Keras CNN：培训准确性和验证准确性非常高

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)

我是cnn和keras的初学者，我正在尝试创建一个cnn以使用keras对257个不同的对象进行分类。以上是我的代码。当我运行代码时，培训的准确性和验证性都非常高。

100/102 [===========================>。]-ETA：1s-损失：0.0265-acc：0.9960 101/102 [===========================>。]-ETA：0s-损失：0.0264-acc：0.9960 102/102 [==============================]-56s 553ms / step-损耗：0.0264-acc：0.9960-val_loss ：0.0250-val_acc：0.9961

谁能告诉我我的代码出了什么问题？谢谢。