Part of the answer refers to GPT, GPT_Pro better solve the problem
CNN convolutional neural network can be used for image recognition. CNN is a special neural network structure, which can automatically extract the features of the image to identify the numbers and shapes in the image. In addition, CNN can identify numbers and graphics separately and output results, which can greatly improve the accuracy.
How to use CNN for image recognition?
- First, we need to convert the original image into input samples that can be used by CNN, which generally include standardization, normalization, cropping, cropping, rotation and expansion, etc.
- Then, we need to train CNN to recognize numbers and shapes in the image. There are many ways to train CNN, and we can choose the best method according to the specific situation.
- Finally, we can predict the test image. In the prediction, we need to match the test image with the trained CNN model, and output serial number + number + figure according to the predicted number and shape, such as '1+2+ triangle '.
Here is an example of Python code that uses CNN for number and graph recognition:
# 导入所需要的库
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Dense, Flatten
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
# 定义模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# 编译模型
model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
# 数据集准备
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2)
test_datagen = ImageDataGenerator(rescale=1./255) #测试集不能进行随机处理
train_set = train_datagen.flow_from_directory('dataset/training_set', target_size=(64, 64), batch_size=32) #注意此处为文件夹目录
test_set = test_datagen.flow_from_directory('dataset/test_set', target_size=(64, 64), batch_size=32) #注意此处为文件夹目录
# 训练模型
model.fit_generator(train_set, epochs=25, steps_per_epoch=8000/32) #注意此处为样本数/batch size
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