TensorFlow基础教程

TensorFlow简介

TensorFlow是Google开发的开源深度学习框架，支持静态图和动态图，广泛应用于工业级部署。

张量操作

import tensorflow as tf

# 创建张量
a = tf.zeros((3, 4))
b = tf.ones((3, 4))
c = tf.random.normal((3, 4))
d = tf.constant([1, 2, 3], dtype=tf.float32)

print(f"零张量: {a.shape}")
print(f"随机张量: {c.shape}")

张量运算

# 基本运算
x = tf.random.normal((2, 3))
y = tf.random.normal((2, 3))

add = tf.add(x, y)
mul = tf.multiply(x, y)
matmul = tf.matmul(x, tf.transpose(y))

print(f"矩阵乘法: {matmul.shape}")

# 形状操作
reshape = tf.reshape(tf.random.normal((2, 6)), (2, 3, 2))
print(f"重塑后: {reshape.shape}")

# 聚合操作
x = tf.random.normal((3, 4))
print(f"求和: {tf.reduce_sum(x, axis=0).shape}")
print(f"均值: {tf.reduce_mean(x, axis=1).shape}")

自动微分

# GradientTape计算梯度
x = tf.Variable([2.0, 3.0])

with tf.GradientTape() as tape:
    y = x ** 2 + 2 * x + 1

grads = tape.gradient(y, x)
print(f"梯度 dy/dx = 2x+2: {grads.numpy()}")

Keras Sequential模型

from tensorflow import keras
from tensorflow.keras import layers

# Sequential API
model = keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(10,)),
    layers.Dropout(0.2),
    layers.Dense(32, activation='relu'),
    layers.Dense(2, activation='softmax')
])

model.summary()

Keras Functional API

# Functional API（支持多输入多输出）
inputs = keras.Input(shape=(10,))
x = layers.Dense(64, activation='relu')(inputs)
x = layers.Dense(32, activation='relu')(x)
outputs = layers.Dense(2, activation='softmax')(x)

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

完整训练流程

import numpy as np

# 1. 准备数据
X_train = np.random.randn(500, 10).astype(np.float32)
y_train = np.random.randint(0, 2, (500,))

# 2. 创建模型
model = keras.Sequential([
    layers.Dense(32, activation='relu', input_shape=(10,)),
    layers.Dense(16, activation='relu'),
    layers.Dense(2, activation='softmax')
])

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

# 4. 训练模型
history = model.fit(
    X_train, y_train,
    epochs=10,
    batch_size=32,
    validation_split=0.2,
    verbose=1
)

# 5. 评估模型
loss, accuracy = model.evaluate(X_train, y_train)
print(f"训练准确率: {accuracy:.4f}")

回调函数

# 使用回调函数
callbacks = [
    keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True),
    keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=2),
    keras.callbacks.ModelCheckpoint('best_model.keras', save_best_only=True)
]

model.fit(X_train, y_train, epochs=20, callbacks=callbacks, validation_split=0.2)

保存与加载

# 保存完整模型
model.save('my_model.keras')

# 加载模型
loaded_model = keras.models.load_model('my_model.keras')

总结

TensorFlow配合Keras API提供了从研究到部署的完整解决方案。