Neural network is not predicting correctly - machine-learning

Question

I am making a neural network to predict different kind of vegetables. It keeps predicting the wrong vegetable every time even though my val_accuracy is around 90% .
type import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import RMSprop
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropouthere
train = ImageDataGenerator(rescale=1/255)
validation = ImageDataGenerator(rescale=1/255)
train_data_set = train.flow_from_directory(r"C:\Users\roshn\Desktop\Vegetable Images\train",target_size=(200,200),batch_size =15,class_mode='categorical')
Found 15000 images belonging to 15 classes.
val_data_set = train.flow_from_directory(r"C:\Users\roshn\Desktop\Vegetable Images\validation",target_size=(200,200),batch_size =15,class_mode='categorical')
Found 3000 images belonging to 15 classes.
train_data_set.class_indices
{'Bean': 0,
'Bitter_Gourd': 1,
'Bottle_Gourd': 2,
'Brinjal': 3,
'Broccoli': 4,
'Cabbage': 5,
'Capsicum': 6,
'Carrot': 7,
'Cauliflower': 8,
'Cucumber': 9,
'Papaya': 10,
'Potato': 11,
'Pumpkin': 12,
'Radish': 13,
'Tomato': 14}
model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(16,(3,3),activation ='relu',input_shape =(200,200,3)),
tf.keras.layers.MaxPool2D(2,2),
##
tf.keras.layers.Conv2D(32,(3,3),activation ='relu'),
tf.keras.layers.MaxPool2D(2,2),
##
tf.keras.layers.Flatten(),
##
tf.keras.layers.Dense(1000,activation ='relu'),
##
tf.keras.layers.Dropout(0.5),
##
tf.keras.layers.Dense(500,activation='relu'),
##
tf.keras.layers.Dropout(0.5),
##
tf.keras.layers.Dense(250,activation='relu'),
##
tf.keras.layers.Dense(15,activation='softmax')])
model.compile(loss='categorical_crossentropy',optimizer ='adam',metrics=['accuracy'])
es = EarlyStopping(monitor='val_accuracy',patience=2,verbose=1,mode='max')
mc = ModelCheckpoint('checkpoint/',monitor='val_accuracy',mode='max',save_best_only=True,verbose=1)
lr = ReduceLROnPlateau(monitor='val_accuracy',factor=0.1,min_lr=0.001,patience=15,mode='max',verbose=1)
model_fit=model.fit(train_data_set,
epochs=10,
batch_size=64,
validation_data=val_data_set,
callbacks=[es,mc,lr],validation_split =0.2)
model.summary()
Model: "sequential_23"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_63 (Conv2D) (None, 198, 198, 16) 448
max_pooling2d_63 (MaxPoolin (None, 99, 99, 16) 0
g2D)
conv2d_64 (Conv2D) (None, 97, 97, 32) 4640
max_pooling2d_64 (MaxPoolin (None, 48, 48, 32) 0
g2D)
flatten_22 (Flatten) (None, 73728) 0
dense_54 (Dense) (None, 1000) 73729000
dropout_8 (Dropout) (None, 1000) 0
dense_55 (Dense) (None, 500) 500500
dropout_9 (Dropout) (None, 500) 0
dense_56 (Dense) (None, 250) 125250
dense_57 (Dense) (None, 15) 3765
=================================================================
Total params: 74,363,603
Trainable params: 74,363,603
Non-trainable params: 0
___________________________________________________
This is the code I used for prediction.
dir_path =r"C:\Users\roshn\Desktop\Vegetable Images\train\Bitter_Gourd\0001.jpg"
img =image.load_img(dir_path,target_size =(200,200,3))
plt.imshow(img)
plt.show()
x= image.img_to_array(img)
x=np.expand_dims(x,axis =0)
images =np.vstack([x])
vl = model.predict(images)
t = train_data_set.class_indices
classes = list(t.keys())
values = t.values()
list_index=list(values)
x=vl
for i in range(15):
for j in range(15):
if x[0][list_index[i]] > x[0][list_index[j]]:
temp=list_index[i]
list_index[i]=list_index[j]
list_index[j]=temp
print(list_index)
for i in range (5):
print(classes[list_index[i]],':',round(vl[0][list_index[i]])*100,"%")