keras gridSearchCV on sklearn One hot Encoded Data - scikit-learn

Question

The problem with this code is that I am giving classifier,
One hot encoded data:
Means:
X-train, X-test, y_train, y_test is one hot encoded.
But the classifier is predicting the output:
y_pred_test, y_pred_train in Numerical form
(which I think is incorrect as well). Can anyone help with this?
This is a dummy example so no concern over low accuracy but just to know why it's predicting the output in not One Hot encoded form.
Thanks !
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
x=pd.DataFrame()
x['names']= np.arange(1,10)
x['Age'] = np.arange(1,10)
y=pd.DataFrame()
y['target'] = np.arange(1,10)
from sklearn.preprocessing import OneHotEncoder, Normalizer
ohX= OneHotEncoder()
x_enc = ohX.fit_transform(x).toarray()
ohY = OneHotEncoder()
y_enc = ohY.fit_transform(y).toarray()
print (x_enc)
print("____")
print (y_enc)
import keras
from keras import regularizers
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.models import load_model
from keras.layers.advanced_activations import LeakyReLU
marker="-------"
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
def create_model(learn_rate=0.001):
model = Sequential()
model.add(Dense(units = 15, input_dim =18,kernel_initializer= 'normal', activation="tanh"))
model.add(Dense(units=9, activation = "softmax"))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])
return model
if __name__=="__main__":
X_train, X_test, y_train, y_test = train_test_split(x_enc, y_enc, test_size=0.33, random_state=42)
print ("\n\n",marker*5," Classification\nX_train shape is: ",X_train.shape,"\tX_test shape is:",X_test.shape)
print ("\ny_train shape is: ",y_train.shape,"\t y_test shape is:",y_test.shape,"\n\n")
norm = Normalizer()
#model
X_train = norm.fit_transform(X_train)
X_test = norm.transform(X_test)
earlyStopping=keras.callbacks.EarlyStopping(monitor='val_loss', patience=0, verbose=0, mode='auto')
model = KerasClassifier(build_fn=create_model, verbose=0)
fit_params={'callbacks': [earlyStopping]}
#grid
# batch_size =[50,100,200, 300,400]
epochs = [2,5]
learn_rate=[0.1,0.001]
param_grid = dict( epochs = epochs, learn_rate = learn_rate)
grid = GridSearchCV(estimator = model, param_grid = param_grid, n_jobs=1)
#Predicting
print (np.shape(X_train), np.shape(y_train))
y_train = np.reshape(y_train, (-1,np.shape(y_train)[1]))
print ("y_train shape after reshaping", np.shape(y_train))
grid_result = grid.fit(X_train, y_train, callbacks=[earlyStopping])
print ("grid score using params: ", grid_result.best_score_, " ",grid_result.best_params_)
#scores
print("SCORES")
print (grid_result.score(X_test,y_test))
# summarize results
#print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
#means = grid_result.cv_results_['mean_test_score']
#stds = grid_result.cv_results_['std_test_score']
#params = grid_result.cv_results_['params']
#for mean, stdev, param in zip(means, stds, params):
# print("%f (%f) with: %r" % (mean, stdev, param))
print("\n\n")
print("y_test is",y_test)
y_hat_test = grid.predict(X_test)
y_hat_train = grid.predict(X_train)
print("y_hat_test is ", y_hat_test)