efficientnet tensorflow

EfficientNet是一种高效的神经网络模型，它在TensorFlow框架下实现。它是由谷歌公司开发的，可以在计算资源有限的情况下获得更好的性能。它采用了一种新的方法，即使用复合缩放系数来平衡深度、宽度和分辨率的比例，从而实现更好的性能和效率。在许多计算机视觉任务中，EfficientNet已经取得了很好的结果。

相关问题

efficientnet的权重

EfficientNet有多个版本，每个版本有不同的权重。以下是一些常见的EfficientNet版本及其权重：

- EfficientNet-B0：16.1M个参数，大小为41 MB
- EfficientNet-B1：29.5M个参数，大小为76 MB
- EfficientNet-B2：39.0M个参数，大小为116 MB
- EfficientNet-B3：48.0M个参数，大小为155 MB
- EfficientNet-B4：63.3M个参数，大小为223 MB
- EfficientNet-B5：87.3M个参数，大小为308 MB
- EfficientNet-B6：141.3M个参数，大小为487 MB
- EfficientNet-B7：198.4M个参数，大小为669 MB

这些权重可以从TensorFlow官方网站上下载。

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\