DepthwiseConv2D深度方向的空间卷积
pointwise convolution,
SeparableConv2D深度可分离卷积

SeparableConv2D实现整个深度分离卷积过程，即深度方向的空间卷积 （分别作用于每个输入通道）+ 输出通道混合在一起的逐点卷积，

DepthwiseConv2D仅仅实现前半部分的空间卷积 （分别作用于每个输入通道）。

DepthwiseConv2D:不同channel用不同的卷积核做卷积

深度可分离 2D 卷积。

讯享网

深度可分离卷积包括仅执行深度空间卷积中的第一步（其分别作用于每个输入通道）。 depth_multiplier 参数控制深度步骤中每个输入通道生成多少个输出通道。

keras.layers.DepthwiseConv2D(kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1, data_format=None, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, bias_constraint=None) 

讯享网卷积核是作用于所有的depth方向的. depthwise conv对不同channel用不同的卷积核做卷积 

pointwise convolution

depthwise卷积没有考虑到同一位置不同channel上的数据之间的关联.所以我们用一个1 x
1的卷积核对depthwise得到的[3,64,64]输出再做卷积.这里做的就是常规卷积. 得到[1,64,64].
用4个这样的1x1卷积核去做常规卷积. 得到输出[4,64,64].

SeparableConv2D

keras.layers.SeparableConv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None) 

实现深度可分离卷积网络实战代码

讯享网#!/usr/bin/env python3 # -*- coding: utf-8 -*- import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import sklearn import pandas as pd import os import sys import time import tensorflow as tf from tensorflow import keras my_seed = 666 np.random.seed(my_seed) import random random.seed(my_seed) import tensorflow as tf tf.random.set_seed(my_seed) # 1,实现tensorflow动态按需分配GPU from tensorflow.compat.v1 import ConfigProto from tensorflow.compat.v1 import InteractiveSession config = ConfigProto() config.gpu_options.allow_growth = True session = InteractiveSession(config=config) # 打印使用的python库的版本信息 print(tf.__version__) print(sys.version_info) for module in mpl, np, pd, sklearn, tf, keras: print(module.__name__, module.__version__) # 2,从tf.keras.datasets中取数据 fashion_mnist = keras.datasets.fashion_mnist (x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data() x_valid, x_train = x_train_all[:5000], x_train_all[5000:] y_valid, y_train = y_train_all[:5000], y_train_all[5000:] print(x_valid.shape, y_valid.shape) print(x_train.shape, y_train.shape) print(x_test.shape, y_test.shape) # 3,将数据整合为标准化数据 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() # 整合数据为1通道数据 x_train_scaled = scaler.fit_transform( x_train.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28, 1) x_valid_scaled = scaler.transform( x_valid.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28, 1) x_test_scaled = scaler.transform( x_test.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28, 1) # 4,构建CNN模型 model = keras.models.Sequential() model.add(keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation='selu', input_shape=(28, 28, 1))) model.add(keras.layers.SeparableConv2D(filters=32, kernel_size=3, padding='same', activation='selu')) model.add(keras.layers.MaxPool2D(pool_size=2)) model.add(keras.layers.SeparableConv2D(filters=64, kernel_size=3, padding='same', activation='selu')) model.add(keras.layers.SeparableConv2D(filters=64, kernel_size=3, padding='same', activation='selu')) model.add(keras.layers.MaxPool2D(pool_size=2)) model.add(keras.layers.SeparableConv2D(filters=128, kernel_size=3, padding='same', activation='selu')) model.add(keras.layers.SeparableConv2D(filters=128, kernel_size=3, padding='same', activation='selu')) model.add(keras.layers.MaxPool2D(pool_size=2)) model.add(keras.layers.Flatten()) model.add(keras.layers.Dense(128, activation='selu')) model.add(keras.layers.Dense(10, activation="softmax")) model.compile(loss="sparse_categorical_crossentropy", optimizer = "sgd", metrics = ["accuracy"]) # 5,查看模型层级和参数 model.summary() # 6,定义callback 并 训练模型 logdir = './separable-cnn-selu-callbacks' if not os.path.exists(logdir): os.mkdir(logdir) output_model_file = os.path.join(logdir, "fashion_mnist_model.h5") callbacks = [ keras.callbacks.TensorBoard(logdir), keras.callbacks.ModelCheckpoint(output_model_file, save_best_only = True), keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3), ] history = model.fit(x_train_scaled, y_train, epochs=30, validation_data=(x_valid_scaled, y_valid), callbacks = callbacks) # 7,打印训练曲线 def plot_learning_curves(history): pd.DataFrame(history.history).plot(figsize=(8, 5)) plt.grid(True) plt.gca().set_ylim(0, 3) plt.show() plot_learning_curves(history) # 8,打印估计器结果 print(model.evaluate(x_test_scaled, y_test, verbose = 0))