Question

QTextCursor curs=ui->textEdit->textCursor();
QTextCharFormat fmt=curs.charFormat();
fmt.setBackground(Qt::yellow);
curs.setCharFormat(fmt);
ui->textEdit->setTextCursor(curs);

运行此代码后，我们得到一个'.h5'模型，然后为predict this image 我添加了以下代码：

from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras.preprocessing import image
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)).astype('float32')
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1)).astype('float32')
X_train = X_train / 255
X_test = X_test / 255
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
def larger_model():
    model = Sequential()
    model.add(Conv2D(30, (5, 5), input_shape=(28, 28, 1), activation='relu'))
    model.add(MaxPooling2D())
    model.add(Conv2D(15, (3, 3), activation='relu'))
    model.add(MaxPooling2D())
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model
model = larger_model()
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Large CNN Error: %.2f%%" % (100-scores[1]*100))

model.save('good_model.h5')
print("Model saved")

但是我总是得到与4相同的答案。上面，我尝试用cv加载图像并将其转换为灰色，然后重塑为输入的大小。它正确地接受了输入，但是不管我给它什么图像作为输入，答案始终是相同的

Answer 1

在预测之前，您需要反转图像。反转图像后，它将正确预测。给定的示例预测为“ 2”，但我检查了诸如“ 7”之类的其他图像，这是正确的预测。

file = cv2.bitwise_not(file)

除上述内容外，我进行了一项更改。我从Tensorflow 2.x导入了模块。请检查完整的代码here。

预测函数每次都给出相同的输出

1 个答案: