用Python实现卷积神经网络
时间: 2023-11-05 11:03:42 浏览: 46
以下是使用Python实现卷积神经网络的基本步骤:
1. 导入必要的库
```python
import numpy as np
import tensorflow as tf
```
2. 设置数据集
```python
# 加载数据集
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 对数据集进行预处理
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 = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)
```
3. 构建卷积神经网络模型
```python
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Dropout(0.25))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
```
4. 编译模型
```python
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adadelta(),
metrics=['accuracy'])
```
5. 训练模型
```python
model.fit(x_train, y_train,
batch_size=128,
epochs=12,
verbose=1,
validation_data=(x_test, y_test))
```
6. 评估模型
```python
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
```
7. 预测结果
```python
predictions = model.predict(x_test)
```
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