ValueError Traceback (most recent call last) in () ----> 1 prediction = model.predict(image_resized.reshape(1,50,50,3)) 2 print('Prediction Score:\n',prediction[0]) ValueError: cannot reshape array of size 2352 into shape (1,50,50,3)
By just looking at what you have posted you should replace image_resized.reshape(1,50,50,3) with image_resized.reshape(1,28,28,3)
Related
I tried to find a similar question, but still couldnt find the solution.
I am working on a dataframe with pandas.
The following code is nor working. It is only working for the first row on the dataframe. Already for the second row I am getting the error. as below. Maybe somebody sees the mistake and can help :)
census2=census_df[census_df["SUMLEV"]==50]
list=census2["CTYNAME"].tolist()
max=0
for county1 in list:
countylist=[]
df1=census2[census2["CTYNAME"]==county1]
countylist.append(df1["POPESTIMATE2010"].item())
countylist.append(df1["POPESTIMATE2011"].item())
countylist.append(df1["POPESTIMATE2012"].item())
countylist.append(df1["POPESTIMATE2013"].item())
countylist.append(df1["POPESTIMATE2014"].item())
countylist.append(df1["POPESTIMATE2015"].item())
countylist.sort()
difference=countylist[5]-countylist[0]
if difference > max:
max=difference
maxcounty=county1
print(maxcounty)
print(max)
[54660, 55253, 55175, 55038, 55290, 55347]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-340aeaf28039> in <module>()
12 countylist=[]
13 df1=census2[census2["CTYNAME"]==county1]
---> 14 countylist.append(df1["POPESTIMATE2010"].item())
15 countylist.append(df1["POPESTIMATE2011"].item())
16 countylist.append(df1["POPESTIMATE2012"].item())
/opt/conda/lib/python3.6/site-packages/pandas/core/base.py in item(self)
829 """
830 try:
--> 831 return self.values.item()
832 except IndexError:
833 # copy numpy's message here because Py26 raises an IndexError
ValueError: can only convert an array of size 1 to a Python scalar
I had been trying to replicated an online tutorial for plotting confusion matrix but got recursion error, tried resetting the recursion limit but still the error persists. The code is a below:
log = LogisticRegression()
log.fit(x_train,y_train)
pred_log = log.predict(x_train)
confusion_matrix(y_train,pred_log)
The error I got is :
---------------------------------------------------------------------------
RecursionError Traceback (most recent call last)
<ipython-input-57-4b8fbe47e72d> in <module>
----> 1 (confusion_matrix(y_train,pred_log))
<ipython-input-48-92d5242f8580> in confusion_matrix(test_data, pred_data)
1 def confusion_matrix(test_data,pred_data):
----> 2 c_mat = confusion_matrix(test_data,pred_data)
3 return pd.DataFrame(c_mat)
... last 1 frames repeated, from the frame below ...
<ipython-input-48-92d5242f8580> in confusion_matrix(test_data, pred_data)
1 def confusion_matrix(test_data,pred_data):
----> 2 c_mat = confusion_matrix(test_data,pred_data)
3 return pd.DataFrame(c_mat)
RecursionError: maximum recursion depth exceeded
The shape of the train and test data is as below
x_train.shape,y_train.shape,x_test.shape,y_test.shape
# ((712, 7), (712,), (179, 7), (179,))
Tried with: sys.setrecursionlimit(1500)
But still no resolution.
Looks like you are recursively calling the same function. Try changing the outer function name.
1 def confusion_matrix(test_data,pred_data):
----> 2 c_mat = confusion_matrix(test_data,pred_data)
3 return pd.DataFrame(c_mat)
To
def confusion_matrix_pd_convertor(test_data,pred_data):
c_mat = confusion_matrix(test_data,pred_data)
return pd.DataFrame(c_mat)
log = LogisticRegression()
log.fit(x_train,y_train)
pred_log = log.predict(x_train)
confusion_matrix_pd_convertor(y_train,pred_log)
Not able to reshape the image in mnist dataset using sklean
This is the starting portion of my code just load the data
some_digit = X[880]
some_digit_image = some_digit.reshape(28, 28)
ERROR PART
ValueError Traceback (most recent call last)
<ipython-input-15-4d618bdb57bc> in <module>
1 some_digit = X[880]
----> 2 some_digit_image = some_digit.reshape(28,28)
ValueError: cannot reshape array of size 64 into shape (28,28)
You can only reshape it into a 8, 8 array. 8x8=64
try:
some_digit = X[880]
some_digit_image = some_digit.reshape(8, 8)
How to find Top features from Naive Bayes using sklearn pipeline
Hi all,
I am trying to apply Naive Bayes(MultinomialNB ) using pipelines and i came up with the code. However I am interested in finding top 10 positve and negative words , but not able to succeed. when I searched , I got the code for finding top features which i mentioned below. However when i tried using the code using pipeline i am getting the error which i mentioned below. I tried searching exhaustively , but got the code without using pipeline.But when i use the code with my output from pipeline, it is not working. COuld you please help me on how to find feature importance from pipeline output.
# Pipeline dictionary
pipelines = {
'bow_MultinomialNB' : make_pipeline(
CountVectorizer(),
preprocessing.Normalizer(),
MultinomialNB()
)
}
# List tuneable hyperparameters of our pipeline
pipelines['bow_MultinomialNB'].get_params()
# BOW - MultinomialNB hyperparameters
bow_MultinomialNB_hyperparameters = {
'multinomialnb__alpha' : [1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]
}
# Create hyperparameters dictionary
hyperparameters = {
'bow_MultinomialNB' : bow_MultinomialNB_hyperparameters
}
tscv = TimeSeriesSplit(n_splits=3) #For time based splitting
for name, pipeline in pipelines.items():
print("NAME:",name)
print("PIPELINE:",pipeline)
%time
# Create empty dictionary called fitted_models
fitted_models = {}
# Loop through model pipelines, tuning each one and saving it to fitted_models
for name, pipeline in pipelines.items():
# Create cross-validation object from pipeline and hyperparameters
model = GridSearchCV(pipeline, hyperparameters[name], cv=tscv, n_jobs=1,verbose=1)
# Fit model on X_train, y_train
model.fit(X_train, y_train)
# Store model in fitted_models[name]
fitted_models[name] = model
# Print '{name} has been fitted'
print(name, 'has been fitted.')
FEAURE IMPORTANCE:-
pipelines['bow_MultinomialNB'].steps[2][1].classes__
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-125-7d45b007e86b> in <module>()
----> 1 pipelines['bow_MultinomialNB'].steps[2][1].classes_
AttributeError: 'MultinomialNB' object has no attribute 'classes_'
pipelines['bow_MultinomialNB'].steps[0][1].get_feature_names()
---------------------------------------------------------------------------
NotFittedError Traceback (most recent call last)
<ipython-input-126-2883929221d1> in <module>()
----> 1 pipelines['bow_MultinomialNB'].steps[0][1].get_feature_names()
~\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in get_feature_names(self)
958 def get_feature_names(self):
959 """Array mapping from feature integer indices to feature name"""
--> 960 self._check_vocabulary()
961
962 return [t for t, i in sorted(six.iteritems(self.vocabulary_),
~\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in _check_vocabulary(self)
301 """Check if vocabulary is empty or missing (not fit-ed)"""
302 msg = "%(name)s - Vocabulary wasn't fitted."
--> 303 check_is_fitted(self, 'vocabulary_', msg=msg),
304
305 if len(self.vocabulary_) == 0:
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_is_fitted(estimator, attributes, msg, all_or_any)
766
767 if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
--> 768 raise NotFittedError(msg % {'name': type(estimator).__name__})
769
770
NotFittedError: CountVectorizer - Vocabulary wasn't fitted.
x=pipelines['bow_MultinomialNB'].steps[0][1]._validate_vocabulary()
x.get_feature_names()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-120-f620c754a34e> in <module>()
----> 1 x.get_feature_names()
AttributeError: 'NoneType' object has no attribute 'get_feature_names'
Regards,
Shree
I have created a RDD named Distance . All are float values. I intend to add them and find the total sum .
print(Distance.take(5))
Output : [802.0, 1055.0, 919.0, 204.0, 951.0]
print(sum(Distance.take(5)))
Output : 3931.0
totalDistance=Distance.reduce(lambda x,y:(x+y))
Output: Py4JJavaError Traceback (most recent call last)
<ipython-input-29-874f9e382e38> in <module>()
2 # Reduce takes a function that acts on two elements and returns an object of same type.
3
----> 4 totalDistance=Distance.reduce(lambda x,y:(x+y))