使用efficientnet代碼

以下是使用EfficientNet模型进行图像分类的Python代码示例：

import tensorflow as tf
from tensorflow.keras.applications import EfficientNetB0

# 加载预训练模型
model = EfficientNetB0(weights='imagenet')

# 加载图像并进行预处理
img_path = 'path/to/image.jpg'
img_size = (224, 224)
img = tf.keras.preprocessing.image.load_img(img_path, target_size=img_size)
x = tf.keras.preprocessing.image.img_to_array(img)
x = tf.keras.applications.efficientnet.preprocess_input(x)

# 进行预测
preds = model.predict(tf.expand_dims(x, axis=0))

在上面的示例中，我们首先使用`EfficientNetB0`函数加载预训练模型。然后，我们加载要进行分类的图像，并将其大小调整为(224, 224)。接下来，我们将图像转换为NumPy数组，并对其进行预处理以与EfficientNet模型兼容。最后，我们使用模型的`predict`方法进行预测，并得到一个包含分类概率的NumPy数组。

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efficientnet代码

以下是使用TensorFlow实现的EfficiN的代码示例：\n\```pyth\impor tensorflow as tf\from tensorflow.keras impor layers\n\f swish(x):\ retur tf.keras.activions.swish(x)\n\f rou_filters(filters, width_coeffici, depth_divisor):\ filters *= width_coeffici\ new_filters = max(depth_divisor, i(filters + depth_divisor / 2) // depth_divisor * depth_divisor)\ if new_filters < .9 * filters\ new_filters += depth_divisor\ retur i(new_filters)\n\f rou_rpeats(rpeats, depth_coeffici):\ retur i(math.cei(depth_coeffici * repeats))\n\ss SEBlock(tf.keras.M):\ def __ini__(self, inpu_filters, s_rati, exp_rati):\ super(SEBlock, self).__ini__()\ self.inpu_filters = inpu_filters\ self.s_rati = s_rati\ self.exp_filters = i(inpu_filters * exp_rati)\n\ self._bui()\n\ def _bui(self):\ num_ru_filters = max(1, i(self.inpu_filters * self.s_rati))\ self.redu_conv = layers.Conv2D(num_ru_filters, ker_siz=[1, 1], strides=[1, 1], padding='sam')\ self.exp_conv = layers.Conv2D(self.exp_filters, ker_siz=[1, 1], strides=[1, 1], padding='sam')\n\ def (self, inputs, training=Fals):\ x = inputs\ x = tf.keras.layers.GlobAveragPooling2D()(x)\ x = tf.keras.layers.Reshap((1, 1, self.inpu_filters))(x)\ x = self.redu_conv(x)\ x = swish(x)\ x = self.exp_conv(x)\ x = tf.keras.activions.sigmoi(x)\ x = layers.multiply([inputs, x])\ retur x\n\ss MBConvBlock(tf.keras.M):\ def __ini__(self, inpu_filters, outpu_filters, ker_siz, strides, exp_rati, s_rati, i_skip, drop__r):\ super(MBConvBlock, self).__ini__()\ self.inpu_filters = inpu_filters\ self.outpu_filters = outpu_filters\ self.ker_siz = ker_siz\ self.strides = strides\ self.exp_rati = exp_rati\ self.s_rati = s_rati\ self.i_skip = i_skip\ self.drop__r = drop__r\n\ self._bui()\n\ def _bui(self):\ self.has_s = (self.s_rati is N) ( < self.s_rati <= 1)\ self.filters = self.inpu_filters * self.exp_rati\ if self.exp_rati != 1\ self.exp_conv = layers.Conv2D(self.filters, ker_siz=[1, 1], strides=[1, 1], padding='sam')\ self.b = layers.BatchNormalizati()\ self.depthwis_conv = layers.DepthwisConv2D(ker_siz=self.ker_siz, strides=self.strides, padding='sam')\ self.b1 = layers.BatchNormalizati()\ if self.has_s\ self.s_block = SEBlock(self.filters, self.s_rati, self.exp_rati)\ self.proj_conv = layers.Conv2D(self.outpu_filters, ker_siz=[1, 1], strides=[1, 1], padding='sam')\ self.b2 = layers.BatchNormalizati()\n\ def (self, inputs, training=Fals):\ x = inputs\ if self.exp_rati != 1\ x = self.exp_conv(x)\ x = self.b(x, training=raining)\ x = swish(x)\n\ x = self.depthwis_conv(x)\ x = self.b1(x, training=raining)\ x = swish(x)\n\ if self.has_s\ x = self.s_block(x)\n\ x = self.proj_conv(x)\ x = self.b2(x, training=raining)\n\ if self.i_skip self.strides == 1 self.inpu_filters == self.outpu_filters\ if self.drop__r\ x = tf.keras.layers.Dropou(self.drop__r)(x, training=raining)\ x = layers.([x, inputs])\ retur x\n\ss EfficiN(tf.keras.M):\ def __ini__(self, width_coeffici, depth_coeffici, defau_resoluti, dropou_r, num_classes, depth_divisor=8, mi_depth=N):\ super(EfficiN, self).__ini__()\ self.width_coeffici = width_coeffici\ self.depth_coeffici = depth_coeffici\ self.defau_resoluti = defau_resoluti\ self.dropou_r = dropou_r\ self.num_classes = num_classes\ self.depth_divisor = depth_divisor\ self.mi_depth = mi_depth\n\ self._bui()\n\ def _bui(self):\ self.blocks_args = [\ {'ker_siz' 3, 'rpeats' 1, 'filters_i' 32, 'filters_ou' 16, 'xp_rati' 1, 'i_skip' Tru, 'strides' 1, 's_rati' .25},\ {'ker_siz' 3, 'rpeats' 2, 'filters_i' 16, 'filters_ou' 24, 'xp_rati' 6, 'i_skip' Tru, 'strides' 2, 's_rati' .25},\ {'ker_siz' 5, 'rpeats' 2, 'filters_i' 24, 'filters_ou' 40, 'xp_rati' 6, 'i_skip' Tru, 'strides' 2, 's_rati' .25},\ {'ker_siz' 3, 'rpeats' 3, 'filters_i' 40, 'filters_ou' 80, 'xp_rati' 6, 'i_skip' Tru, 'strides' 2, 's_rati' .25},\ {'ker_siz' 5, 'rpeats' 3, 'filters_i' 80, 'filters_ou' 112, 'xp_rati' 6, 'i_skip' Tru, 'strides' 1, 's_rati' .25},\ {'ker_siz' 5, 'rpeats' 4, 'filters_i' 112, 'filters_ou' 192, 'xp_rati' 6, 'i_skip' Tru, 'strides' 2, 's_rati' .25},\ {'ker_siz' 3, 'rpeats' 1, 'filters_i' 192, 'filters_ou' 320, 'xp_rati' 6, 'i_skip' Tru, 'strides' 1, 's_rati' .25},\ ]\n\ self.stem_conv = layers.Conv2D(rou_filters(32, self.width_coeffici, self.depth_divisor), ker_siz=[3, 3], strides=[2, 2], padding='sam')\ self.b = layers.BatchNormalizati()\n\ self.blocks = []\ for block_args i self.blocks_args\ block_args['filters_i'] = rou_filters(block_args['filters_i'], self.width_coeffici, self.depth_divisor)\ block_args['filters_ou'] = rou_filters(block_args['filters_ou'], self.width_coeffici, self.depth_divisor)\ block_args['rpeats'] = rou_rpeats(block_args['rpeats'], self.depth_coeffici)\n\ self.blocks.app(MBConvBlock(**block_args))\n\ if block_args['rpeats'] > 1\ block_args['filters_i'] = block_args['filters_ou']\ block_args['strides'] = 1\n\ for _ i rang(block_args['rpeats'] - 1):\ self.blocks.app(MBConvBlock(**block_args))\n\ self.h_conv = layers.Conv2D(rou_filters(128, self.width_coeffici, self.depth_divisor), ker_siz=[1, 1], strides=[1, 1], padding='sam')\ self.b1 = layers.BatchNormalizati()\ self.avg_p = layers.GlobAveragPooling2D()\ self.dropou = layers.Dropou(self.dropou_r)\ self.f = layers.Dens(self.num_classes)\n\ def (self, inputs, training=Fals):\ x = inputs\ x = self.stem_conv(x)\ x = self.b(x, training=raining)\ x = swish(x)\n\ for idx, block i enumer(self.blocks):\ drop__r = self.drop__r\ if drop__r\ drop__r *= f(idx) / (self.blocks)\ x = block(x, training=raining)\n\ x = self.h_conv(x)\ x = self.b1(x, training=raining)\ x = swish(x)\n\ x = self.avg_p(x)\ x = self.dropou(x, training=raining)\ x = self.f(x)\n\ retur x\```\n\

EfficientNet网络代码

以下是使用 Keras 实现的 EfficientNetB0 网络的代码示例：

from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, Activation, Add, Dropout, GlobalAveragePooling2D, Dense
from keras.utils import plot_model
from keras import backend as K

# 定义 Swish 激活函数
def swish(x):
    return K.sigmoid(x) * x

# 定义 Depthwise Convolution 模块
def depthwise_conv_block(inputs, pointwise_conv_filters, alpha=1.0, depth_multiplier=1, strides=(1, 1)):
    pointwise_conv_filters = int(pointwise_conv_filters * alpha)

    x = inputs
    # Depthwise Convolution
    x = DepthwiseConv2D((3, 3), padding='same', depth_multiplier=depth_multiplier, strides=strides)(x)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    # Pointwise Convolution
    x = Conv2D(pointwise_conv_filters, (1, 1), padding='same', strides=(1, 1))(x)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    return x

# 定义 Squeeze-and-Excitation 模块
def squeeze_excite_block(inputs, filters, ratio=16):
    # Squeeze
    se = GlobalAveragePooling2D()(inputs)
    se = Dense(filters // ratio, activation='relu')(se)
    # Excite
    se = Dense(filters, activation='sigmoid')(se)
    x = inputs
    x = Multiply()([x, se])
    return x

# 定义 EfficientNetB0 网络
def EfficientNetB0(input_shape, classes):
    inputs = Input(shape=input_shape)

    # Stem
    x = Conv2D(32, (3, 3), strides=(2, 2), padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    # MBConv1
    x = depthwise_conv_block(x, 16, alpha=1.0, depth_multiplier=1, strides=(1, 1))

    # MBConv6
    x = depthwise_conv_block(x, 24, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 24)
    x = depthwise_conv_block(x, 24, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 24)
    x = depthwise_conv_block(x, 24, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 24)

    # MBConv6
    x = depthwise_conv_block(x, 40, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 40)
    x = depthwise_conv_block(x, 40, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 40)
    x = depthwise_conv_block(x, 40, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 40)

    # MBConv6
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 80)
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 80)
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 80)
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 80)

    # MBConv6
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)

    # MBConv6
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 192)
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 192)
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 192)
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 192)

    # MBConv6
    x = depthwise_conv_block(x, 320, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 320)

    # Top
    x = Conv2D(1280, (1, 1), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    # Average Pooling
    x = GlobalAveragePooling2D()(x)

    # Dropout
    x = Dropout(0.2)(x)

    # Output
    outputs = Dense(classes, activation='softmax')(x)

    # 创建模型
    model = Model(inputs, outputs)

    return model

注意，这个代码示例中只实现了 EfficientNetB0 网络的部分层，如果要实现完整的 EfficientNet 网络，需要根据具体的网络结构进行修改。