使用 Python 深度学习库进行开发与应用

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

# 构建一个简单的卷积神经网络（CNN）
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

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

import torch
import torch.nn as nn
import torch.optim as optim

# 定义一个简单的全连接网络
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(28 * 28, 128),
            nn.ReLU(),
            nn.Linear(128, 10)
        )

    def forward(self, x):
        return self.fc(x)

model = SimpleNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 示例训练过程
for epoch in range(10):
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data.view(-1, 28*28))
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

from keras.models import Sequential
from keras.layers import Dense, Flatten

# 构建一个简单的前馈神经网络
model = Sequential([
    Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

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

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

# 加载数据
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape((-1, 28, 28, 1)) / 255.0
x_test = x_test.reshape((-1, 28, 28, 1)) / 255.0
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# 模型定义
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# 编译和训练
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.2)

from torchtext.legacy import data, datasets

# 数据预处理
TEXT = data.Field(tokenize='spacy', tokenizer_language='en_core_web_sm')
LABEL = data.LabelField(dtype=torch.float)
train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)
TEXT.build_vocab(train_data, max_size=25000)
LABEL.build_vocab(train_data)

# 模型定义
class SentimentRNN(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_dim, output_dim):
        super(SentimentRNN, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.rnn = nn.LSTM(embed_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        embedded = self.embedding(x)
        _, (hidden, _) = self.rnn(embedded)
        return self.fc(hidden.squeeze(0))

# 训练代码略