目标模型预测mnist数据集准确率 代码
时间: 2023-10-05 10:09:18 浏览: 42
以下是使用Keras框架建立目标模型并对MNIST数据集进行预测的代码示例:
```python
import numpy as np
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
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据预处理
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
# 建立模型
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(10, activation='softmax'))
# 编译模型
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train,
batch_size=128,
epochs=12,
verbose=1,
validation_data=(x_test, y_test))
# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
```
该模型使用了卷积神经网络(Convolutional Neural Network,CNN)来处理图像数据,以改善预测准确率。在训练过程中,使用Adadelta优化器和分类交叉熵损失函数。最终评估模型时,输出了测试数据集的损失和准确率。