direct)
2672 finally:
2673 for f in futures.values():
~/.sage/local/lib/python3.9/site-packages/distributed/client.py in gather(self, futures, errors, direct, asynchronous)
1946 else:
1947 local_worker = None
-> 1948 return self.sync(
1949 self._gather,
1950 futures,
~/.sage/local/lib/python3.9/site-packages/distributed/client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
843 return future
844 else:
--> 845 return sync(
846 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
847 )
~/.sage/local/lib/python3.9/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs)
324 if error[0]:
325 typ, exc, tb = error[0]
--> 326 raise exc.with_traceback(tb)
327 else:
328 return result[0]
~/.sage/local/lib/python3.9/site-packages/distributed/utils.py in f()
307 if callback_timeout is not None:
308 future = asyncio.wait_for(future, callback_timeout)
--> 309 result[0] = yield future
310 except Exception:
311 error[0] = sys.exc_info()
/var/tmp/sage-9.4-current/local/lib/python3.9/site-packages/tornado/gen.py in run(self)
733
734 try:
--> 735 value = future.result()
736 except Exception:
737 exc_info = sys.exc_info()
~/.sage/local/lib/python3.9/site-packages/distributed/client.py in _gather(self, futures, errors, direct, local_worker)
1811 exc = CancelledError(key)
1812 else:
-> 1813 raise exception.with_traceback(traceback)
1814 raise exc
1815 if errors == "skip":
~/.sage/local/lib/python3.9/site-packages/distributed/protocol/pickle.py in loads()
73 return pickle.loads(x, buffers=buffers)
74 else:
---> 75 return pickle.loads(x)
76 except Exception:
77 logger.info("Failed to deserialize %s", x[:10000], exc_info=True)
/var/tmp/sage-9.4-current/local/lib/python3.9/site-packages/sage/rings/integer.pyx in init sage.rings.integer (build/cythonized/sage/rings/integer.c:54201)()
----> 1 r"""
2 Elements of the ring `\ZZ` of integers
3
4 Sage has highly optimized and extensive functionality for arithmetic with integers
5 and the ring of integers.
/var/tmp/sage-9.4-current/local/lib/python3.9/site-packages/sage/rings/rational.pyx in init sage.rings.rational (build/cythonized/sage/rings/rational.cpp:40442)()
98
99
--> 100 import sage.rings.real_mpfr
101 import sage.rings.real_double
102 from libc.stdint cimport uint64_t
/var/tmp/sage-9.4-current/local/lib/python3.9/site-packages/sage/rings/real_mpfr.pyx in init sage.rings.real_mpfr (build/cythonized/sage/rings/real_mpfr.c:46795)()
----> 1 r"""
2 Arbitrary Precision Real Numbers
3
4 AUTHORS:
5
/var/tmp/sage-9.4-current/local/lib/python3.9/site-packages/sage/libs/mpmath/utils.pyx in init sage.libs.mpmath.utils (build/cythonized/sage/libs/mpmath/utils.c:9062)()
----> 1 """
2 Utilities for Sage-mpmath interaction
3
4 Also patches some mpmath functions for speed
5 """
/var/tmp/sage-9.4-current/local/lib/python3.9/site-packages/sage/rings/complex_mpfr.pyx in init sage.rings.complex_mpfr (build/cythonized/sage/rings/complex_mpfr.c:34594)()
----> 1 """
2 Arbitrary Precision Floating Point Complex Numbers
3
4 AUTHORS:
5
/var/tmp/sage-9.4-current/local/lib/python3.9/site-packages/sage/rings/complex_double.pyx in init sage.rings.complex_double (build/cythonized/sage/rings/complex_double.c:25284)()
96 from cypari2.convert cimport new_gen_from_double, new_t_COMPLEX_from_double
97
---> 98 from . import complex_mpfr
99
100 from .complex_mpfr import ComplexField
ImportError: cannot import name complex_mpfr
The only other time I have seen an ImportError like this is when I have been running sage inside python and did not include a from sage.all import *, so I am wondering if what is happening is that Dask is trying to run my code in python. I'm also not sure whether this qualifies as a Sage or a Dask problem. Any help would be greatly appreciated!
I am running my code on google colabs but my sequential model is not compiling. I could not create a new Sequential mode from a pre-existing model and compile it. I have called compile function from model but I have no idea why the error pops up. I don't know where I messed up. Actually Im doing something different but this is a simplied code with intention to find error.
train_path = '/content/drive/My Drive/ML/train'
model_custom=load_model("/content/drive/My Drive/ML/model_after_vgg_back_up.h5")
model=Sequential()
for layer in model_custom.layers:
model.add(layer)
model.compile(Adam(lr=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])
train_batches = ImageDataGenerator().flow_from_directory(train_path,target_size=[224,224],classes=['mom','nanu','prabesh','sanu'],batch_size=15)
valid_path = '/content/drive/My Drive/ML/test1'
valid_batches = ImageDataGenerator().flow_from_directory(valid_path,target_size=[224,224],classes=['mom','nanu','prabesh','sanu'],batch_size=15)
model.fit_generator(train_batches,validation_data=valid_batches,validation_steps=1,steps_per_epoch=35,verbose=2,epochs=3)
Traceback is like follows:
Found 569 images belonging to 4 classes.
Found 12 images belonging to 4 classes.
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-16-c160c47bcd7c> in <module>()
7 valid_path = '/content/drive/My Drive/ML/test1'
8 valid_batches = ImageDataGenerator().flow_from_directory(valid_path,target_size=[224,224],classes=['mom','nanu','prabesh','sanu'],batch_size=15)
----> 9 model.fit_generator(train_batches,validation_data=valid_batches,validation_steps=1,steps_per_epoch=35,verbose=2,epochs=3)
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1416 use_multiprocessing=use_multiprocessing,
1417 shuffle=shuffle,
-> 1418 initial_epoch=initial_epoch)
1419
1420 #interfaces.legacy_generator_methods_support
/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
38
39 do_validation = bool(validation_data)
---> 40 model._make_train_function()
41 if do_validation:
42 model._make_test_function()
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _make_train_function(self)
494 def _make_train_function(self):
495 if not hasattr(self, 'train_function'):
--> 496 raise RuntimeError('You must compile your model before using it.')
497 self._check_trainable_weights_consistency()
498 if self.train_function is None:
RuntimeError: You must compile your model before using it.
I try to implement a simple ARMA model, however have serious difficulties getting it to run. When adding a parameter to the error term everything works fine (see the return x_m1 + a*e statement, commented out below), however if I add a parameter to the auto regressive part, I get a FloatingPointError or LinAlgError or PositiveDefiniteError, depending on the initialization method I use.
The code is also put into a gist you can find here. The model definition is replicated here:
with pm.Model() as model:
a = pm.Normal("a", 0, 1)
sigma = pm.Exponential('sigma', 0.1, testval=F(.1))
e = pm.Normal("e", 0, sigma, shape=(N-1,))
def x(e, x_m1, a):
# return x_m1 + a*e
return a*x_m1 + e
x, updates = theano.scan(
fn=x,
sequences=[e],
outputs_info=[tt.as_tensor_variable(data.iloc[0])],
non_sequences=[a]
)
x = pm.Deterministic('x', x)
lam = pm.Exponential('lambda', 5.0, testval=F(.1))
y = pm.StudentT("y", mu=x, lam=lam, nu=1, observed=data.values[1:]) #
with model:
trace = pm.sample(2000, init="NUTS", n_init=1000)
Here the errors respective to the initialization methods:
"ADVI" / "ADVI_MAP": FloatingPointError: NaN occurred in ADVI optimization.
"MAP": LinAlgError: 35-th leading minor not positive definite
"NUTS": PositiveDefiniteError: Scaling is not positive definite. Simple check failed. Diagonal contains negatives. Check indexes [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71]
For details on the error messages, please look at this github issue posted at pymc3.
To be explicit, I really would like to have a scan-like solution which is easily extendable to for instance a full ARMA model. I know that one can represent the presented AR(1) model without scan by defining logP as already done in pymc3/distributions/timeseries.py#L18-L46, however I was not able to extend this vectorized style to a full ARMA model. The use of theano.scan seems preferable I think.
Any help is highly appriciated!
This program insists that 35 is a prime number even though, going through it step-by-step, the program should reach the point where it calculates 35%5 and then ignore the number (because the result is 0.) I haven't checked every single number but it seems to display only primes otherwise (except for numbers that are anologous to 35 like 135.)
print ('How many prime numbers do you require?')
primes = io.read("*n")
print ('Here you go:')
num,denom,num_primes=2,2,0
while num_primes<primes do
if denom<num then
if num%denom==0 then
num=num+1
else
denom=denom+1
end
else
print(num)
num=num+1
num_primes=num_primes+1
denom=2
end
end
Sample output:
How many prime numbers do you require?
50
Here you go:
2
3
5
7
11
13
17
19
23
27
29
31
35
37
41
43
47
53
59
61
67
71
73
79
83
87
89
95
97
101
103
107
109
113
119
123
127
131
135
137
139
143
147
149
151
157
163
167
173
179
You aren't resetting denom in the % case.
if num%denom==0 then
num=num+1
else
So when you fall-through this test you start testing the next number starting from the previous denominator instead of from 2 again.
Simple debugging print lines in the loop printing out denom and num would have shown this to you (as, in fact, that's exactly how I found it). You only need to three prime numbers output to see the issue.
Fixed it, set denom=2 after num=num+1
print ('How many prime numbers do you require?')
primes = io.read("*n")
print ('Here you go:')
num,denom,num_primes=2,2,0
while num_primes<primes do
if denom<num then
if num%denom==0 then
num=num+1
denom=2
else
denom=denom+1
end
else
print(num)
num=num+1
num_primes=num_primes+1
denom=2
end
end
I am brand new to programming in general, and am working on a project for which I need to capture images from my webcam (possibly using OpenCV), and save the images as pgm files.
What's the simplest way to do this? Willow Garage provides this code for image capturing:
http://opencv.willowgarage.com/wiki/CameraCapture
Using this code as a base, how might I modify it to:
capture an image from the live cam every 2 seconds
save the images to a folder in pgm format
Thanks so much for any help you can provide!
First of all, please use newer site - opencv.org. Using outdated references leads to chain effect, when new users see old references, read old docs and post old links again.
There's actually no reason to use old C API. Instead, you can use newer C++ interface, which, among other things, handles capturing video gracefully. Here's shortened version of example from docs on VideoCapture:
#include "opencv2/opencv.hpp"
using namespace cv;
int main(int, char**)
{
VideoCapture cap(0); // open the default camera
if(!cap.isOpened()) // check if we succeeded
return -1;
for(;;)
{
Mat frame;
cap >> frame; // get a new frame from camera
// do any processing
imwrite("path/to/image.png", frame);
if(waitKey(30) >= 0) break; // you can increase delay to 2 seconds here
}
// the camera will be deinitialized automatically in VideoCapture destructor
return 0;
}
Also, if you are new to programming, consider using Python interface to OpenCV - cv2 module. Python is often considered simpler than C++, and using it you can play around with OpenCV functions right in an interactive console. Capturing video with cv2 looks something like this (adopted code from here):
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
# do what you want with frame
# and then save to file
cv2.imwrite('path/to/image.png', frame)
if cv2.waitKey(30) & 0xFF == ord('q'): # you can increase delay to 2 seconds here
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
Since ffriend's answer is only partially true, I'll add some more to it (in C++). The author of this question asks explicitly for exporting to PGM (PXM file format that stores each pixel in 8 bits) and not PNG (as ffriend describes in his/her reply). The main issue here is that the official documentation for imwrite is omho not clear about this matter at all:
For PPM, PGM, or PBM, it can be a binary format flag ( CV_IMWRITE_PXM_BINARY ), 0 or 1. Default value is 1.
If we read the sentence in normal English, we have a list of options: CV_IMWRITE_PXM_BINARY, 0 or 1. There is no mention that those can and actually are supposed to be combined! I had to experiment a little bit (I also needed to store 8-bit images for my project) and finally got to the desired solution:
std::vector<int> compression_params; // Stores the compression parameters
compression_params.push_back(CV_IMWRITE_PXM_BINARY); // Set to PXM compression
compression_params.push_back(0); // Set type of PXM in our case PGM
const std::string imageFilename = "myPGM.pgm"; // Some file name - C++ requires an std::string
cv::imwrite(imageFilename, myImageMatrix, compression_params); // Write matrix to file
My investigation was also fueled by this question where the author was (maybe still is) struggling with the very same issue and also by some basic information on the PXM format, which you can find here.
The result (only part of the image) is displayed below:
P2
32 100
255
121 237 26 102 88 143 67 224 160 164 238 8 119 195 138 16 176 244 72 106 72 211 168 45 250 161 37 1 96 130 74 8
126 122 227 86 106 120 102 150 185 218 164 232 111 230 207 191 39 222 236 78 137 71 174 96 146 122 117 175 34 245 6 125
124 121 241 67 225 203 118 209 227 168 175 40 90 19 197 190 40 254 68 90 88 242 136 32 123 201 37 35 99 179 198 163
97 161 129 35 48 140 234 237 98 73 105 77 211 234 88 176 152 12 68 93 159 184 238 5 172 252 38 68 200 130 194 216
160 188 21 53 16 79 71 54 124 234 34 92 246 49 0 17 145 102 72 42 105 252 81 63 161 146 81 16 72 104 66 41
189 100 252 37 13 91 71 40 123 246 33 157 67 96 71 59 17 196 96 110 109 116 253 30 42 203 69 53 97 188 90 68
101 36 84 5 41 59 80 8 107 160 168 9 194 8 71 55 152 132 232 102 12 96 213 24 134 208 1 55 64 43 74 22
92 77 30 44 139 96 70 152 160 146 142 8 87 243 11 91 49 196 104 250 72 67 159 44 240 225 69 29 34 115 42 2
109 176 145 90 137 172 65 25 162 57 169 92 214 211 72 94 149 20 104 56 27 67 218 17 203 182 5 124 138 2 130 48
121 225 25 106 89 76 69 189 34 25 173 8 114 83 72 52 145 154 64 40 91 2 251 53 251 237 20 124 82 2 194 42 ...
Which is exactly what is required in this case. You can see the "P2" marking at the top and also the values are clearly from 0 to 255, which is exactly 8 bits per pixel.
Read most of the answers but none of them could satisfy my requirement. Here's how I implemented it.
This program will use webcam as a camera and clicks picture when you press 'c' - we can change the condition, then make it to click pictures automatically after certain interval
# Created on Sun Aug 12 12:29:05 2018
# #author: CodersMine
import cv2
video_path = 0 # 0 internal cam, 1 external webcam
cap = cv2.VideoCapture(video_path)
img_ctr = 0 # To Generate File Names
while(True):
ret, frame = cap.read()
cv2.imshow("imshow",frame)
key = cv2.waitKey(1)
if key==ord('q'): # Quit
break
if key==ord('c'): # Capture
cv2.imshow("Captured",frame)
flag = cv2.imwrite(f"image{img_ctr}.png", frame)
print(f"Image Written {flag}")
img_ctr += 1
# Release the Camera
cap.release()
cv2.destroyAllWindows()
If you don't need superaccurate 2seconds then simply put a sleep(2) or Sleep(2000) in the while(1) loop to wait fro 2seconds before each grab,
Write images with cvSaveImage() just put the extention .pgm on the filename and it will use pgm.
I believe that the format is chosen from the extension of the filename - so assuming your opencv lib's are linked against the appropriate libs you can do something like: (this is from memory, might not be correct.)
CvCapture* capture = cvCaptureFromCam(0);
IplImage* frame = cvQueryFrame(capture);
while (1) {
frame = cvQueryFrame(capture);
cvSaveImage("foo.pgm", &frame);
sleep(2);
}
cvReleaseImage(&frame);
cvReleaseCapture(&capture);