输出使用lstm进行cifar10分类的python代码

以下是使用LSTM进行CIFAR10分类的Python代码：

首先，我们需要导入必要的库：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical

接着，我们需要加载CIFAR10数据集并进行预处理：

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

然后，我们可以定义一个基本的LSTM模型：

model = Sequential()
model.add(LSTM(128, input_shape=(32, 32, 3)))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

最后，我们可以训练模型并输出结果：

model.fit(x_train, y_train,
          batch_size=128,
          epochs=10,
          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])

完整代码如下：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

model = Sequential()
model.add(LSTM(128, input_shape=(32, 32, 3)))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=128,
          epochs=10,
          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])

希望这对你有所帮助！