python深度学习代码
时间: 2023-08-24 16:05:00 浏览: 82
以下是一个使用Python深度学习的简单示例代码:
首先,您需要安装所需的库。您可以使用以下命令安装TensorFlow和Keras:
```
pip install tensorflow
pip install keras
```
接下来,让我们创建一个简单的神经网络来识别手写数字。我们将使用MNIST数据集,该数据集包含许多手写数字的图像,以及它们所代表的数字。
```
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
from keras import backend as K
# 设置输入图像的维度
img_rows, img_cols = 28, 28
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 根据图像格式重新排列输入数据
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# 将输入数据转换为浮点数并归一化
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# 将标签转换为 one-hot 向量
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
# 构建模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
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])
```
这个代码将创建一个卷积神经网络,并使用MNIST数据集进行训练和测试。它将输出模型的准确性。