为什么MNIST CNN的预测有误？

Question

我在MNIST数据集上对CNN进行了训练，其训练和验证精度约为0.99。

我遵循了Keras documentation of implementing CNN with MNIST dataset中给出的示例中的确切步骤：

import cv2
import numpy as np
import tensorflow.keras as keras
import math

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                activation='relu',
                input_shape=input_shape))
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(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
            optimizer=keras.optimizers.Adadelta(),
            metrics=['accuracy'])

model.fit(x_train, y_train,
        batch_size=batch_size,
        epochs=epochs,
        verbose=1,
        validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

当我测试以下图像时：

使用以下测试代码：

img = cv2.imread("m9.png", 0)
img = cv2.resize(img, (28,28))
img = img / 255.


prob = model.predict_proba(img.reshape((1,28, 28, 1)))

print(prob)

model.predict_classes(img.reshape((1,28, 28, 1)))

它打印的类是array([1])，表示数字1。我不明白原因。我是否尝试以错误的方式进行预测？

预测数字array([1])的类别完全相同8，如下所示：

好像我在预测期间出错了？我试图了解会发生什么，但不了解。

Answer 1

没有错误，只是您的图像看上去完全不同于MNIST数据集中的图像。此数据集无意训练通用的数字识别算法，仅适用于相似的图像。

在您的情况下，在28x28的图像中数字将非常小，因此预测是随机的。

Answer 2

您要将输入图像的大小调整为28 X 28.，而应首​​先在数字周围裁剪图像，以使其看起来像MNIST中的数据集。否则，在调整大小的图像中，数字将只占很小的一部分，结果将是任意的。