Error Convolutional Neural Network in Python

I am trying to use inception model to train my AI and i am getting the error

ValueError: Error when checking target: expected concatenate_3 to have 4 dimensions, but got array with shape (1862, 12)

Would you please help me about this? my code:

# example of creating a CNN with an inception module

from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers.merge import concatenate
from keras.utils import plot_model
 
    
model= Sequential()

# function for creating a naive inception block
def naive_inception_module(layer_in, f1, f2, f3):
    # 1x1 conv
    conv1 = Conv2D(f1, (1,1), padding='same', activation='relu')(layer_in)
    # 3x3 conv
    conv3 = Conv2D(f2, (3,3), padding='same', activation='relu')(layer_in)
    # 5x5 conv
    conv5 = Conv2D(f3, (5,5), padding='same', activation='relu')(layer_in)
    # 3x3 max pooling
    pool = MaxPooling2D((3,3), strides=(1,1), padding='same')(layer_in)
    # concatenate filters, assumes filters/channels last
    layer_out = concatenate([conv1, conv3, conv5, pool], axis=-1)
    return layer_out
 
# define model input
visible = Input(shape=(256, 256, 3))
# add inception module
layer = naive_inception_module(visible, 64, 128, 32)
# create model
model = Model(inputs=visible, outputs=layer)


# summarize model
model.summary()
# plot model architecture


optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer = optimizer, loss = "categorical_crossentropy", metrics = ["accuracy"])

checkpointer = ModelCheckpoint(filepath='saved_models/cnn_model_wo_aug_af.hdf5', 
                               verbose=1, save_best_only=True)
    
history = model.fit(train_tensors, train_targets, epochs = 20, validation_data = `(val_train,val_targets),callbacks=[checkpointer],batch_size = 64)`

[![Model][1]][1]

ValueError: Error when checking target: expected concatenate_3 to have 4 dimensions, but got array with shape (1862, 12)

Processing data for train_target:

import numpy as np
import pandas as pd
from glob import glob
import cv2
import matplotlib.pyplot as plt
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from tqdm import tqdm
import time
%matplotlib inline

from sklearn.datasets import load_files
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report
import seaborn as sns

from keras.utils import np_utils
from keras.preprocessing import image
from keras.models import Sequential, model_from_json
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dropout, Flatten, Dense ,Input

from keras.optimizers import Adam ,RMSprop

def load_dataset(path):
    data=load_files(path)
    plant_files=np.array(data['filenames'])
    plant_targets=np_utils.to_categorical(np.array(data['target']),12)
    return plant_files,plant_targets

print('Loading Train Files and Targets')
train_files, train_targets = load_dataset(r'C:\Users\kaan\Documents\Plant_Seedlings_Classification-master\Plant_Seedlings_Classification-master\data')
print('Loading Complete!')
print('There are %d training plant images.' % len(train_files))

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(256, 256))
    # convert PIL.Image.Image type to 3D tensor with shape (256, 256, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 256, 256, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255

# do not change seed to reproduce my results
seed = 31
np.random.seed(seed)



# Split the train and the validation set
train_tensors, val_train, train_targets, val_targets = train_test_split(train_tensors,
                                              train_targets, 
                                              test_size=0.15,
                                              random_state=seed
                                             )


print(train_tensors.shape)
print(val_train.shape)
print(train_targets.shape)
print(val_targets.shape)