我需要 卷积神经网络图像识别 代码

以下是一个简单的卷积神经网络图像识别的代码示例，使用Python和Keras库：

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.datasets import mnist
from keras.utils import to_categorical

# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Normalize pixel values to be between 0 and 1
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# Convert class labels to one-hot encoded vectors
num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# Define the model architecture
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
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'))

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

# Train the model
batch_size = 128
epochs = 10
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

# Evaluate the model on the test set
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

该代码使用MNIST数据集进行训练和测试，并使用两个卷积层，池化层，Dropout层和全连接层构建了一个简单的卷积神经网络模型。在编译模型时，使用Adadelta优化器和分类交叉熵损失函数。在训练模型时，使用128批次和10个时期。最后，在测试集上评估模型的准确性。