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Custom python model : succeed to load but fail to predict/serve


By : Ahmed A
Date : September 30 2020, 11:00 PM
this will help Problem solved: in main.py, simply update the save_model() command with :
code :
mlflow.pyfunc.save_model(
        path=model_path,
        python_model=model,
        code_path=['multi_model.py'],
        conda_env={
            'channels': ['defaults', 'conda-forge'],
            'dependencies': [
                'mlflow=1.2.0',
                'numpy=1.16.5',
                'python=3.6.9',
                'scikit-learn=0.21.3',
                'cloudpickle==1.2.2'
            ],
            'name': 'mlflow-env'
        }
    )


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Why would loading a custom widget in Dojo fail if debugAtAllCosts is false, but succeed if true?


By : user3168228
Date : March 29 2020, 07:55 AM
I hope this helps . After reading about the different loader strategies, I figured my problem had to be with instantiating a dijit in some javascript code before the file had been loaded.
Turned out the offending line was here:
code :
dojo.provide("widget.DatePicker");

dojo.require("dijit._Widget");
dojo.require("dijit._Templated");
dojo.require("dijit.Dialog");

dojo.declare("widget.DatePicker", [dijit._Widget, dijit._Templated],  {

    ...

    //private fields
    _dialogBox:new dijit.Dialog({
        title: "Warning!"
    }),

    ...

});

Why would create table fail via python but succeed on cli?


By : rach75
Date : March 29 2020, 07:55 AM
Hope that helps Although the error you are getting seems to be generated by the first DELIMITER // statement, you have a typo at the last mention of DELIMITER - you wrote it DELIMIETER ; - try to change that and see if that solves your issue.
Update
code :
 DELIMITER //
    CREATE TRIGGER %(table)s_before_insert BEFORE INSERT ON %(table)s
    FOR EACH ROW
    BEGIN
        SET NEW.create_ts = NOW();
        SET NEW.mod_ts = NOW();
        SET NEW.uid = UUID();
    END;// DELIMIETER ; <-- this one is wrong, it should be DELIMITER

Keras: can I use model.predict but not model.predict_generator to predict if I train the model with model.fit_generator


By : Z. Rong
Date : March 29 2020, 07:55 AM
should help you out Yes you can do that Taking a look at the code for fit_generator() you can see that model does not remember how it was trained. In fact you can use all these APIs (fit, fit_generator, predict, predict_generator) on a keras model in any order.

Why does training succeed and predict fail with keras and tensorflow?


By : eliah
Date : March 29 2020, 07:55 AM
hope this fix your issue The problem was in the byte order of cv2.imread() which is Blue-Green-Red whereas ImageDataGenerator uses Red-Green-Blue.
Solution:
code :
def prepare_frame(frame):
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # <- correct byte order!!!
    frame = cv2.resize(frame, (150, 150))
    frame = np.expand_dims(frame, axis=0)
    frame = np.asarray(frame, dtype='int32')
    frame = frame / 255
    return frame

Fasttext how to load a .csv column into model.predict


By : user3222818
Date : March 29 2020, 07:55 AM
hope this fix your issue Take this CSV as example:
index;id;text;author 0;id26305;This process, however, afforded me no means of...;EAP 1;id17569;It never once occurred to me that the fumbling...;HPL 2;id11008;In his left hand was a gold snuff box, from wh...;EAP 3;id27763;How lovely is spring As we looked from Windsor...;MWS 4;id12958;Finding nothing else, not even gold, the Super...;HPL 5;id22965;A youth passed in solitude, my best years spen...;MWS 6;id09674;The astronomer, perhaps, at this point, took r...;EAP 7;id13515;The surcingle hung in ribands from my body. ;EAP 8;id19322;I knew that you could not say to yourself 'ste...;EAP 9;id00912;I confess that neither the structure of langua...;MWS
code :
import pandas as pd
import fastText as ft

# here you load the csv into pandas dataframe
df=pd.read_csv('csv_file.csv',sep=';')

# here you load your fasttext module
model=ft.load_model(MODELPATH)

# line by line, you make the predictions and store them in a list
predictions=[]
for line in df['text']:
    pred_label=model.predict(line, k=-1, threshold=0.5)[0][0]
    predictions.append(pred_label)

# you add the list to the dataframe, then save the datframe to new csv
df['prediction']=predictions
df.to_csv('csv_file_w_pred.csv',sep=';',index=False)
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