一、殘差git
殘差在數理統計中是指實際觀察值與估計值(擬合值)之間的差。在集成學習中能夠經過基模型擬合殘差,使得集成的模型變得更精確;在深度學習中也有人利用layer去擬合殘差將深度神經網絡的性能提升變強。這裏筆者選了Gradient Boosting和Resnet兩個算法試圖讓你們更感性的認識到擬合殘差的做用機理。算法
二、Gradient Boosting網絡
Gradient Boosting模型大體能夠總結爲三部:性能
下方代碼僅僅作了3步的殘差擬合,最後一步就是體現出集成學習的特徵,將多個基學習器組合成一個組合模型。學習
from sklearn.tree import DecisionTreeRegressor tree_reg1 = DecisionTreeRegressor(max_depth=2) tree_reg1.fit(X, y) y2 = y - tree_reg1.predict(X) tree_reg2 = DecisionTreeRegressor(max_depth=2) tree_reg2.fit(X, y2) y3 = y2 - tree_reg2.predict(X) tree_reg3 = DecisionTreeRegressor(max_depth=2) tree_reg3.fit(X, y3) y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))
其實上方代碼就等價於調用sklearn中的GradientBoostingRegressor這個集成學習API,同時將基學習器的個數n_estimators設爲3。spa
from sklearn.ensemble import GradientBoostingRegressor gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0) gbrt.fit(X, y)
形象的理解Gradient Boosting,其的過程就像射箭屢次射向同一個箭靶,上一次射的偏右,下一箭就會盡可能偏左一點,就這樣慢慢調整射箭的位置,使得箭的位置和靶心的誤差變小,最終射到靶心。這也是boosting的集成方式會減少模型bias的緣由。.net
殘差網絡的做用:scala
(1)爲何殘差學習的效果會如此的好?與其餘論文相比,深度殘差學習具備更深的網絡結構,此外,殘差學習也是網絡變深的緣由?爲何網絡深度如此的重要?設計
解:通常認爲神經網絡的每一層分別對應於提取不一樣層次的特徵信息,有低層,中層和高層,而網絡越深的時候,提取到的不一樣層次的信息會越多,而不一樣層次間的層次信息的組合也會越多。rest
(2)爲何在殘差以前網絡的深度最深的也只是GoogleNet 的22 層, 而殘差卻能夠達到152層,甚至1000層?
解:深度學習對於網絡深度遇到的主要問題是梯度消失和梯度爆炸,傳統對應的解決方案則是數據的初始化(normlized initializatiton)和(batch normlization)正則化,可是這樣雖然解決了梯度的問題,深度加深了,卻帶來了另外的問題,就是網絡性能的退化問題,深度加深了,錯誤率卻上升了,而殘差用來設計解決退化問題,其同時也解決了梯度問題,更使得網絡的性能也提高了。
殘差網絡的基本結構:
將輸入疊加到下層的輸出上。對於一個堆積層結構(幾層堆積而成)當輸入爲x時其學習到的特徵記爲H(x),如今咱們但願其能夠學習到殘差F(x)=H(x)-x,這樣其實原始的學習特徵是F(x)+x 。當殘差爲0時,此時堆積層僅僅作了恆等映射,至少網絡性能不會降低,實際上殘差不會爲0,這也會使得堆積層在輸入特徵基礎上學習到新的特徵,從而擁有更好的性能。
對着下方代碼咱們能夠更清晰的看到residual block的具體操做:
就獲得了residual block的總輸出,整個過程就是經過三層convolutiaon層去擬合residual block輸出與輸出的殘差m。
from keras.layers import Conv2D from keras.layers import add def residual_block(x, f=32, r=4): """ residual block :param x: the input tensor :param f: the filter numbers :param r: :return: """ m = conv2d(x, f // r, k=1) m = conv2d(m, f // r, k=3) m = conv2d(m, f, k=1) return add([x, m])
在resnet中殘差的思想就是去掉相同的主體部分,從而突出微小的變化,讓模型集中注意去學習一些這些微小的變化部分。這和咱們以前討論的Gradient Boosting中使用一個基學習器去學習殘差思想幾乎同樣。
三、slim庫
要學習殘差網絡,先學習slim庫的用法。
首先讓咱們看看tensorflow怎麼實現一個層,例如卷積層:
input = ... with tf.name_scope('conv1_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(input, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(bias, name=scope)
而後slim的實現:
input = ... net = slim.conv2d(input, 128, [3, 3], scope='conv1_1')
但這個不是重要的,由於tenorflow目前也有大部分層的簡單實現,這裏比較吸引人的是slim中的repeat和stack操做:
假設定義三個相同的卷積層:
net = ... net = slim.conv2d(net, 256, [3, 3], scope='conv3_1') net = slim.conv2d(net, 256, [3, 3], scope='conv3_2') net = slim.conv2d(net, 256, [3, 3], scope='conv3_3') net = slim.max_pool2d(net, [2, 2], scope='pool2')
在slim中的repeat操做能夠減小代碼量:
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') net = slim.max_pool2d(net, [2, 2], scope='pool2')
而stack是處理卷積核或者輸出不同的狀況:
假設定義三層FC:
x = slim.fully_connected(x, 32, scope='fc/fc_1') x = slim.fully_connected(x, 64, scope='fc/fc_2') x = slim.fully_connected(x, 128, scope='fc/fc_3')
使用stack操做:
slim.stack(x, slim.fully_connected, [32, 64, 128], scope='fc')
同理卷積層也同樣:
# 普通方法: x = slim.conv2d(x, 32, [3, 3], scope='core/core_1') x = slim.conv2d(x, 32, [1, 1], scope='core/core_2') x = slim.conv2d(x, 64, [3, 3], scope='core/core_3') x = slim.conv2d(x, 64, [1, 1], scope='core/core_4') # 簡便方法: slim.stack(x, slim.conv2d, [(32, [3, 3]), (32, [1, 1]), (64, [3, 3]), (64, [1, 1])], scope='core')
採用如上方法,定義一個VGG也就十幾行代碼的事了。
def vgg16(inputs): with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn=tf.nn.relu, weights_initializer=tf.truncated_normal_initializer(0.0, 0.01), weights_regularizer=slim.l2_regularizer(0.0005)): net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') net = slim.max_pool2d(net, [2, 2], scope='pool1') net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') net = slim.max_pool2d(net, [2, 2], scope='pool2') net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') net = slim.max_pool2d(net, [2, 2], scope='pool3') net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4') net = slim.max_pool2d(net, [2, 2], scope='pool4') net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5') net = slim.max_pool2d(net, [2, 2], scope='pool5') net = slim.fully_connected(net, 4096, scope='fc6') net = slim.dropout(net, 0.5, scope='dropout6') net = slim.fully_connected(net, 4096, scope='fc7') net = slim.dropout(net, 0.5, scope='dropout7') net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8') return net
這個沒什麼好說的,說一下直接拿經典網絡來訓練吧。
import tensorflow as tf vgg = tf.contrib.slim.nets.vgg # Load the images and labels. images, labels = ... # Create the model. predictions, _ = vgg.vgg_16(images) # Define the loss functions and get the total loss. loss = slim.losses.softmax_cross_entropy(predictions, labels)
【注】slim的卷積層默認是SAME模式的padding,這也就意味着卷積以後和卷積以前的大小相同。殘差網絡剛好須要這個特性。
四、殘差網絡模型
編寫殘差網絡單元
import tensorflow as tf import tensorflow.contrib.slim as slim def resnet_block(inputs,ksize,num_outputs,i): with tf.variable_scope('res_unit'+str(i)) as scope: part1 = slim.batch_norm(inputs,activation_fn=None) part2 = tf.nn.elu(part1) part3 = slim.conv2d(part2,num_outputs,[ksize,ksize],activation_fn=None) part4 = slim.batch_norm(part3,activation_fn=None) part5 = tf.nn.elu(part4) part6 = slim.conv2d(part5,num_outputs,[ksize,ksize],activation_fn=None) output = part6 + inputs return output def resnet(X_input,ksize,num_outputs,num_classes,num_blocks): layer1 = slim.conv2d(X_input,num_outputs,[ksize,ksize],normalizer_fn=slim.batch_norm,scope='conv_0') for i in range(num_blocks): layer1 = resnet_block(layer1,ksize,num_outputs,i+1) top = slim.conv2d(layer1,num_classes,[ksize,ksize],normalizer_fn=slim.batch_norm,activation_fn=None,scope='conv_top') top = tf.reduce_mean(top,[1,2]) output = slim.layers.softmax(slim.layers.flatten(top)) return output
五、訓練網絡
import tensorflow as tf import tensorflow.contrib.slim as slim from scrips import config from scrips import resUnit from scrips import read_tfrecord from scrips import convert2onehot import numpy as np log_dir = config.log_dir model_dir = config.model_dir IMG_W = config.IMG_W IMG_H = config.IMG_H IMG_CHANNELS = config.IMG_CHANNELS NUM_CLASSES = config.NUM_CLASSES BATCH_SIZE = config.BATCH_SIZE tf.reset_default_graph() X_input = tf.placeholder(shape=[None,IMG_W,IMG_H,IMG_CHANNELS],dtype=tf.float32,name='input') y_label = tf.placeholder(shape=[None,NUM_CLASSES],dtype=tf.int32) #***************************************************************************************************** output = resUnit.resnet(X_input,3,64,NUM_CLASSES,5) #***************************************************************************************************** #loss and accuracy loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_label, logits=output)) train_step = tf.train.AdamOptimizer(config.lr).minimize(loss) accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y_label, 1), tf.argmax(output, 1)), tf.float32)) #tensor board tf.summary.scalar('loss',loss) tf.summary.scalar('accuracy',accuracy) #從tfrecord中讀取數據及對應的標籤 image, label = read_tfrecord.read_and_decode(config.tfrecord_dir,IMG_W,IMG_H,IMG_CHANNELS) image_batches, label_batches = tf.train.shuffle_batch([image, label], batch_size=BATCH_SIZE, capacity=2000,min_after_dequeue=1000) #訓練網絡 init = tf.global_variables_initializer() saver = tf.train.Saver() with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state(model_dir)#從中間模型加載權重 if ckpt and ckpt.model_checkpoint_path: print('Restore model from ',end='') print(ckpt.model_checkpoint_path) saver.restore(sess,ckpt.model_checkpoint_path) if (ckpt.model_checkpoint_path.split('-')[-1]).isdigit(): global_step = int(ckpt.model_checkpoint_path.split('-')[-1]) print('Restore step at #',end='') print(global_step) else: global_step = 0 else: global_step = 0 sess.run(init) tensor_board_writer = tf.summary.FileWriter(log_dir,tf.get_default_graph()) merged = tf.summary.merge_all() #sess.graph.finalize() threads = tf.train.start_queue_runners(sess=sess) while True: try: global_step += 1 X_train, y_train = sess.run([image_batches, label_batches]) y_train_onehot = convert2onehot.one_hot(y_train,NUM_CLASSES) feed_dict = {X_input: X_train, y_label: y_train_onehot} [_, temp_loss, temp_accuracy,summary] = sess.run([train_step, loss, accuracy,merged], feed_dict=feed_dict) tensor_board_writer.add_summary(summary,global_step) if global_step % config.display == 0: print('step at #{},'.format(global_step), end=' ') print('train loss: {:.5f}'.format(temp_loss), end=' ') print('train accuracy: {:.2f}%'.format(temp_accuracy * 100)) if global_step % config.snapshot== 0: saver.save(sess,model_dir+'/model.ckpt',global_step) except: tensor_board_writer.close() break;