总结：
https://blog.csdn.net/weixin_43624538/article/details/84963116
https://www.cnblogs.com/eniac1946/p/8669937.html
代码参考：
https://blog.csdn.net/shankezh/article/details/89088085
设计原则
- 2.1避免特征表示瓶颈，尤其是在网络的前面。要避免严重压缩导致的瓶颈。特征表示尺寸应该温和的减少，从输入端到输出端。特征表示的维度只是一个粗浅的信息量表示，它丢掉了一些重要的因素如相关性结构。
- 2.2高纬信息更适合在网络的局部处理。在卷积网络中逐步增加非线性激活响应可以解耦合更多的特征，那么网络就会训练的更快。
- 2.3空间聚合可以通过低纬嵌入，不会导致网络表示能力的降低。例如在进行大尺寸的卷积（如3*3）之前，我们可以在空间聚合前先对输入信息进行降维处理，如果这些信号是容易压缩的，那么降维甚至可以加快学习速度。
- 2.4平衡好网络的深度和宽度。通过平衡网络每层滤波器的个数和网络的层数可以是网络达到最佳性能。增加网络的宽度和深度都会提升网络的性能，但是两者并行增加获得的性能提升是最大的。
import tensorflow as tf import numpy as np from sklearn.model_selection import train_test_split import tensorflow.contrib.slim as slim class InceptionV3(object): def __init__(self): self.n_input = 299*299*3 self.n_classes = 10 self.batch_size = 10 self.training_iters = 20 self.display_step = 20 self.learning_rate = 0.001 def conv2d(self,x,filters,k_size,stride=[1,1],padding='SAME',activation = tf.nn.relu,scope='conv2d'): return tf.layers.conv2d(inputs=x,filters=filters,kernel_size=k_size, strides=stride,padding=padding,name=scope,activation=activation) def maxpool2d(self,x,pool_size=[2,2],stride=[2,2],padding='same',scope='conv2d'): return tf.layers.max_pooling2d(inputs=x,pool_size=pool_size,strides=stride,padding=padding,name=scope) def dropoutx(self,x,d_rate): return tf.layers.dropout(x,rate=d_rate) def avgpool2d(self,x,pool_size=[1,1],stride=[1,1],padding='same',scope='avgpool2d'): return tf.layers.average_pooling2d(x,pool_size=pool_size,strides=stride,padding=padding,name=scope) def inception_module_v3_1(self,x,filter_num,stride=[1,1],scope='inception_moudle_v3_1'): with tf.variable_scope(scope): with tf.variable_scope("bh1"): bh1 = self.conv2d(x,filters=filter_num[0],k_size=[1,1],stride=stride,scope='bh1_conv1_1x1') with tf.variable_scope("bh2"): bh2 = self.avgpool2d(x,pool_size=[1,1],stride=stride,scope='bh2_avg_3x3') bh2 = self.conv2d(bh2,filters=filter_num[2],k_size=[3,3],stride=stride,scope='bh2_conv_1x1') with tf.variable_scope("bh3"): bh3 = self.conv2d(x,filters=filter_num[3],k_size=[1,1],stride=stride,scope='bh3_conv1_1x1') bh3 = self.conv2d(bh3,filters=filter_num[4],k_size=[5,5],stride=stride,scope='bh3_conv2_3x3') with tf.variable_scope("bh4"): bh4 = self.conv2d(x,filters=filter_num[4],k_size=[1,1],stride=stride,scope='bh4_conv1_1x1') bh4 = self.conv2d(bh4,filters=filter_num[5],k_size=[3,3],stride=stride,scope='bh4_conv2_3x3') bh4 = self.conv2d(bh4,filters=filter_num[6],k_size=[3,3],stride=stride,scope='bh4_conv3_3x3') return tf.concat([bh1,bh2,bh3,bh4],axis=3) def inception_moudle_v3_2(self,net, scope, filter_num, stride=[1,1]): with tf.variable_scope(scope): with tf.variable_scope("bh1"): bh1 = self.conv2d(net, filters=filter_num[0], k_size=[1, 1], stride=stride, scope="bh1_conv_1x1") with tf.variable_scope("bh2"): bh2 = self.avgpool2d(net, [3, 3], stride=stride, scope='bh2_avg_3x3') bh2 = self.conv2d(bh2, filter_num[1], k_size=[1, 1], stride=stride, scope='bh2_conv_1x1') with tf.variable_scope("bh3"): bh3 = self.conv2d(net, filter_num[2], k_size=[1, 1], stride=stride, scope='bh3_conv1_1x1') bh3 = self.conv2d(bh3, filter_num[3], k_size=[1, 7], stride=stride, scope='bh3_conv2_1x7') bh3 = self.conv2d(bh3, filter_num[4], k_size=[7, 1], stride=stride, scope='bh3_conv3_7x1') with tf.variable_scope("bh4"): bh4 = self.conv2d(net, filter_num[5], [1, 1], stride=stride, scope='bh4_conv1_1x1') bh4 = self.conv2d(bh4, filter_num[6], [1, 7], stride=stride, scope='bh4_conv2_1x7') bh4 = self.conv2d(bh4, filter_num[7], [7, 1], stride=stride, scope='bh4_conv3_7x1') bh4 = self.conv2d(bh4, filter_num[8], [1, 7], stride=stride, scope='bh4_conv4_1x7') bh4 = self.conv2d(bh4, filter_num[9], [7, 1], stride=stride, scope='bh4_conv5_7x1') net = tf.concat([bh1, bh2, bh3, bh4], axis=3) return net def inception_moudle_v3_3(self,net, scope, filter_num, stride=[1,1]): with tf.variable_scope(scope): with tf.variable_scope("bh1"): bh1 = self.conv2d(net, filter_num[0], [1, 1], stride=stride, scope='bh1_conv_1x1') with tf.variable_scope("bh2"): bh2 = self.avgpool2d(net, [3, 3], stride=stride, scope='bh2_avg_3x3') bh2 = self.conv2d(bh2, filter_num[1], [1, 1], stride=stride, scope='bh2_conv_1x1') with tf.variable_scope("bh3"): bh3 = self.conv2d(net, filter_num[2], [1, 1], stride=stride, scope='bh3_conv1_1x1') bh3_1 = self.conv2d(bh3, filter_num[3], [3, 1], stride=stride, scope='bh3_conv2_3x1') bh3_2 = self.conv2d(bh3, filter_num[4], [1, 3], stride=stride, scope='bh3_conv2_1x3') with tf.variable_scope("bh4"): bh4 = self.conv2d(net, filter_num[5], [1, 1], stride=stride, scope='bh4_conv1_1x1') bh4 = self.conv2d(bh4, filter_num[6], [3, 3], stride=stride, scope='bh4_conv2_3x3') bh4_1 = self.conv2d(bh4, filter_num[7], [3, 1], stride=stride, scope='bh4_conv3_3x1') bh4_2 = self.conv2d(bh4, filter_num[8], [1, 3], stride=stride, scope='bh4_conv3_1x3') net = tf.concat([bh1, bh2, bh3_1, bh3_2, bh4_1, bh4_2], axis=3) return net def inception_moudle_v3_reduce(self,net, scope, filter_num): with tf.variable_scope(scope): with tf.variable_scope("bh1"): bh1 = self.avgpool2d(net, [3, 3], stride=[2,2], padding='VALID', scope="bh1_max_3x3") with tf.variable_scope("bh2"): bh2 = self.conv2d(net, filter_num[0], [1, 1], stride=[1,1], scope='bh2_conv1_1x1') bh2 = self.conv2d(bh2, filter_num[1], [3, 3], stride=[2,2], padding='VALID', scope='bh2_conv2_3x3') with tf.variable_scope("bh3"): bh3 = self.conv2d(net, filter_num[2], [1, 1], stride=[1,1], scope='bh3_conv1_1x1') bh3 = self.conv2d(bh3, filter_num[3], [3, 3], stride=[1,1], scope='bh3_conv2_3x3') bh3 = self.conv2d(bh3, filter_num[4], [3, 3], stride=[2,2], padding='VALID', scope='bh3_conv3_3x3') net = tf.concat([bh1, bh2, bh3], axis=3) return net def set_net(self,x,keep_prob,is_train=True,spatital_squeeze=True): check_point = {} net = tf.reshape(x,[-1,299,299,3]) net = self.conv2d(net, 32, [3, 3], stride=[2,2], scope="layer1") # 149x149 net = self.conv2d(net, 32, [3, 3], scope='layer2') # 147x147 net = self.conv2d(net, 64, [3, 3], padding='SAME', scope='layer3') # 147x147 net = self.maxpool2d(net, [3, 3], stride=[2,2],scope='layer4') # 73x73 net = self.conv2d(net, 80, [3, 3], scope='layer5') # 71x71 net = self.conv2d(net, 192, [3, 3], stride=[2,2],scope='layer6') # 35x35 net = self.conv2d(net, 288, [3, 3], scope='layer7') # 3 x inception net = self.inception_module_v3_1(net, scope='layer8', filter_num=[64, 32, 48, 64, 64, 96, 96]) # 35x35 net = self.inception_module_v3_1(net, scope='layer11', filter_num=[64, 64, 48, 64, 64, 96, 96]) net = self.inception_module_v3_1(net, scope='layer14', filter_num=[64, 64, 48, 64, 64, 96, 96]) print('1111',net) # 5 x inception net = self.inception_moudle_v3_reduce(net, scope='layer17', filter_num=[192, 384, 64, 96, 96]) # 17x17 print('2222',net) net = self.inception_moudle_v3_2(net, scope='layer20', filter_num=[192, 192, 128, 128, 192, 128, 128, 128, 128, 192]) net = self.inception_moudle_v3_2(net, scope='layer25', filter_num=[192, 192, 160, 160, 192, 160, 160, 160, 160, 192]) net = self.inception_moudle_v3_2(net, scope='layer30', filter_num=[192, 192, 160, 160, 192, 160, 160, 160, 160, 192]) net = self.inception_moudle_v3_2(net, scope='layer35', filter_num=[192, 192, 160, 160, 192, 160, 160, 160, 160, 192]) print(net) # 3 x inception net = self.inception_moudle_v3_reduce(net, scope='layer40', filter_num=[192, 320, 192, 192, 192]) # 8x8 net = self.inception_moudle_v3_3(net, scope='layer43', filter_num=[320, 192, 384, 384, 384, 448, 384, 384, 384]) net = self.inception_moudle_v3_3(net, scope='layer46', filter_num=[320, 192, 384, 384, 384, 448, 384, 384, 384]) print(net) net = self.avgpool2d(net, [8, 8], padding='VALID', scope='layer49') net = slim.dropout(net) net = slim.conv2d(net, self.n_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='layer50') print(net) if spatital_squeeze: net = tf.squeeze(net, [1, 2], name='squeeze') net = slim.softmax(net, scope='softmax') return net def inceptionv1_prediction(self, X,Y, scope='vgg'): X_train, X_vaild, y_train, y_vaild = train_test_split(X, Y, test_size=0.2) x = tf.placeholder(tf.float32, [None, self.n_input]) y = tf.placeholder(tf.float32, [None, self.n_classes]) pred = self.set_net(x,0.8) # pred是计算完的值，此时还没归一化 a = tf.nn.softmax(pred) # a是归一化后的值。 # 定义损失函数和学习步骤 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) # 这个是损失loss optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(cost) # 最小化loss # 测试网络 correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # 初始化所有的共享变量 init = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init) step = 1 # Keep training until reach max iterations while step * self.batch_size < self.training_iters: # 直到达到最大迭代次数，没考虑梯度！！！ batch_xs, batch_ys = X_train[self.batch_size*(step-1):self.batch_size*step],y_train[self.batch_size*(step-1):self.batch_size*step] batch_xs = np.reshape(batch_xs,(-1,self.n_input)) print('~!!!!!!!!',batch_xs.shape) print(batch_ys.shape) # 获取批数据 sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) if step % self.display_step == 0: # 每一步里有64batch，64*20=1280 # 计算精度 acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys}) # 计算损失值 loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys}) print("Iter " + str(step * self.batch_size) + ", Minibatch Loss= " + "{:.6f}".format( loss) + ", Training Accuracy = " + "{:.5f}".format(acc)) step += 1 print("Optimization Finished!") # 计算测试精度 X_vaild = np.reshape(X_vaild, (-1, self.n_input)) print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: X_vaild[:256], y: y_vaild[:256], })) # 拿前256个来测试 print("Testing Result:", sess.run(a, feed_dict={x: X_vaild[63:64], y: y_vaild[63:64], })) # 数组范围，从0开始，含左不含右 print(y_vaild[63:64])