Python TensorFlow介绍与实战

pip install tensorflow -i https://pypi.tuna.tsinghua.edu.cn/simple

import tensorflow as tf
print(tf.__version__)

pip install tensorflow-gpu -i https://pypi.tuna.tsinghua.edu.cn/simple

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

# 创建张量
tensor1 = tf.constant([[1, 2], [3, 4]])
tensor2 = tf.constant([[5, 6], [7, 8]])
# 张量相加
add = tf.add(tensor1, tensor2)
# 张量乘法
multiply = tf.matmul(tensor1, tensor2)
print("Addition:", add)
print("Multiplication:", multiply)

# 创建计算图
a = tf.constant(5)
b = tf.constant(6)
c = tf.constant(7)
# 复杂操作
d = tf.add(a, b)
e = tf.multiply(d, c)
# 执行计算图
with tf.Session() as sess:
    result = sess.run(e)
    print(result)

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
# 评估模型
model.evaluate(x_test, y_test)

# 保存模型
model.save('my_model.h5')
# 加载模型
loaded_model = tf.keras.models.load_model('my_model.h5')

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import numpy as np
# 加载MNIST数据集
(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
# 标准化图像数据
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
# 将标签转换为one-hot编码
train_labels = tf.keras.utils.to_categorical(train_labels)
test_labels = tf.keras.utils.to_categorical(test_labels)
# 构建卷积神经网络模型
model = models.Sequential()
# 第一层卷积，使用32个3x3的卷积核，激活函数为ReLU
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
# 池化层，使用2x2的池化窗口
model.add(layers.MaxPooling2D((2, 2)))
# 第二层卷积，使用64个3x3的卷积核，激活函数为ReLU
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
# 第二个池化层，使用2x2的池化窗口
model.add(layers.MaxPooling2D((2, 2)))
# 第三层卷积，使用64个3x3的卷积核，激活函数为ReLU
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
# 展平特征图，为全连接层做准备
model.add(layers.Flatten())
# 全连接层，使用64个神经元，激活函数为ReLU
model.add(layers.Dense(64, activation='relu'))
# 输出层，使用10个神经元，对应10个类别，激活函数为softmax
model.add(layers.Dense(10, activation='softmax'))
# 编译模型
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
# 训练模型
model.fit(train_images, train_labels, epochs=5, batch_size=64)
# 评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f'测试准确率: {test_acc:.4f}')
# 使用模型进行预测
predictions = model.predict(test_images)
# 获取预测结果
predicted_labels = np.argmax(predictions, axis=1)
true_labels = np.argmax(test_labels, axis=1)
# 打印前10个预测结果和真实标签
for i in range(10):
    print(f'预测结果: {predicted_labels[i]}, 真实标签: {true_labels[i]}')