I am trying to perform an image classification task using a pre-trained VGG16 model in Keras. The code I wrote, following the instructions in the Keras application page, is:
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input, decode_predictions
import numpy as np
model = VGG16(weights='imagenet', include_top=True)
img_path = './train/cat.1.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features = model.predict(x)
(inID, label) = decode_predictions(features)[0]
which is quite similar to the code shown in this question already asked in the forum. But in spite of having the include_top parameter as True, I am getting the following error:
Traceback (most recent call last):
File "vgg16-keras-classifier.py", line 14, in <module>
(inID, label) = decode_predictions(features)[0]
ValueError: too many values to unpack
Any help will be deeply appreciated! Thanks!
It's because (according to a function definition which might be found here) a function decode_predictions returns a triple (class_name, class_description, score). This why it claims that there are too many values to unpack.
Related
Actually I have a problem when calling a Matlab script from Python.
import matlab.engine
import os
import random
import numpy as np
a=[str(random.randint(1,3)) for _ in range(3)]
print(a)
eng=matlab.engine.start_matlab()
eng.cd("/Users/dha/Documents/MATLAB/test-matlab/",nargout=0)
sr, state=eng.test_func()
print(sr)
print(state)
In fact I want to return "sr" which is a float and an array of integer "state", e.g. sr = 34.31 and state = [1,2,5]. The function test_func() work well on Matlab, but when I run this in Python from terminal (python test_matlab_engine.py) I received the following error:
Traceback (most recent call last):
File "test_matlab_engine.py", line 10, in <module>
sr, state=eng.mabuc_drl(a)
TypeError: 'float' object is not iterable
Anyone please give me the solution. Thank you so much in advance.
It seems that the result from MATLAB to Python has been cut off. If you have two parameters, you only get one which is the first parameter from the MATLAB. So, the question is how to get two or more parameters.
In a word, you should write this in your Python file:
re = eng.your_function_name(parameter1, parameter2, nargout=2)
where re contains two parameters which come from MATLAB.
You can find more information in the official documentation: Call MATLAB Functions from Python
Below is the code for a simple Bayesian Linear regression. After I obtain the trace and the plots for the parameters, is there any way in which I can save the data that created the plots in a file so that if I need to plot it again I can simply plot it from the data in the file rather than running the whole simulation again?
import pymc3 as pm
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,9,5)
y = 2*x + 5
yerr=np.random.rand(len(x))
def soln(x, p1, p2):
return p1+p2*x
with pm.Model() as model:
# Define priors
intercept = pm.Normal('Intercept', 15, sd=5)
slope = pm.Normal('Slope', 20, sd=5)
# Model solution
sol = soln(x, intercept, slope)
# Define likelihood
likelihood = pm.Normal('Y', mu=sol,
sd=yerr, observed=y)
# Sampling
trace = pm.sample(1000, nchains = 1)
pm.traceplot(trace)
print pm.summary(trace, ['Slope'])
print pm.summary(trace, ['Intercept'])
plt.show()
There are two easy ways of doing this:
Use a version after 3.4.1 (currently this means installing from master, with pip install git+https://github.com/pymc-devs/pymc3). There is a new feature that allows saving and loading traces efficiently. Note that you need access to the model that created the trace:
...
pm.save_trace(trace, 'linreg.trace')
# later
with model:
trace = pm.load_trace('linreg.trace')
Use cPickle (or pickle in python 3). Note that pickle is at least a little insecure, don't unpickle data from untrusted sources:
import cPickle as pickle # just `import pickle` on python 3
...
with open('trace.pkl', 'wb') as buff:
pickle.dump(trace, buff)
#later
with open('trace.pkl', 'rb') as buff:
trace = pickle.load(buff)
Update for someone like me who is still coming over to this question:
load_trace and save_trace functions were removed. Since version 4.0 even the deprecation waring for these functions were removed.
The way to do it is now to use arviz:
with model:
trace = pymc.sample(return_inferencedata=True)
trace.to_netcdf("filename.nc")
And it can be loaded with:
trace = arviz.from_netcdf("filename.nc")
This way works for me :
# saving trace
pm.save_trace(trace=trace_nb, directory=r"c:\Users\xxx\Documents\xxx\traces\trace_nb")
# loading saved traces
with model_nb:
t_nb = pm.load_trace(directory=r"c:\Users\xxx\Documents\xxx\traces\trace_nb")
Following Pickle figures from matplotlib, I am trying to load a figure from a pickle. I am using the same code with the modifications that are suggested in the responses.
Saving script:
import numpy as np
import matplotlib.pyplot as plt
import pickle as pl
# Plot simple sinus function
fig_handle = plt.figure()
x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.plot(x,y)
# plt.show()
# Save figure handle to disk
pl.dump(fig_handle,file('sinus.pickle','wb'))
Loading script:
import matplotlib.pyplot as plt
import pickle as pl
import numpy as np
# Load figure from disk and display
fig_handle = pl.load(open('sinus.pickle', 'rb'))
fig_handle.show()
The saving script produces a file named "sinus.pickle" but the loading file does not show the anticipated figure. Any suggestions?
Python 2.7.13
matplotlib 2.0.0
numpy 1.12.1
p.s. following a suggestion replaced fig_handle.show() with pat.show() which produced an error:
Traceback (most recent call last):
File "/usr/local/lib/python2.7/
site-packages/matplotlib/backends/backend_macosx.py", line 109,
in_set_device_scale
self.figure.dpi = self.figure.dpi / self._device_scale * value
File "/usr/local/lib/python2.7/site-packages/matplotlib/figure.py",
line 416, in _set_dpi
self.callbacks.process('dpi_changed', self)
File "/usr/local/lib/python2.7/site-packages/matplotlib/cbook.py",
line 546, in process
if s in self.callbacks:
AttributeError: 'CallbackRegistry' object has no attribute 'callbacks'
What you call your "loading script" doesn't make any sense.
From the very link that you provided in your question, loading the picked figure is as simple as:
# Load figure from disk and display
fig_handle2 = pl.load(open('sinus.pickle','rb'))
fig_handle2.show()
Final solution included modification of
fig_handle.show()
to
plt.show()
and modification of the backend to "TkAgg", based to an advice given by ImportanceOfBeingErnest
Here is my code
from pandas import read_csv
from pandas.tools.plotting import scatter_matrix
from matplotlib import pyplot
filename = 'iris.data.csv'
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv(filename, names=names)
print(dataset.shape)
print(dataset.head(20))
# Data visualizations
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
pyplot.show()
When i run above code. Then following error is shown
Traceback (most recent call last):
File "/media/k/UBUNTU2/Work and stuff/coding language/Python/Machine learning/exp.py", line 43, in <module>
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
File "/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 2090, in plot_frame
raise ValueError('Invalid chart type given %s' % kind)
ValueError: Invalid chart type given box
Any idea ? What should i do? Please help
Your pandas version (0.14) is already 3 years old. The "box" kind was introduced in version 0.15. Now we are at version 0.20.
The solution is thus to install a newer version of pandas in order to be able to use kind="box" in the plotting wrapper.
If you need to use version 0.14 you can get boxplot using the DataFrame.boxplot() method. The usage according to documentation would be:
df = DataFrame(rand(10,5))
plt.figure();
bp = df.boxplot()
I trained an instance of scikit-learn's TfidfVectorizer and I want to persist it to disk. I saved the IDF matrix (the idf_ attribute) to disk as a numpy array and I saved the vocabulary (vocabulary_) to disk as a JSON object (I'm avoiding pickle, for security and other reasons). I'm trying to do this:
import json
from idf import idf # numpy array with the pre-computed IDFs
from sklearn.feature_extraction.text import TfidfVectorizer
# dirty trick so I can plug my pre-computed IDFs
# necessary because "vectorizer.idf_ = idf" doesn't work,
# it returns "AttributeError: can't set attribute."
class MyVectorizer(TfidfVectorizer):
TfidfVectorizer.idf_ = idf
# instantiate vectorizer
vectorizer = MyVectorizer(lowercase = False,
min_df = 2,
norm = 'l2',
smooth_idf = True)
# plug vocabulary
vocabulary = json.load(open('vocabulary.json', mode = 'rb'))
vectorizer.vocabulary_ = vocabulary
# test it
vectorizer.transform(['foo bar'])
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 1314, in transform
return self._tfidf.transform(X, copy=False)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 1014, in transform
check_is_fitted(self, '_idf_diag', 'idf vector is not fitted')
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 627, in check_is_fitted
raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.utils.validation.NotFittedError: idf vector is not fitted
So, what am I doing wrong? I'm failing to fool the vectorizer object: somehow it knows that I'm cheating (i.e., passing it pre-computed data and not training it with actual text). I inspected the attributes of the vectorizer object but I can't find anything like 'istrained', 'isfitted', etc. So, how do I fool the vectorizer?
Ok, I think I got it: the vectorizer instance has an attribute _tfidf, which in turn must have an attribute _idf_diag. The transform method calls a check_is_fitted function that checks whether whether that _idf_diag exists. (I had missed it because it's an attribute of an attribute.) So, I inspected the TfidfVectorizer source code to see how _idf_diag is created. Then I just added it to the _tfidf attribute:
import scipy.sparse as sp
# ... code ...
vectorizer._tfidf._idf_diag = sp.spdiags(idf,
diags = 0,
m = len(idf),
n = len(idf))
And now the vectorization works.