系列一： 经典深度学习模型
1. 深度卷积神经网络（Deep CNN）
深度卷积神经网络通过增加更多的卷积层和池化层来捕捉更多的图像特征，
从而提高图像分类的准确率。以下是一个使用VGG16模型的示例，该模型在ImageNet挑战中表现优异。
代码语言：javascript复制python复制代码from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers, models
# 加载预训练的VGG16模型
vgg16_base = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
# 冻结卷积基
vgg16_base.trainable = False
# 构建模型
model = models.Sequential()
model.add(vgg16_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
# 编译模型
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
# 数据预处理
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'data/train',
target_size=(150, 150),
batch_size=20,
class_mode='binary')
validation_generator = validation_datagen.flow_from_directory(
'data/validation',
target_size=(150, 150),
batch_size=20,
class_mode='binary')
# 训练模型
history = model.fit(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)2. 长短期记忆网络（LSTM）
LSTM是RNN的一种变体，擅长处理长时间依赖问题。以下是一个改进版的LSTM实现，用于文本生成任务。
代码语言：javascript复制python复制代码import tensorflow as tf
from tensorflow.keras.layers import Embedding, LSTM, Dense
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
# 文本数据
data = """Your text data here"""
# 文本预处理
tokenizer = Tokenizer()
tokenizer.fit_on_texts([data])
total_words = len(tokenizer.word_index) 1
input_sequences = []
for line in data.split('n'):
token_list = tokenizer.texts_to_sequences([line])[0]
for i in range(1, len(token_list)):
n_gram_sequence = token_list[:i 1]
input_sequences.append(n_gram_sequence)
# 填充序列
max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))
# 创建预测变量
xs, labels = input_sequences[:,:-1],input_sequences[:,-1]
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)
# 构建模型
model = tf.keras.Sequential()
model.add(Embedding(total_words, 100, input_length=max_sequence_len-1))
model.add(LSTM(150))
model.add(Dense(total_words, activation='softmax'))
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 训练模型
history = model.fit(xs, ys, epochs=100, verbose=1)
# 文本生成
seed_text = "Your seed text"
next_words = 50
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
predicted = np.argmax(model.predict(token_list, verbose=0), axis=-1)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break
seed_text = " " output_word
print(seed_text)3. 注意力机制和Transformer模型
慕课AI人工智能算法工程师Transformer模型引入了注意力机制，在自然语言处理任务中表现出色。以下是一个简单的Transformer实现，用于机器翻译任务。
代码语言：javascript复制python复制代码import tensorflow as tf
from tensorflow.keras.layers import Dense, LayerNormalization, Embedding, MultiHeadAttention, Dropout
from tensorflow.keras.models import Model
import numpy as np
class TransformerBlock(tf.keras.layers.Layer):
def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
super(TransformerBlock, self).__init__()
self.att = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
self.ffn = tf.keras.Sequential(
[Dense(ff_dim, activation="relu"), Dense(embed_dim),]
)
self.layernorm1 = LayerNormalization(epsilon=1e-6)
self.layernorm2 = LayerNormalization(epsilon=1e-6)
self.dropout1 = Dropout(rate)
self.dropout2 = Dropout(rate)
def call(self, inputs, training):
attn_output = self.att(inputs, inputs)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(inputs attn_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layernorm2(out1 ffn_output)
# 创建Transformer模型
class TokenAndPositionEmbedding(tf.keras.layers.Layer):
def __init__(self, maxlen, vocab_size, embed_dim):
super(TokenAndPositionEmbedding, self).__init__()
self.token_emb = Embedding(input_dim=vocab_size, output_dim=embed_dim)
self.pos_emb = Embedding(input_dim=maxlen, output_dim=embed_dim)
def call(self, x):
maxlen = tf.shape(x)[-1]
positions = tf.range(start=0, limit=maxlen, delta=1)
positions = self.pos_emb(positions)
x = self.token_emb(x)
return x positions
vocab_size = 20000 # 词汇表大小
maxlen = 200 # 序列最大长度
embed_dim = 32 # 嵌入维度
num_heads = 2 # 注意力头数量
ff_dim = 32 # 前馈网络维度
inputs = tf.keras.Input(shape=(maxlen,))
embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
x = embedding_layer(inputs)
transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim)
x = transformer_block(x)
x = tf.keras.layers.GlobalAveragePooling1D()(x)
x = tf.keras.layers.Dropout(0.1)(x)
x = tf.keras.layers.Dense(20, activation="relu")(x)
x = tf.keras.layers.Dropout(0.1)(x)
outputs = tf.keras.layers.Dense(2, activation="softmax")(x)
model = Model(inputs=inputs, outputs=outputs)
# 编译和训练模型
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])4. 自编码器（Autoencoder）
自编码器用于无监督学习，尤其是在数据降维和特征提取方面。以下是一个简单的自编码器实现示例，用于图像去噪。
代码语言：javascript复制python复制代码import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np
# 构建自编码器模型
input_img = tf.keras.Input(shape=(28, 28, 1))
# 编码器
x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)
# 解码器
x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = models.Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
# 加载数据并添加噪声
(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
noise_factor = 0.5
x_train_noisy = x_train noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
# 训练自编码器
autoencoder.fit(x_train_noisy, x_train,
epochs=50,
batch_size=256,
shuffle=True,
validation_data=(x_test_noisy, x_test))
# 预测
decoded_imgs = autoencoder.predict(x_test_noisy)
