TensorFlow 入门 - AI & ML

01. TensorFlow 简介

TensorFlow 是 Google 开发的开源机器学习框架，广泛应用于深度学习。 TensorFlow 的特点： - 张量（Tensor）计算 - 自动微分（GradientTape） - GPU/TPU 加速 - 生产部署工具（TensorFlow Serving、Lite） - 丰富的预训练模型

python

import tensorflow as tf

# 张量基础
tensor = tf.constant([[1, 2], [3, 4]])
print("形状:", tensor.shape)  # (2, 2)
print("数据类型:", tensor.dtype)  # int32

# 张量运算
a = tf.constant([1, 2, 3])
b = tf.constant([4, 5, 6])
c = a + b  # [5, 7, 9]
d = tf.multiply(a, b)  # [4, 10, 18]

# 自动微分
x = tf.Variable(3.0)
with tf.GradientTape() as tape:
y = x ** 2 + 2 * x + 1
dy_dx = tape.gradient(y, x)
print("dy/dx =", dy_dx.numpy())  # 8.0

02. Keras 模型构建

Keras 是 TensorFlow 的高级 API，提供简洁的模型构建方式。两种模型构建方式： - Sequential：层栈式模型 - Functional API：支持多输入输出、共享层 - Model Subclassing：完全自定义

python

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Sequential 模型
model = keras.Sequential([
layers.Dense(64, activation='relu', input_shape=(784,)),
layers.Dropout(0.2),
layers.Dense(32, activation='relu'),
layers.Dense(10, activation='softmax')
])

model.summary()

# Functional API
inputs = keras.Input(shape=(784,))
x = layers.Dense(64, activation='relu')(inputs)
x = layers.Dropout(0.2)(x)
x = layers.Dense(32, activation='relu')(x)
outputs = layers.Dense(10, activation='softmax')(x)

model = keras.Model(inputs=inputs, outputs=outputs)

# 编译模型
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)

03. 图像分类实战

使用 TensorFlow 构建图像分类模型。工作流程： - 数据加载和预处理 - 模型构建 - 训练和验证 - 预测和评估

python

import tensorflow as tf
from tensorflow.keras import datasets, layers, models

# 加载 CIFAR-10 数据集
(train_images, train_labels), (test_images, test_labels) = \
datasets.cifar10.load_data()

# 归一化
train_images, test_images = train_images / 255.0, test_images / 255.0

# 构建 CNN 模型
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])

# 编译
model.compile(optimizer='adam',
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])

# 训练
history = model.fit(train_images, train_labels, epochs=10,
                validation_data=(test_images, test_labels))

# 评估
test_loss, test_acc = model.evaluate(test_images, test_labels)
print("测试准确率:", round(test_acc, 4))

知识测验

第 1/5 题正确 0

TensorFlow 中 GradientTape 的作用是什么？

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