non-local neural networks

非局部神经网络（non-local neural networks）是一种用来提高神经网络泛化能力的方法。它通过在网络中增加非局部块来捕捉输入数据中的非局部关系，使得网络能够学习到更加广泛的特征。这类网络最早被用于图像分类，但也可以应用到自然语言处理、视频分析等其他领域。

相关问题

disentangled non-local neural networks

“disentangled non-local neural networks”是一种神经网络的分类。这种神经网络的设计旨在实现将不同特征之间相互分离和独立，同时又能够考虑全局数据信息。这样的设计可以用于许多高级的视觉和自然语言处理应用程序，例如分析影像的不同要素和分类语言样本。

Non-local Neural Networks的tensorflow2代码

Non-local Neural Networks (NLNet) 是一个用于图像和视频处理的深度学习模型，它在处理长距离的空间关系时表现出色。下面是一个使用 TensorFlow 2 实现的 NLNet 模型的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Add, Input, Lambda, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2

def non_local_block(x, compression=2, mode='embedded_gaussian', reg=l2(0.)):
    '''
    Non-local block with optional compression and different modes:
    - 'dot_product': dot product similarity (original paper)
    - 'embedded_gaussian': embedded Gaussian similarity (default)
    - 'concatenation': concatenation-based similarity (not recommended)
    '''
    # Get input shape and channels
    input_shape = tf.shape(x)
    channels = input_shape[-1]
    
    # Define theta, phi, and g
    theta = Conv2D(channels // compression, 1, use_bias=False, kernel_regularizer=reg)(x)
    phi = Conv2D(channels // compression, 1, use_bias=False, kernel_regularizer=reg)(x)
    g = Conv2D(channels // compression, 1, use_bias=False, kernel_regularizer=reg)(x)
    
    # Reshape theta and phi to (N, H*W, C')
    theta = Lambda(lambda x: tf.reshape(x, (input_shape[0], -1, channels // compression)))(theta)
    phi = Lambda(lambda x: tf.reshape(x, (input_shape[0], -1, channels // compression)))(phi)
    
    # Compute similarity between theta and phi
    if mode == 'dot_product':
        similarity = Lambda(lambda x: tf.matmul(x[0], x[1], transpose_b=True))([theta, phi])
    elif mode == 'embedded_gaussian':
        theta = Lambda(lambda x: tf.expand_dims(x, 2))(theta)
        phi = Lambda(lambda x: tf.expand_dims(x, 1))(phi)
        theta_phi = Add()([theta, phi])
        f = Conv2D(1, 1, use_bias=False, kernel_regularizer=reg)(theta_phi)
        f = Activation('softmax')(f)
        similarity = Lambda(lambda x: tf.matmul(x[0], x[1]))([f, g])
    elif mode == 'concatenation':
        theta_phi = Concatenate(axis=-1)([theta, phi])
        h = Conv2D(channels // compression, 1, use_bias=False, kernel_regularizer=reg)(theta_phi)
        similarity = Lambda(lambda x: tf.matmul(x[0], x[1], transpose_b=True))([h, g])
    
    # Reshape similarity to (N, H, W, C')
    similarity = Lambda(lambda x: tf.reshape(x, (input_shape[0], input_shape[1], input_shape[2], channels // compression)))(similarity)
    
    # Compute output
    y = Conv2D(channels, 1, use_bias=False, kernel_regularizer=reg)(similarity)
    y = Add()([y, x])
    y = BatchNormalization()(y)
    y = Activation('relu')(y)
    
    return y

def build_nlnet(input_shape, num_classes, num_blocks=5, compression=2, mode='embedded_gaussian', reg=l2(0.)):
    '''
    Build NLNet model with optional number of blocks, compression, and mode.
    '''
    # Define input
    inputs = Input(shape=input_shape)
    
    # Initial convolution
    x = Conv2D(64, 3, padding='same', use_bias=False, kernel_regularizer=reg)(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    
    # Non-local blocks
    for i in range(num_blocks):
        x = non_local_block(x, compression=compression, mode=mode, reg=reg)
    
    # Final convolution and pooling
    x = Conv2D(128, 1, use_bias=False, kernel_regularizer=reg)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(num_classes, 1, use_bias=False, kernel_regularizer=reg)(x)
    x = BatchNormalization()(x)
    x = Activation('softmax')(x)
    x = Lambda(lambda x: tf.reduce_mean(x, axis=(1, 2)))(x)
    
    # Define model
    model = Model(inputs=inputs, outputs=x)
    
    return model

此代码实现了一个用于图像分类的 NLNet 模型，其中包含多个非局部块。该模型使用可配置的压缩因子和模式，并支持 L2 正则化。要使用此代码，请调用 `build_nlnet()` 函数并传递输入形状、类别数以及其他可选参数。例如：

input_shape = (224, 224, 3)
num_classes = 1000
model = build_nlnet(input_shape, num_classes, num_blocks=5, compression=2, mode='embedded_gaussian', reg=l2(0.0001))