Inception-v4 搭建模型代码

Inception-v4 是一种用于图像分类的卷积神经网络模型，它是 Google 在 2016 年提出的一种改进版本，相较于 Inception-v3，它在网络架构上做了一些改进，可以获得更好的性能。下面是使用 TensorFlow 搭建 Inception-v4 模型的代码：

import tensorflow as tf

def conv2d_bn(x, filters, kernel_size, strides=1, padding='same', activation='relu', use_bias=False):
    x = tf.keras.layers.Conv2D(filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    if activation is not None:
        x = tf.keras.layers.Activation(activation)(x)
    return x

def block_inception_a(x):
    branch_1x1 = conv2d_bn(x, 96, 1)

    branch_5x5 = conv2d_bn(x, 64, 1)
    branch_5x5 = conv2d_bn(branch_5x5, 96, 5)

    branch_3x3dbl = conv2d_bn(x, 64, 1)
    branch_3x3dbl = conv2d_bn(branch_3x3dbl, 96, 3)
    branch_3x3dbl = conv2d_bn(branch_3x3dbl, 96, 3)

    branch_pool = tf.keras.layers.AveragePooling2D(3, strides=1, padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 96, 1)

    x = tf.keras.layers.concatenate([branch_1x1, branch_5x5, branch_3x3dbl, branch_pool], axis=3)
    return x

def block_reduction_a(x):
    branch_3x3 = conv2d_bn(x, 384, 3, strides=2, padding='valid')

    branch_3x3dbl = conv2d_bn(x, 192, 1)
    branch_3x3dbl = conv2d_bn(branch_3x3dbl, 224, 3)
    branch_3x3dbl = conv2d_bn(branch_3x3dbl, 256, 3, strides=2, padding='valid')

    branch_pool = tf.keras.layers.MaxPooling2D(3, strides=2, padding='valid')(x)

    x = tf.keras.layers.concatenate([branch_3x3, branch_3x3dbl, branch_pool], axis=3)
    return x

def block_inception_b(x):
    branch_1x1 = conv2d_bn(x, 384, 1)

    branch_7x7 = conv2d_bn(x, 192, 1)
    branch_7x7 = conv2d_bn(branch_7x7, 224, [1, 7])
    branch_7x7 = conv2d_bn(branch_7x7, 256, [7, 1])

    branch_7x7dbl = conv2d_bn(x, 192, 1)
    branch_7x7dbl = conv2d_bn(branch_7x7dbl, 192, [7, 1])
    branch_7x7dbl = conv2d_bn(branch_7x7dbl, 224, [1, 7])
    branch_7x7dbl = conv2d_bn(branch_7x7dbl, 224, [7, 1])
    branch_7x7dbl = conv2d_bn(branch_7x7dbl, 256, [1, 7])

    branch_pool = tf.keras.layers.AveragePooling2D(3, strides=1, padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 128, 1)

    x = tf.keras.layers.concatenate([branch_1x1, branch_7x7, branch_7x7dbl, branch_pool], axis=3)
    return x

def block_reduction_b(x):
    branch_3x3 = conv2d_bn(x, 192, 1)
    branch_3x3 = conv2d_bn(branch_3x3, 192, 3, strides=2, padding='valid')

    branch_7x7x3 = conv2d_bn(x, 256, 1)
    branch_7x7x3 = conv2d_bn(branch_7x7x3, 256, [1, 7])
    branch_7x7x3 = conv2d_bn(branch_7x7x3, 320, [7, 1])
    branch_7x7x3 = conv2d_bn(branch_7x7x3, 320, 3, strides=2, padding='valid')

    branch_pool = tf.keras.layers.MaxPooling2D(3, strides=2, padding='valid')(x)

    x = tf.keras.layers.concatenate([branch_3x3, branch_7x7x3, branch_pool], axis=3)
    return x

def inception_v4(input_shape=(299, 299, 3), num_classes=1001):
    inputs = tf.keras.layers.Input(shape=input_shape)

    x = conv2d_bn(inputs, 32, 3, strides=2, padding='valid')
    x = conv2d_bn(x, 32, 3, padding='valid')
    x = conv2d_bn(x, 64, 3)

    x = tf.keras.layers.MaxPooling2D(3, strides=2)(x)

    x = conv2d_bn(x, 80, 1)
    x = conv2d_bn(x, 192, 3, padding='valid')

    x = tf.keras.layers.MaxPooling2D(3, strides=2)(x)

    x = block_inception_a(x)
    x = block_inception_a(x)
    x = block_inception_a(x)
    x = block_reduction_a(x)

    x = block_inception_b(x)
    x = block_inception_b(x)
    x = block_inception_b(x)
    x = block_inception_b(x)
    x = block_reduction_b(x)

    x = block_inception_a(x)
    x = block_inception_a(x)

    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dropout(0.2)(x)

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

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

这个代码定义了 `conv2d_bn` 函数来创建一个卷积层和一个批归一化层，然后使用 `block_inception_a`、`block_reduction_a`、`block_inception_b` 和 `block_reduction_b` 函数来搭建 Inception-v4 的模型结构。最后，使用 `inception_v4` 函数将这些层组合起来，并返回一个 Keras 模型对象。