def __init__(self, model, target_layers): self.model = model print(self.model.features) self.feature_extractor = FeatureExtractor(self.model.features, target_layers) def get_gradients(self): return self.feature_extractor.gradients gradients = self.extractor.get_gradients() 中gradients为空列表，怎么修改

根据代码片段来看，需要检查以下几点：

1. FeatureExtractor类中是否正确地记录了梯度信息，即是否在forward函数中正确地调用了register_hook函数。

2. 检查是否正确地调用了目标层（target_layers）的forward函数，即是否正确地提取了目标层的特征。

3. 检查是否正确地调用了反向传播函数backward，即是否正确地计算了梯度。

如果以上几点都没有问题，还是无法获取梯度值，那么可以尝试检查一下是否使用了正确的损失函数。有些损失函数可能不支持自动求导，需要手动计算梯度。

相关问题

有没有transformer的例子

是的，以下是一个使用Transformer模型进行文本分类的示例：

import tensorflow as tf
import tensorflow_datasets as tfds

# 加载IMDB数据集
(train_data, test_data), info = tfds.load('imdb_reviews/subwords8k', 
                                         split=(tfds.Split.TRAIN, tfds.Split.TEST), 
                                         with_info=True, as_supervised=True)
encoder = info.features['text'].encoder

BUFFER_SIZE = 10000
BATCH_SIZE = 64

# 训练数据和测试数据预处理
padded_shapes = ([None], ())
train_batches = (train_data.shuffle(BUFFER_SIZE).padded_batch(BATCH_SIZE, padded_shapes=padded_shapes))
test_batches = (test_data.padded_batch(BATCH_SIZE, padded_shapes=padded_shapes))

# Transformer模型定义
class TransformerModel(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, dropout_rate=0.1):
        super(TransformerModel, self).__init__()

        self.encoder = tf.keras.layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(input_vocab_size, d_model)

        self.transformer_blocks = [TransformerBlock(d_model, num_heads, dff, dropout_rate) for _ in range(num_layers)]
        self.dropout = tf.keras.layers.Dropout(dropout_rate)
        self.final_layer = tf.keras.layers.Dense(target_vocab_size)

    def call(self, inputs, training):
        input_seq, input_mask = inputs
        input_emb = self.encoder(input_seq)
        input_emb *= tf.math.sqrt(tf.cast(self.encoder.embedding_dim, tf.float32))
        input_emb += self.pos_encoding[:input_emb.shape[1], :]

        x = self.dropout(input_emb, training=training)

        for i in range(len(self.transformer_blocks)):
            x = self.transformer_blocks[i](x, input_mask, training)

        x = tf.reduce_mean(x, axis=1)
        x = self.final_layer(x)

        return x

# Transformer块定义
class TransformerBlock(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, dropout_rate=0.1):
        super(TransformerBlock, self).__init__()

        self.multi_head_attention = MultiHeadAttention(d_model, num_heads)
        self.feed_forward_network = point_wise_feed_forward_network(d_model, dff)

        self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)

        self.dropout1 = tf.keras.layers.Dropout(dropout_rate)
        self.dropout2 = tf.keras.layers.Dropout(dropout_rate)

    def call(self, x, mask, training):
        attn_output, _ = self.multi_head_attention(x, x, x, mask)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layer_norm1(x + attn_output)

        ffn_output = self.feed_forward_network(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layer_norm2(out1 + ffn_output)

        return out2

# 多头注意力机制定义
class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model

        assert d_model % self.num_heads == 0

        self.depth = d_model // self.num_heads

        self.wq = tf.keras.layers.Dense(d_model)
        self.wk = tf.keras.layers.Dense(d_model)
        self.wv = tf.keras.layers.Dense(d_model)

        self.dense = tf.keras.layers.Dense(d_model)

    def split_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, q, k, v, mask):
        batch_size = tf.shape(q)[0]

        q = self.wq(q)
        k = self.wk(k)
        v = self.wv(v)

        q = self.split_heads(q, batch_size)
        k = self.split_heads(k, batch_size)
        v = self.split_heads(v, batch_size)

        scaled_attention, attention_weights = scaled_dot_product_attention(q, k, v, mask)
        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))

        output = self.dense(concat_attention)

        return output, attention_weights

# 点式前馈网络定义
def point_wise_feed_forward_network(d_model, dff):
    return tf.keras.Sequential([
        tf.keras.layers.Dense(dff, activation='relu'),
        tf.keras.layers.Dense(d_model)
    ])

# 编码器位置编码定义
def get_angles(pos, i, d_model):
    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
    return pos * angle_rates

def positional_encoding(position, d_model):
    angle_rads = get_angles(np.arange(position)[:, np.newaxis], np.arange(d_model)[np.newaxis, :], d_model)
    angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
    angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])
    pos_encoding = angle_rads[np.newaxis, ...]
    return tf.cast(pos_encoding, dtype=tf.float32)

# 损失函数定义
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def loss_function(real, pred):
    mask = tf.math.logical_not(tf.math.equal(real, 0))
    loss_ = loss_object(real, pred)

    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask

    return tf.reduce_mean(loss_)

# 评估指标定义
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy')

# 模型训练
EPOCHS = 10

num_layers = 4
d_model = 128
num_heads = 8
dff = 512
dropout_rate = 0.1

input_vocab_size = encoder.vocab_size
target_vocab_size = 2

transformer = TransformerModel(num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, dropout_rate)

optimizer = tf.keras.optimizers.Adam()

for epoch in range(EPOCHS):
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for (batch, (input_seq, target)) in enumerate(train_batches):
        input_mask = tf.math.logical_not(tf.math.equal(input_seq, 0))
        target = tf.expand_dims(target, axis=-1)

        with tf.GradientTape() as tape:
            predictions = transformer((input_seq, input_mask), True)
            loss = loss_function(target, predictions)

        gradients = tape.gradient(loss, transformer.trainable_variables)
        optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))

        train_loss(loss)
        train_accuracy(target, predictions)

        if batch % 100 == 0:
            print('Epoch {} Batch {} Train Loss {:.4f} Train Accuracy {:.4f}'.format(
                epoch + 1, batch, train_loss.result(), train_accuracy.result()))

    for (batch, (input_seq, target)) in enumerate(test_batches):
        input_mask = tf.math.logical_not(tf.math.equal(input_seq, 0))
        target = tf.expand_dims(target, axis=-1)

        predictions = transformer((input_seq, input_mask), False)
        loss = loss_function(target, predictions)

        test_loss(loss)
        test_accuracy(target, predictions)

    print('Epoch {} Test Loss {:.4f} Test Accuracy {:.4f}'.format(
        epoch + 1, test_loss.result(), test_accuracy.result()))

这个示例使用Transformer模型来对IMDB电影评论进行情感分析。它使用TensorFlow Datasets中的IMDB数据集，将每个评论进行编码并将其输入到Transformer模型中，以预测评论的情感（正面或负面）。

图像风格迁移python代码

以下是一个基于PyTorch实现的图像风格迁移的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image
import matplotlib.pyplot as plt

# 定义图像处理函数
loader = transforms.Compose([
    transforms.Resize((512, 512)),  # 调整图像大小
    transforms.ToTensor()  # 转换为张量
])

unloader = transforms.ToPILImage()  # 将张量转换为图像


# 定义图像加载函数
def image_loader(image_name):
    image = Image.open(image_name)
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)


# 定义模型
class ContentLoss(nn.Module):
    def __init__(self, target):
        super(ContentLoss, self).__init__()
        self.target = target.detach()

    def forward(self, input):
        self.loss = nn.functional.mse_loss(input, self.target)
        return input


class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super(StyleLoss, self).__init__()
        self.target = gram_matrix(target_feature).detach()

    def forward(self, input):
        G = gram_matrix(input)
        self.loss = nn.functional.mse_loss(G, self.target)
        return input


def gram_matrix(input):
    a, b, c, d = input.size()
    features = input.view(a * b, c * d)
    G = torch.mm(features, features.t())
    return G.div(a * b * c * d)


class Normalization(nn.Module):
    def __init__(self, mean, std):
        super(Normalization, self).__init__()
        self.mean = torch.tensor(mean).view(-1, 1, 1)
        self.std = torch.tensor(std).view(-1, 1, 1)

    def forward(self, img):
        return (img - self.mean) / self.std


# 定义模型
class StyleTransferModel(nn.Module):
    def __init__(self, content_img, style_img, cnn=models.vgg19(pretrained=True).features.to(device).eval(),
                 content_layers=['conv_4'], style_layers=['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']):
        super(StyleTransferModel, self).__init__()
        self.content_layers = content_layers
        self.style_layers = style_layers
        self.content_losses = []
        self.style_losses = []
        self.model = nn.Sequential(Normalization([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))
        i = 0
        for layer in cnn.children():
            if isinstance(layer, nn.Conv2d):
                i += 1
                name = 'conv_{}'.format(i)
            elif isinstance(layer, nn.ReLU):
                name = 'relu_{}'.format(i)
                layer = nn.ReLU(inplace=False)
            elif isinstance(layer, nn.MaxPool2d):
                name = 'pool_{}'.format(i)
            elif isinstance(layer, nn.BatchNorm2d):
                name = 'bn_{}'.format(i)
            else:
                raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
            self.model.add_module(name, layer)

            if name in content_layers:
                target = self.model(content_img).detach()
                content_loss = ContentLoss(target)
                self.model.add_module("content_loss_{}".format(i), content_loss)
                self.content_losses.append(content_loss)

            if name in style_layers:
                target_feature = self.model(style_img).detach()
                style_loss = StyleLoss(target_feature)
                self.model.add_module("style_loss_{}".format(i), style_loss)
                self.style_losses.append(style_loss)

        for i in range(len(self.model) - 1, -1, -1):
            if isinstance(self.model[i], ContentLoss) or isinstance(self.model[i], StyleLoss):
                break
        self.model = self.model[:(i + 1)]

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


# 定义图像风格迁移函数
def style_transfer(content_img_path, style_img_path, num_steps=300,
                   style_weight=100000, content_weight=1):
    content_img = image_loader(content_img_path)
    style_img = image_loader(style_img_path)
    input_img = content_img.clone()
    optimizer = optim.LBFGS([input_img.requires_grad_()])

    model = StyleTransferModel(content_img, style_img).to(device)

    print('Start style transfer...')
    run = [0]
    while run[0] <= num_steps:

        def closure():
            input_img.data.clamp_(0, 1)
            optimizer.zero_grad()
            model(input_img)
            style_score = 0
            content_score = 0

            for sl in model.style_losses:
                style_score += sl.loss
            for cl in model.content_losses:
                content_score += cl.loss

            style_score *= style_weight
            content_score *= content_weight

            loss = style_score + content_score
            loss.backward()

            run[0] += 1
            if run[0] % 50 == 0:
                print("run {}:".format(run))
                print('Style Loss : {:4f} Content Loss: {:4f}'.format(
                    style_score.item(), content_score.item()))
                print()

            return style_score + content_score

        optimizer.step(closure)

    input_img.data.clamp_(0, 1)
    return input_img


# 定义图像显示函数
def imshow(tensor, title=None):
    image = tensor.cpu().clone()
    image = image.squeeze(0)
    image = unloader(image)
    plt.imshow(image)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)


# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 运行图像风格迁移
output = style_transfer('content.jpg', 'style.jpg')

# 显示结果
plt.figure()
imshow(output, title='Output Image')

# 保存结果
output_img = unloader(output.cpu().clone().squeeze(0))
output_img.save('output.jpg')