[Python] Grid search error : 'super' object has no attribute '__sklearn_tags__'

I am trying to run a gridsearch in my pycharm jupyter notebook.

Getting 'super' object has no attribute 'sklearn_tags' error. This question was asked for RandomizedSearch previously. But none of the answers seem to work for me.

The cell which does the import

import boto3
import pandas as pd
import re
import time
from sklearn.model_selection import train_test_split
import concurrent.futures
import tensorflow as tf
from sklearn.preprocessing import normalize
from scikeras.wrappers import KerasClassifier
import numpy as np
from keras.api.models import Sequential
from keras.api.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.api.optimizers import Adam, SGD
from sklearn.model_selection import GridSearchCV


The code which is written to do the GridSearch:

def create_model2(optimizer='adam', learning_rate=0.001, dropout_rate=0.5,
momentum=0.0, beta_1=0.9):

if optimizer == 'adam':
opt = Adam(learning_rate=learning_rate, beta_1=beta_1)
elif optimizer == 'sgd':
opt = SGD(learning_rate=learning_rate, momentum=momentum)
else:
raise ValueError("Unsupported optimizer")

model = Sequential()
model.add(Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(dropout_rate))
model.add(Dense(10, activation='softmax'))

model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model


model = KerasClassifier(build_fn=create_model2, verbose=0)

param_grid = {
'optimizer': ['adam', 'sgd'],
'learning_rate': [0.001],
'dropout_rate': [0.3],
'batch_size': [64],
'epochs': [5],
'momentum': [0.9],
'beta_1': [0.9]
}

grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=2, n_jobs=1)
grid_result = grid.fit(x_train_normalized, y_train)

print("Best Grid Score: %.4f using %s" % (grid_result.best_score_, grid_result.best_params_)


The requirements file :

pyspark==3.5.1
findspark
seaborn
scipy
pyarrow
pandas
scikit-learn==1.5.2
matplotlib
s3fs
mlxtend
pypdf==5.4.0
reportlab
graphviz
pydotplus
tensorflow==2.16.1
keras==3.9.2
scikeras


Error trace :

---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[123], line 36
25 param_grid = {
26 'optimizer': ['adam', 'sgd'],
27 'learning_rate': [0.001],
(...) 32 'beta_1': [0.9]
33 }
35 grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=2, n_jobs=1)
---> 36 grid_result = grid.fit(x_train_normalized, y_train)

AttributeError: 'super' object has no attribute '__sklearn_tags__'


I know there is a similar question here ,but thats for RandomizedSearch and none of the answers given work for me.

'super' object has no attribute '__sklearn_tags__'

How can I run the gridsearch cv by changing any of the above snippets ? I need to find the best optimizer among adam, sgd, RMSProp etc.... for my training dataframe.

Continue reading...