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。

Keras CNN：培训准确性和验证准确性非常高

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