[Solved] leaf disease detection using keras

[SuperGops]

I think you need to use the fit or fit_transform function before you predict the classes and use the Binarizer. Have a look at scikit's official documentation for the same.

[sreu13]

@SuperGops

i've entered this code,

def fit_transform(self, n_classes):

but as I run it, I get the indentation Error given below
File"C:/Users/admin/Desktop/plant_disease_classification/plant_disease_classification/untitled1.py", line 42
label_binarizer = LabelBinarizer()
^
IndentationError: expected an indented block

[SuperGops]

@sreu13
fit and fit_transform are actually inbuilt functions found in the scikit-learn library. So I'd suggest you fit your model with the available data using those functions whose application can be found on scikit-learn's documentation and then proceed with the Binarizer.

[sreu13]

@SuperGops
so basically ,i'll have to restart and retrain the model with fit_transform?

[SuperGops]

@sreu13 Yup

[arunksoman]

Could you please follow my steps:

1. Uninstall your current Python 3.7 version
2. Install Python 3.6.5
3. If you are using spyder editor make a change to vscode.
4. Go to integrated terminal of vscode and create a virtual environment

python -m venv venv

5. Activate your virtual environment

.\venv\Scripts\activate

6. Create a requirements.txt over your current working directory. Contents for requirements.txt given below:

h5py==2.8.0
imutils==0.5.1
Keras==2.2.4
Keras-Applications==1.0.6
Keras-Preprocessing==1.0.5
kiwisolver==1.0.1
matplotlib==3.0.2
numpy==1.15.3
opencv-contrib-python==3.4.3.18
Pillow==5.3.0
PyWavelets==1.0.1
scikit-image==0.14.1
scikit-learn==0.20.0
scipy==1.1.0
six==1.11.0
sklearn==0.0
tensorboard==1.12.0
tensorflow==1.12.0
termcolor==1.1.0
toolz==0.9.0

7. Then install necessary packages using requirements.txt file

pip install -r requirements.txt

8. Then run your code within this venv and say what happened as reply here.

[sreu13]

@SuperGops
i also have another pickle file "label_transform", which I got as an output from referring the initial code from gaggle, is there any use of this file?

label_binarizer = LabelBinarizer()
image_labels = label_binarizer.fit_transform(label_list)
pickle.dump(label_binarizer,open('label_transform.pkl', 'wb'))
n_classes = len(label_binarizer.classes_)

Kaggle >https://www.kaggle.com/emmarex/plant-disease-detection-using-keras/data

[sreu13]

@arunksoman
i'll follow this proceedure, but would I be able to deploy this code in raspberry pi 4?

[arunksoman]

@sreu13 As the @SuperGops says you have to use fit_transform. It can be implemented on Raspberry Pi 4. But if it slows down your pi you have use multiprocessing as well as threads to improve that.

[sreu13]

@arunksoman
by multiprocessing, do you mean to use multilabel binarizer, if yes , then I have already used it during the training process.
@SuperGops , fit_transform has also been used in the code prior to using label_binarizer.