模型介绍
模型特点：1.轻量级网络。2.可分离卷积。
简介：如果你知道VGG的话，你会发现，其实MobileNet就是将VGG中的标准卷积层替换成深度可分离卷积。
卷积： 一个12x12x3的特征图，采用5x5x3的卷积核进行卷积，得到8x8x1的特征图，如果有256个特征图，则得到8x8x256的特征图
可分离卷积：可分离卷积包括深度可分离卷积和空间可分离卷积
空间可分离卷积：就是把3x3的卷积核分成3x1和1x3的卷积核。
深度可分离卷积：深度卷积+逐点卷积。
代码：在建立模型的时候，可以使用Keras中的DepthwiseConv2D层实现深度可分离卷积，然后再利用1x1卷积调整channels数。
深度卷积：采用单通道进行卷积。如图，我们可以看到输出通道数为3，特征图的维度太少。
逐点卷积：就是1*1卷积，主要作用是变换维度。（升或降）经过深度卷积后我们得到8x8x3的特征图，我们采用256个1x1x3的卷积核进行卷积，得到8x8x256的特征图。
参数量和计算量的比较：
A.普通卷积
卷积核的尺寸是Dk×Dk×M，一共有N个，所以标准卷积的参数量是：
卷积核的尺寸是Dk×Dk×M，一共有N个，每一个都要进行Dw×Dh次运算，所以标准卷积的计算量是：
B.深度可分离卷积
深度卷积的卷积核尺寸Dk×Dk×M；逐点卷积的卷积核尺寸为1×1×M，一共有N个，所以深度可分离卷积的参数量是：
深度卷积的卷积核尺寸Dk×Dk×M，一共要做Dw×Dh次乘加运算；逐点卷积的卷积核尺寸为1×1×M，有N个，一共要做Dw×Dh次乘加运算，所以深度可分离卷积的计算量是：
下图是正常卷积和可分离卷积的网络结构：
ReLU6:如图所示。
性能比较：准确率下降了1%左右，但是计算量从4866下降到569。
MobileNet网络结构：
如图所示，首先采用3x3的卷积，然后就是深度可分离卷积，其中s2是下采样，即stride。最后采用平均池化将feature变成1x1，根据预测类别的大小加上全连接层，这里是1000，最后用一个softmax层。整个网络有24层，其中13层为深度可分离卷积。
softmax 可以理解为归一化，如目前图片分类有一百种，那经过 softmax 层的输出就是一个一百维的向量。向量中的第一个值就是当前图片属于第一类的概率值，向量中的第二个值就是当前图片属于第二类的概率值…这一百维的向量之和为1.
全连接：卷积取的是局部特征，全连接就是把以前的局部特征重新通过权值矩阵组装成完整的图。因为用到了所有的局部特征，所以叫全连接。
代码复现
这是一个3x3的卷积操作
def _conv_block(inputs, filters, kernel=(3, 3), strides=(1, 1)): x = Conv2D(filters, kernel, padding='same', use_bias=False, strides=strides, name='conv1')(inputs) x = BatchNormalization(name='conv1_bn')(x) return Activation(relu6, name='conv1_relu')(x)
深度可分离卷积：3X3X1深度卷积后，用1x1逐点卷积，每次卷积后都进行标准化和激活函数。
def _depthwise_conv_block(inputs, pointwise_conv_filters, depth_multiplier=1, strides=(1, 1), block_id=1): x = DepthwiseConv2D((3, 3), padding='same', depth_multiplier=depth_multiplier, strides=strides, use_bias=False, name='conv_dw_%d' % block_id)(inputs) x = BatchNormalization(name='conv_dw_%d_bn' % block_id)(x) x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x) x = Conv2D(pointwise_conv_filters, (1, 1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%d' % block_id)(x) x = BatchNormalization(name='conv_pw_%d_bn' % block_id)(x) return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)
MobileNet网络结构的代码实现： depth_multiplier=1是指卷积后的维度是1 dropout：暂时去除一些神经元，防止过拟合，使模型更有鲁棒性。 Dropout可以作为训练深度神经网络的一种trick供选择。在每个训练批次中， 通过忽略一半的特征检测器（让一半的隐层节点值为0）， 可以明显地减少过拟合现象。这种方式可以减少特征检测器（隐层节点）间的相互作 用，检测器相互作用是指某些检测器依赖其他检测器才能发挥作用。 Dropout说的简单一点就是：我们在前向传播的时候，让某个神经元的激活值以 一定的概率p停止工作，这样可以使模型泛化性更强，因为它不会太依赖某些局部 的特征
def MobileNet(input_shape=[224,224,3], depth_multiplier=1, dropout=1e-3, classes=1000): img_input = Input(shape=input_shape) # 224,224,3 -> 112,112,32 x = _conv_block(img_input, 32, strides=(2, 2)) # 112,112,32 -> 112,112,64 x = _depthwise_conv_block(x, 64, depth_multiplier, block_id=1) # 112,112,64 -> 56,56,128 x = _depthwise_conv_block(x, 128, depth_multiplier, strides=(2, 2), block_id=2) # 56,56,128 -> 56,56,128 x = _depthwise_conv_block(x, 128, depth_multiplier, block_id=3) # 56,56,128 -> 28,28,256 x = _depthwise_conv_block(x, 256, depth_multiplier, strides=(2, 2), block_id=4) # 28,28,256 -> 28,28,256 x = _depthwise_conv_block(x, 256, depth_multiplier, block_id=5) # 28,28,256 -> 14,14,512 x = _depthwise_conv_block(x, 512, depth_multiplier, strides=(2, 2), block_id=6) # 14,14,512 -> 14,14,512 x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=7) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=8) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=9) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=10) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=11) # 14,14,512 -> 7,7,1024 x = _depthwise_conv_block(x, 1024, depth_multiplier, strides=(2, 2), block_id=12) x = _depthwise_conv_block(x, 1024, depth_multiplier, block_id=13) # 7,7,1024 -> 1,1,1024 x = GlobalAveragePooling2D()(x) x = Reshape((1, 1, 1024), name='reshape_1')(x) x = Dropout(dropout, name='dropout')(x) x = Conv2D(classes, (1, 1),padding='same', name='conv_preds')(x) x = Activation('softmax', name='act_softmax')(x) x = Reshape((classes,), name='reshape_2')(x) inputs = img_input model = Model(inputs, x, name='mobilenet_1_0_224_tf') model_name = 'mobilenet_1_0_224_tf.h5' model.load_weights(model_name) return model
图片预测
通过下面代码就可以预测啦。
def preprocess_input(x): x /= 255. x -= 0.5 x *= 2. return x if __name__ == '__main__': model = MobileNet(input_shape=(224, 224, 3)) img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) print('Input image shape:', x.shape) preds = model.predict(x) print(np.argmax(preds)) print('Predicted:', decode_predictions(preds, 1))
最后放上所有代码：
import warnings import numpy as np from keras.preprocessing import image from keras.models import Model from keras.layers import DepthwiseConv2D,Input,Activation,Dropout,Reshape,BatchNormalization,GlobalAveragePooling2D,GlobalMaxPooling2D,Conv2D from keras.applications.imagenet_utils import decode_predictions from keras import backend as K def MobileNet(input_shape=[224,224,3], depth_multiplier=1, dropout=1e-3, classes=1000): img_input = Input(shape=input_shape) # 224,224,3 -> 112,112,32 x = _conv_block(img_input, 32, strides=(2, 2)) # 112,112,32 -> 112,112,64 x = _depthwise_conv_block(x, 64, depth_multiplier, block_id=1) # 112,112,64 -> 56,56,128 x = _depthwise_conv_block(x, 128, depth_multiplier, strides=(2, 2), block_id=2) # 56,56,128 -> 56,56,128 x = _depthwise_conv_block(x, 128, depth_multiplier, block_id=3) # 56,56,128 -> 28,28,256 x = _depthwise_conv_block(x, 256, depth_multiplier, strides=(2, 2), block_id=4) # 28,28,256 -> 28,28,256 x = _depthwise_conv_block(x, 256, depth_multiplier, block_id=5) # 28,28,256 -> 14,14,512 x = _depthwise_conv_block(x, 512, depth_multiplier, strides=(2, 2), block_id=6) # 14,14,512 -> 14,14,512 x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=7) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=8) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=9) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=10) x = _depthwise_conv_block(x, 512, depth_multiplier, block_id=11) # 14,14,512 -> 7,7,1024 x = _depthwise_conv_block(x, 1024, depth_multiplier, strides=(2, 2), block_id=12) x = _depthwise_conv_block(x, 1024, depth_multiplier, block_id=13) # 7,7,1024 -> 1,1,1024 # 7x7x1024 # 1024 x = GlobalAveragePooling2D()(x) x = Reshape((1, 1, 1024), name='reshape_1')(x) x = Dropout(dropout, name='dropout')(x) # 1024*2 x = Conv2D(classes, (1, 1),padding='same', name='conv_preds')(x) x = Activation('softmax', name='act_softmax')(x) x = Reshape((classes,), name='reshape_2')(x) inputs = img_input model = Model(inputs, x, name='mobilenet_1_0_224_tf') return model def _conv_block(inputs, filters, kernel=(3, 3), strides=(1, 1)): x = Conv2D(filters, kernel, padding='same', use_bias=False, strides=strides, name='conv1')(inputs) x = BatchNormalization(name='conv1_bn')(x) return Activation(relu6, name='conv1_relu')(x) def _depthwise_conv_block(inputs, pointwise_conv_filters, depth_multiplier=1, strides=(1, 1), block_id=1): x = DepthwiseConv2D((3, 3), padding='same', depth_multiplier=depth_multiplier, strides=strides, use_bias=False, name='conv_dw_%d' % block_id)(inputs) x = BatchNormalization(name='conv_dw_%d_bn' % block_id)(x) x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x) x = Conv2D(pointwise_conv_filters, (1, 1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%d' % block_id)(x) x = BatchNormalization(name='conv_pw_%d_bn' % block_id)(x) return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x) def relu6(x): return K.relu(x, max_value=6) def preprocess_input(x): x /= 255. x -= 0.5 x *= 2. return x if __name__ == '__main__': model = MobileNet(input_shape=(224, 224, 3)) model.summary() # img_path = 'elephant.jpg' # img = image.load_img(img_path, target_size=(224, 224)) # x = image.img_to_array(img) # x = np.expand_dims(x, axis=0) # x = preprocess_input(x) # print('Input image shape:', x.shape) # # preds = model.predict(x) # print(np.argmax(preds)) # print('Predicted:', decode_predictions(preds, 1))
