理解神經(jīng)網(wǎng)絡(luò)，第三部分神經(jīng)網(wǎng)絡(luò)和人工智能

神經(jīng)網(wǎng)絡(luò)與人工智能transformer

主要差異

1. 結(jié)構(gòu)

傳統(tǒng)的RNN:

def create_rnn():

return tf.keras.Sequential([

tf.keras.layers.LSTM(64, return_sequences=True),

tf.keras.layers.LSTM(32),

tf.keras.layers.Dense(10)

])

簡(jiǎn)單transformer:

def create_transformer():

return tf.keras.Sequential([

tf.keras.layers.MultiHeadAttention(num_heads=8, key_dim=64),

tf.keras.layers.LayerNormalization(),

tf.keras.layers.Dense(64, activation='relu'),

tf.keras.layers.Dense(10)

])

2. 處理能力

神經(jīng)網(wǎng)絡(luò) :順序處理。

變形器 :并行處理帶有注意機(jī)制。

3. 用例

神經(jīng)網(wǎng)絡(luò) 傳統(tǒng)的計(jì)算機(jī)任務(wù),計(jì)算機(jī)視覺(jué)。

變形器 :NLP,大型語(yǔ)言模型。

實(shí)際推行技巧

1. 選型

def choose_model(task_type, input_shape):

if task_type == 'image':

return create_cnn()

elif task_type == 'sequence':

return create_rnn()

else:

return create_basic_nn()

2. 超參數(shù)調(diào)整

from keras_tuner import RandomSearch

def tune_hyperparameters(model_builder, x_train, y_train):

tuner = RandomSearch(

model_builder,

objective='val_accuracy',

max_trials=5

)

tuner.search(x_train, y_train,

epochs=5,

validation_split=0.2)

return tuner.get_best_hyperparameters()[0]

現(xiàn)實(shí)世界案例研究

1.醫(yī)學(xué)影像分析

實(shí)例:COVD-19X射線分類:

def create_medical_cnn():

model = tf.keras.Sequential([

tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),

tf.keras.layers.BatchNormalization(),

tf.keras.layers.MaxPooling2D((2, 2)),

tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),

tf.keras.layers.BatchNormalization(),

tf.keras.layers.MaxPooling2D((2, 2)),

tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),

tf.keras.layers.BatchNormalization(),

tf.keras.layers.GlobalAveragePooling2D(),

tf.keras.layers.Dense(2, activation='softmax')

])

return model

定制數(shù)據(jù)生成器,增加:

def create_medical_data_generator():

return tf.keras.preprocessing.image.ImageDataGenerator(

rotation_range=20,

width_shift_range=0.2,

height_shift_range=0.2,

horizontal_flip=True,

validation_split=0.2,

preprocessing_function=tf.keras.applications.resnet50.preprocess_input

)

2.財(cái)務(wù)時(shí)間序列預(yù)測(cè)

例子:股價(jià)預(yù)測(cè):

def create_financial_lstm():

model = tf.keras.Sequential([

tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(60, 5)),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.LSTM(50, return_sequences=False),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.Dense(1)])

return

財(cái)務(wù)數(shù)據(jù)特征工程:

def prepare_financial_data(df, look_back=60):

features = ['Open', 'High', 'Low', 'Close', 'Volume']

scaler = MinMaxScaler()

scaled_data = scaler.fit_transform(df[features])

X, y = [], []

for i in range(look_back, len(scaled_data)):

X.append(scaled_data[i-look_back:i])

y.append(scaled_data[i, 3]) # Predicting Close price

return np.array(X), np.array(y), scaler

示范部署指南

1.模型優(yōu)化

量化:

def quantize_model(model):

converter = tf.lite.TFLiteConverter.from_keras_model(model)

converter.optimizations = [tf.lite.Optimize.DEFAULT]

converter.target_spec.supported_types = [tf.float16]

tflite_model = converter.convert()

return tflite_model

修剪:

def create_pruned_model(model, training_data):

pruning_params = {

'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(

initial_sparsity=0.30,

final_sparsity=0.80,

begin_step=0,

end_step=end_step)

}

model_pruned = tfmot.sparsity.keras.prune_low_magnitude(

model, pruning_params)

return model_pruned

2.生產(chǎn)部署

提供模式服務(wù)的瓶狀A(yù)PI:

from flask import Flask, request, jsonify

app = Flask(__name__)

model = None

def load_model():

global model

model = tf.keras.models.load_model('path/to/model')

@app.route('/predict', methods=['POST'])

def predict():

data = request.json['data']

processed_data = preprocess_input(data)

prediction = model.predict(processed_data)

return jsonify({'prediction': prediction.tolist()})

文檔文件:

FROM python:3.8-slim

WORKDIR /app

COPY requirements.txt .

RUN pip install -r requirements.txt

COPY . .

CMD ["python", "app.py"]

最新的Transformers創(chuàng)新

1. Vision Transformers (ViT)

def create_vit_model(input_shape, num_classes):

inputs = tf.keras.Input(shape=input_shape)

# Patch embedding

patches = tf.keras.layers.Conv2D(filters=768, kernel_size=16, strides=16)(inputs)

flat_patches = tf.keras.layers.Reshape((-1, 768))(patches)

# Position embedding

positions = tf.range(start=0, limit=flat_patches.shape[1], delta=1)

pos_embedding = tf.keras.layers.Embedding(input_dim=flat_patches.shape[1], output_dim=768)(positions)

x = flat_patches + pos_embedding

# Transformer blocks

for _ in range(12):

x = transformer_block(x)

x = tf.keras.layers.GlobalAveragePooling1D()(x)

outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)

return tf.keras.Model(inputs=inputs, outputs=outputs)

2. MLP-Mixer Architecture

def mlp_block(x, hidden_units, dropout_rate):

x = tf.keras.layers.Dense(hidden_units, activation='gelu')(x)

x = tf.keras.layers.Dense(x.shape[-1])(x)

x = tf.keras.layers.Dropout(dropout_rate)(x)

return x

def mixer_block(x, tokens_mlp_dim, channels_mlp_dim, dropout_rate):

# Token-mixing

y = tf.keras.layers.LayerNormalization()(x)

y = tf.transpose(y, perm=[0, 2, 1])

y = mlp_block(y, tokens_mlp_dim, dropout_rate)

y = tf.transpose(y, perm=[0, 2, 1])

x = x + y

# Channel-mixing

y = tf.keras.layers.LayerNormalization()(x)

y = mlp_block(y, channels_mlp_dim, dropout_rate)

return x + y

優(yōu)化表現(xiàn)技巧

1.記憶管理

大型數(shù)據(jù)集定制數(shù)據(jù)生成器:

class DataGenerator(tf.keras.utils.Sequence):

def __init__(self, x_set, y_set, batch_size):

self.x, self.y = x_set, y_set

self.batch_size = batch_size

def __len__(self):

return int(np.ceil(len(self.x) / self.batch_size))

def __getitem__(self, idx):

batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]

batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]

return np.array(batch_x), np.array(batch_y)

2.培訓(xùn)優(yōu)化

混合精密訓(xùn)練:

def enable_mixed_precision():

policy = tf.keras.mixed_precision.Policy('mixed_float16')

tf.keras.mixed_precision.set_global_policy(policy)

具有梯度積累的定制訓(xùn)練循環(huán):

def enable_mixed_precision():

policy = tf.keras.mixed_precision.Policy('mixed_float16')

tf.keras.mixed_precision.set_global_policy(policy)

Custom training loop with gradient accumulation:

Python

def train_with_gradient_accumulation(model, dataset, accumulation_steps=4):

optimizer = tf.keras.optimizers.Adam()

gradients = [tf.zeros_like(v) for v in model.trainable_variables]

for step, (x_batch, y_batch) in enumerate(dataset):

with tf.GradientTape() as tape:

predictions = model(x_batch, training=True)

loss = compute_loss(y_batch, predictions)

loss = loss / accumulation_steps

grads = tape.gradient(loss, model.trainable_variables)

gradients = [(acc_grad + grad) for acc_grad, grad in zip(gradients, grads)]

if (step + 1) % accumulation_steps == 0:

optimizer.apply_gradients(zip(gradients, model.trainable_variables))

gradients = [tf.zeros_like(v) for v in model.trainable_variables]

追加資源

1. 網(wǎng)上學(xué)習(xí)平臺(tái)

課程:深入學(xué)習(xí)專業(yè)化

快速綜合學(xué)習(xí):實(shí)際深入學(xué)習(xí)

張力流官方教程

2. 書(shū)籍

伊恩·古德費(fèi)羅的《深度學(xué)習(xí)》

邁克爾尼爾森的《神經(jīng)網(wǎng)絡(luò)與深度學(xué)習(xí)》

3. 實(shí)踐平臺(tái)

卡格爾:真實(shí)世界數(shù)據(jù)集和競(jìng)爭(zhēng)

谷歌COLAB:免費(fèi)的GPU接入

張力流游樂(lè)場(chǎng):互動(dòng)可視化

結(jié)論

神經(jīng)網(wǎng)絡(luò)是不斷發(fā)展的強(qiáng)大工具。本指南提供了基礎(chǔ),但該領(lǐng)域正在迅速發(fā)展。堅(jiān)持實(shí)驗(yàn),保持好奇,記住實(shí)踐經(jīng)驗(yàn)是最好的老師。

下面是一些成功的秘訣:

從簡(jiǎn)單的架構(gòu)開(kāi)始。

徹底了解你的數(shù)據(jù)。

仔細(xì)監(jiān)控培訓(xùn)指標(biāo)。

為您的模型使用版本控制。

繼續(xù)進(jìn)行最新的研究.

記住 :最好的學(xué)習(xí)方法是實(shí)現(xiàn)和試驗(yàn)不同的架構(gòu)和數(shù)據(jù)集。

20250114_6785b114e9b0a__理解神經(jīng)網(wǎng)絡(luò)，第三部分神經(jīng)網(wǎng)絡(luò)和人工智能