cv計(jì)算機(jī)視覺(jué)訓(xùn)練營(yíng)貪心

1、深度學(xué)習(xí)調(diào)參一. 建立評(píng)價(jià)指標(biāo)最好是一個(gè)指標(biāo)指標(biāo)是容易理解的指標(biāo)的范圍最好是在0,1之間, 最好是線性的.回歸問(wèn)題: 均方差 mse分類問(wèn)題: F1 分?jǐn)?shù)(good), 準(zhǔn)確率precision + 召回率recall (bad)標(biāo)題生成: BLEU二. 建立合理的預(yù)期深度網(wǎng)絡(luò)可能達(dá)到的評(píng)價(jià)指標(biāo)最好的值? (以image captioning為例)Image captioning可以理解為四個(gè)串行任務(wù):1. 目標(biāo)識(shí)別2. 目標(biāo)動(dòng)作識(shí)別3. 場(chǎng)景理解4. 語(yǔ)言合成1. 目標(biāo)檢測(cè)1. 目標(biāo)檢測(cè)的效果2. 動(dòng)作識(shí)別 2. 動(dòng)作識(shí)別效果 3. 場(chǎng)景理解 3.場(chǎng)景理解的效果 4. 文本合成4. 文本合成

2、的效果 Image Captioning 預(yù)期的最佳效果目標(biāo)檢測(cè)效果 * 動(dòng)作識(shí)別效果 * 場(chǎng)景理解效果 * 文本合成效果 = 0.36 * 0.88 * 0.66 * 0.753 = 0.16三. 建立合理的數(shù)據(jù)集測(cè)試數(shù)據(jù)集的數(shù)據(jù) = 訓(xùn)練數(shù)據(jù)集的數(shù)據(jù)?以image captioning 為例: 1. 測(cè)試圖像里面的物體(人, 狗, 飛機(jī), 籃球)和場(chǎng)景(打籃球, 坐飛機(jī))是否出現(xiàn)在訓(xùn)練數(shù)據(jù)集中? 如果沒(méi)有, 需要添加具有這些物體和場(chǎng)景的圖像在訓(xùn)練數(shù)據(jù)集中2. 測(cè)試圖像對(duì)應(yīng)的標(biāo)題中的單詞(飛機(jī), 籃球, 坐飛機(jī), 打籃球) 和句子(一群男孩在打籃球) 是否出現(xiàn)在訓(xùn)練數(shù)據(jù)集中? 如果沒(méi)有, 也

3、需要添加這些物體和場(chǎng)景在訓(xùn)練數(shù)據(jù)集中3. 如果測(cè)試數(shù)據(jù)和訓(xùn)練數(shù)據(jù)的coverage(包含的圖像內(nèi)容, 和單詞句子描述)都一致的情況下. 網(wǎng)絡(luò)是欠擬合還是過(guò)擬合, 如果欠擬合, 應(yīng)該增加網(wǎng)絡(luò)復(fù)雜度四. 判斷是否欠/過(guò)擬合Underfitting (欠擬合)Good fitting (正好擬合)Overfitting (過(guò)擬合)f(x) = ax + b f(x) = a x4 + b x3 + c x2 + dx + e f(x) = a x15 + b x14 + 理解bias/variance理解bias/variance (續(xù))Bias 和variance的平衡判斷過(guò)/欠擬合的一般代碼欠擬

4、合loss變化曲線(1)理論上的比較好的lossfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import LSTMfrom matplotlib import pyplotfrom numpy import array# return training datadef get_train():seq = 0.0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.5seq = array(seq)X, y = seq:, 0, seq:,

5、1X = X.reshape(len(X), 1, 1)return X, y# return validation datadef get_val():seq = 0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8, 0.9, 0.9, 1.0seq = array(seq)X, y = seq:, 0, seq:, 1X = X.reshape(len(X), 1, 1)return X, y# define modelmodel = Sequential()model.add(LSTM(10, input_shape=(1,1)model.add(Dense(1, act

6、ivation=linear)# compile model pile(loss=mse, optimizer=adam)# fit modelX,y = get_train()valX, valY = get_val()history = model.fit(X, y, epochs=100, validation_data=(valX, valY), shuffle=False)# plot train and validation losspyplot.plot(history.historyloss)pyplot.plot(history.historyval_loss)pyplot.

7、title(model train vs validation loss)pyplot.ylabel(loss)pyplot.xlabel(epoch)pyplot.legend(train, validation, loc=upper right)pyplot.show()消除欠擬合(1)增加訓(xùn)練epoch增大batch-size調(diào)整激活函數(shù)(使用relu)調(diào)整優(yōu)化算法使用Adam增大learning rate理論上的比較好的loss欠擬合loss變化曲線(2)理論上的比較好的loss0.0from keras.models import Sequentialfrom keras.layer

8、s import Densefrom keras.layers import LSTMfrom matplotlib import pyplotfrom numpy import array# return training datadef get_train():seq = 0.0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.5seq = array(seq)X, y = seq:, 0, seq:, 1X = X.reshape(5, 1, 1)return X, y# return validation datadef get_val():seq

9、 = 0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8, 0.9, 0.9, 1.0seq = array(seq)X, y = seq:, 0, seq:, 1X = X.reshape(len(X), 1, 1)return X, y# define modelmodel = Sequential()model.add(LSTM(1, input_shape=(1,1)model.add(Dense(1, activation=linear)# compile model pile(loss=mae, optimizer=sgd)# fit modelX,y = get_

10、train()valX, valY = get_val()history = model.fit(X, y, epochs=300, validation_data=(valX, valY), shuffle=False)# plot train and validation losspyplot.plot(history.historyloss)pyplot.plot(history.historyval_loss)pyplot.title(model train vs validation loss)pyplot.ylabel(loss)pyplot.xlabel(epoch)pyplot

11、.legend(train, validation, loc=upper right)pyplot.show()消除欠擬合(2)增加網(wǎng)絡(luò)復(fù)雜度增加層數(shù)增加卷積層輸出的通道數(shù)增加全連接層的節(jié)點(diǎn)數(shù)2. 檢測(cè)訓(xùn)練數(shù)據(jù)集和測(cè)試數(shù)據(jù)是否有相對(duì)應(yīng)的特征增加訓(xùn)練數(shù)據(jù)的種類, 使得訓(xùn)練數(shù)據(jù)覆蓋所有測(cè)試數(shù)據(jù)的特性使用數(shù)據(jù)增強(qiáng)理論上的比較好的loss0.0好的擬合loss變化曲線理論上的比較好的lossfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import LSTMfrom matplotl

12、ib import pyplotfrom numpy import array# return training datadef get_train():seq = 0.0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.5seq = array(seq)X, y = seq:, 0, seq:, 1X = X.reshape(5, 1, 1)return X, y# return validation datadef get_val():seq = 0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8, 0.9, 0.9, 1.0seq =

13、 array(seq)X, y = seq:, 0, seq:, 1X = X.reshape(len(X), 1, 1)return X, y# define modelmodel = Sequential()model.add(LSTM(10, input_shape=(1,1)model.add(Dense(1, activation=linear)# compile model pile(loss=mse, optimizer=adam)# fit modelX,y = get_train()valX, valY = get_val()history = model.fit(X, y,

14、 epochs=800, validation_data=(valX, valY), shuffle=False)# plot train and validation losspyplot.plot(history.historyloss)pyplot.plot(history.historyval_loss)pyplot.title(model train vs validation loss)pyplot.ylabel(loss)pyplot.xlabel(epoch)pyplot.legend(train, validation, loc=upper right)pyplot.show

15、()過(guò)擬合loss變化曲線理論上的比較好的loss理論上的比較好的lossfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import LSTMfrom matplotlib import pyplotfrom numpy import array# return training datadef get_train():seq = 0.0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.5seq = array(seq)X, y = se

16、q:, 0, seq:, 1X = X.reshape(5, 1, 1)return X, y# return validation datadef get_val():seq = 0.5, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8, 0.9, 0.9, 1.0seq = array(seq)X, y = seq:, 0, seq:, 1X = X.reshape(len(X), 1, 1)return X, y# define modelmodel = Sequential()model.add(LSTM(10, input_shape=(1,1)model.add(Dens

17、e(1, activation=linear)# compile model pile(loss=mse, optimizer=adam)# fit modelX,y = get_train()valX, valY = get_val()history = model.fit(X, y, epochs=1200, validation_data=(valX, valY), shuffle=False)# plot train and validation losspyplot.plot(history.historyloss500:)pyplot.plot(history.historyval_loss500:)pyplot.title(model train vs validation loss)pyplot.ylabel(loss)pyplot.xlabel(epoch)pyplo