This MLPClassifier instance is not fitted yet

[Gbouna]

Hello, I am trying to use the package with imagedata generator, but i am getting error. Below is my code

from __future__ import print_function, absolute_import, division, unicode_literals, with_statement
from hypopt import GridSearch
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import train_test_split

# Neural Network imports (simple sklearn Neural Network)
from sklearn.neural_network import MLPClassifier
# Silence neural network SGD convergence warnings.
from sklearn.exceptions import ConvergenceWarning
import warnings
warnings.filterwarnings('ignore', category=ConvergenceWarning)


param_grid = {
    'dropout_rate': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
    'hidden_layer_sizes': [(10,20), (12,30)],
    'batch_size': [16, 32, 64],
    'optimizer':  ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam'],
    'learn_rate':  [0.00001, 0.0001, 0.001, 0.01],
    'init_mode':  ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'],
    'activation':  ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear'],
    'activation': ['relu'],
}


from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1.0/255.0)
test_datagen = ImageDataGenerator(rescale=1.0/255.0)
# prepare iterators
#folder_count = glob('Data/Test')
train = train_datagen.flow_from_directory('train_dir', 
                                             class_mode='categorical', target_size=(224, 224))
val = test_datagen.flow_from_directory('val_dir',
                                           class_mode='categorical',  target_size=(224, 224))
test = test_datagen.flow_from_directory('test_dir',
                                            class_mode='categorical', target_size=(224, 224))

y_train = train.class_indices
y_test = test.class_indices
y_val = val.class_indices
print(y_train)
print(y_val)
print(y_test)


gs_val = GridSearch(model = MLPClassifier(max_iter=6, random_state=0), param_grid=param_grid,\
    parallelize=False)
print("Grid-search using a validation set.\n","-"*79)
%time gs_val.fit(train, val)
test_score = round(gs_val.score(test, y_test), 4)
val_score = round(gs_val.score(val, y_val), 4)
print('\nTEST SCORE (hyper-parameter optimization with validation set):', test_score)
print('VALIDATION SCORE (hyper-parameter optimization with validation set):', val_score)

I added the labels (y) value and use it in the code because it was initially giving the error that

TypeError: score() missing 1 required positional argument: 'y

Although i beleive it should have been fine without it since the imagedata generator generates the lables alongside.

However, after that, i am still getting the error

NotFittedError: This MLPClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator

I will appreciate your response. Thanks