深度学习python的代码实例
时间: 2024-05-19 22:09:49 浏览: 18
深度学习是机器学习的一个分支,它使用神经网络模型来解决各种问题,包括图像识别、语音识别、自然语言处理等。Python是深度学习的主流编程语言之一,有很多成熟的深度学习框架,如TensorFlow、Keras、PyTorch等。
下面是一个使用Keras框架实现图像分类的代码示例:
```python
# 导入所需的库
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
# 加载数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 数据预处理
X_train = X_train.reshape(X_train.shape, 28, 28, 1)
X_test = X_test.reshape(X_test.shape, 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)
# 构建模型
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='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 训练模型
model.fit(X_train, y_train,
batch_size=32,
epochs=10,
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)
print('Test accuracy:', score)
```
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