Keras cnn: training accuracy and validation accuracy are incredibly high - tensorflow

Question

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import numpy as np
import cv2
import os
from imutils import paths
import imutils
def image_to_feature_vector(image, size=(32, 32)):
# resize the image to a fixed size, then flatten the image into
# a list of raw pixel intensities
return np.reshape(cv2.resize(image, size).flatten(), (32, 32, 3))
def extract_color_histogram(image, bins=(8, 8, 8)):
# extract a 3D color histogram from the HSV color space using
# the supplied number of `bins` per channel
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
hist = cv2.calcHist([hsv], [0, 1, 2], None, bins,
[0, 180, 0, 256, 0, 256])
# handle normalizing the histogram if we are using OpenCV 2.4.X
if imutils.is_cv2():
hist = cv2.normalize(hist)
# otherwise, perform "in place" normalization in OpenCV 3
else:
cv2.normalize(hist, hist)
# return the flattened histogram as the feature vector
return hist.flatten()
DATA_SET_PATH = '../training_images'
# grab the list of images that we'll be describing
print("[INFO] describing images...")
imagePaths = list(paths.list_images(DATA_SET_PATH))
# initialize the raw pixel intensities matrix, the features matrix,
# and labels list
rawImages = []
features = []
labels = []
# loop over the input images
for (i, imagePath) in enumerate(imagePaths):
# load the image and extract the class label (assuming that our
# path as the format: /path/to/dataset/{class}.{image_num}.jpg
image = cv2.imread(imagePath)
label = imagePath.split(os.path.sep)[-2].split(".")[1]
# extract raw pixel intensity "features", followed by a color
# histogram to characterize the color distribution of the pixels
# in the image
pixels = image_to_feature_vector(image)
hist = extract_color_histogram(image)
# update the raw images, features, and labels matricies,
# respectively
rawImages.append(pixels)
labels.append(label)
features.append(hist)
# show an update every 1,000 images
if i > 0 and i % 1000 == 0:
print("[INFO] processed {}/{}".format(i, len(imagePaths)))
# show some information on the memory consumed by the raw images
# matrix and features matrix
rawImages = np.array(rawImages)
features = np.array(features)
labels = np.array(labels)
print("[INFO] pixels matrix: {:.2f}MB".format(
rawImages.nbytes / (1024 * 1000.0)))
encoder = LabelEncoder()
encoder.fit(labels)
labels = to_categorical(encoder.transform(labels))
# partition the data into training and testing splits, using 75%
# of the data for training and the remaining 25% for testing
(trainRI, testRI, trainRL, testRL) = train_test_split(
rawImages, labels, test_size=0.15, random_state=42)
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(257, activation='softmax'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest")
model.fit_generator(aug.flow(trainRI, trainRL, batch_size=256), epochs=10, validation_data=(testRI, testRL))
model.evaluate(testRI, testRL)
I'm a beginner in cnn and keras and I'm trying to create a cnn to classify 257 different objects using keras. Above is my code. When I run the code, both training accuracy and validation are incredibly high.
100/102 [============================>.] - ETA: 1s - loss: 0.0265 - acc: 0.9960
101/102 [============================>.] - ETA: 0s - loss: 0.0264 - acc: 0.9960
102/102 [==============================] - 56s 553ms/step - loss: 0.0264 - acc: 0.9960 - val_loss: 0.0250 - val_acc: 0.9961
Could anyone tell me what's wrong with my code? Thanks.