Python實(shí)現(xiàn)Keras搭建神經(jīng)網(wǎng)絡(luò)訓(xùn)練分類模型教程
我就廢話不多說(shuō)了,大家還是直接看代碼吧~
注釋講解版:
# Classifier example import numpy as np # for reproducibility np.random.seed(1337) # from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense, Activation from keras.optimizers import RMSprop # 程序中用到的數(shù)據(jù)是經(jīng)典的手寫體識(shí)別mnist數(shù)據(jù)集 # download the mnist to the path if it is the first time to be called # X shape (60,000 28x28), y # (X_train, y_train), (X_test, y_test) = mnist.load_data() # 下載minst.npz: # 鏈接: https://pan.baidu.com/s/1b2ppKDOdzDJxivgmyOoQsA # 提取碼: y5ir # 將下載好的minst.npz放到當(dāng)前目錄下 path='./mnist.npz' f = np.load(path) X_train, y_train = f['x_train'], f['y_train'] X_test, y_test = f['x_test'], f['y_test'] f.close() # data pre-processing # 數(shù)據(jù)預(yù)處理 # normalize # X shape (60,000 28x28),表示輸入數(shù)據(jù) X 是個(gè)三維的數(shù)據(jù) # 可以理解為 60000行數(shù)據(jù),每一行是一張28 x 28 的灰度圖片 # X_train.reshape(X_train.shape[0], -1)表示:只保留第一維,其余的緯度,不管多少緯度,重新排列為一維 # 參數(shù)-1就是不知道行數(shù)或者列數(shù)多少的情況下使用的參數(shù) # 所以先確定除了參數(shù)-1之外的其他參數(shù),然后通過(guò)(總參數(shù)的計(jì)算) / (確定除了參數(shù)-1之外的其他參數(shù)) = 該位置應(yīng)該是多少的參數(shù) # 這里用-1是偷懶的做法,等同于 28*28 # reshape后的數(shù)據(jù)是:共60000行,每一行是784個(gè)數(shù)據(jù)點(diǎn)(feature) # 輸入的 x 變成 60,000*784 的數(shù)據(jù),然后除以 255 進(jìn)行標(biāo)準(zhǔn)化 # 因?yàn)槊總€(gè)像素都是在 0 到 255 之間的,標(biāo)準(zhǔn)化之后就變成了 0 到 1 之間 X_train = X_train.reshape(X_train.shape[0], -1) / 255 X_test = X_test.reshape(X_test.shape[0], -1) / 255 # 分類標(biāo)簽編碼 # 將y轉(zhuǎn)化為one-hot vector y_train = np_utils.to_categorical(y_train, num_classes = 10) y_test = np_utils.to_categorical(y_test, num_classes = 10) # Another way to build your neural net # 建立神經(jīng)網(wǎng)絡(luò) # 應(yīng)用了2層的神經(jīng)網(wǎng)絡(luò),前一層的激活函數(shù)用的是relu,后一層的激活函數(shù)用的是softmax #32是輸出的維數(shù) model = Sequential([ Dense(32, input_dim=784), Activation('relu'), Dense(10), Activation('softmax') ]) # Another way to define your optimizer # 優(yōu)化函數(shù) # 優(yōu)化算法用的是RMSprop rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) # We add metrics to get more results you want to see # 不自己定義,直接用內(nèi)置的優(yōu)化器也行,optimizer='rmsprop' #激活模型:接下來(lái)用 model.compile 激勵(lì)神經(jīng)網(wǎng)絡(luò) model.compile( optimizer=rmsprop, loss='categorical_crossentropy', metrics=['accuracy'] ) print('Training------------') # Another way to train the model # 訓(xùn)練模型 # 上一個(gè)程序是用train_on_batch 一批一批的訓(xùn)練 X_train, Y_train # 默認(rèn)的返回值是 cost,每100步輸出一下結(jié)果 # 輸出的樣式與上一個(gè)程序的有所不同,感覺(jué)用model.fit()更清晰明了 # 上一個(gè)程序是Python實(shí)現(xiàn)Keras搭建神經(jīng)網(wǎng)絡(luò)訓(xùn)練回歸模型: # https://blog.csdn.net/weixin_45798684/article/details/106503685 model.fit(X_train, y_train, nb_epoch=2, batch_size=32) print('\nTesting------------') # Evaluate the model with the metrics we defined earlier # 測(cè)試 loss, accuracy = model.evaluate(X_test, y_test) print('test loss:', loss) print('test accuracy:', accuracy)
運(yùn)行結(jié)果:
Using TensorFlow backend. Training------------ Epoch 1/2 32/60000 [..............................] - ETA: 5:03 - loss: 2.4464 - accuracy: 0.0625 864/60000 [..............................] - ETA: 14s - loss: 1.8023 - accuracy: 0.4850 1696/60000 [..............................] - ETA: 9s - loss: 1.5119 - accuracy: 0.6002 2432/60000 [>.............................] - ETA: 7s - loss: 1.3151 - accuracy: 0.6637 3200/60000 [>.............................] - ETA: 6s - loss: 1.1663 - accuracy: 0.7056 3968/60000 [>.............................] - ETA: 5s - loss: 1.0533 - accuracy: 0.7344 4704/60000 [=>............................] - ETA: 5s - loss: 0.9696 - accuracy: 0.7564 5408/60000 [=>............................] - ETA: 5s - loss: 0.9162 - accuracy: 0.7681 6112/60000 [==>...........................] - ETA: 5s - loss: 0.8692 - accuracy: 0.7804 6784/60000 [==>...........................] - ETA: 4s - loss: 0.8225 - accuracy: 0.7933 7424/60000 [==>...........................] - ETA: 4s - loss: 0.7871 - accuracy: 0.8021 8128/60000 [===>..........................] - 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accuracy: 0.9016 56064/60000 [===========================>..] - ETA: 0s - loss: 0.3526 - accuracy: 0.9021 56736/60000 [===========================>..] - ETA: 0s - loss: 0.3514 - accuracy: 0.9026 57376/60000 [===========================>..] - ETA: 0s - loss: 0.3499 - accuracy: 0.9029 58112/60000 [============================>.] - ETA: 0s - loss: 0.3482 - accuracy: 0.9033 58880/60000 [============================>.] - ETA: 0s - loss: 0.3459 - accuracy: 0.9039 59584/60000 [============================>.] - ETA: 0s - loss: 0.3444 - accuracy: 0.9043 60000/60000 [==============================] - 5s 87us/step - loss: 0.3435 - accuracy: 0.9046 Epoch 2/2 32/60000 [..............................] - ETA: 11s - loss: 0.0655 - accuracy: 1.0000 736/60000 [..............................] - ETA: 4s - loss: 0.2135 - accuracy: 0.9389 1408/60000 [..............................] - ETA: 4s - loss: 0.2217 - accuracy: 0.9361 1984/60000 [..............................] - ETA: 4s - loss: 0.2316 - accuracy: 0.9390 2432/60000 [>.............................] - 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ETA: 0s - loss: 0.1956 - accuracy: 0.9438 58560/60000 [============================>.] - ETA: 0s - loss: 0.1951 - accuracy: 0.9440 59360/60000 [============================>.] - ETA: 0s - loss: 0.1947 - accuracy: 0.9440 60000/60000 [==============================] - 5s 76us/step - loss: 0.1946 - accuracy: 0.9440 Testing------------ 32/10000 [..............................] - ETA: 15s 1248/10000 [==>...........................] - ETA: 0s 2656/10000 [======>.......................] - ETA: 0s 4064/10000 [===========>..................] - ETA: 0s 5216/10000 [==============>...............] - ETA: 0s 6464/10000 [==================>...........] - ETA: 0s 7744/10000 [======================>.......] - ETA: 0s 9056/10000 [==========================>...] - ETA: 0s 9984/10000 [============================>.] - ETA: 0s 10000/10000 [==============================] - 0s 47us/step test loss: 0.17407772153392434 test accuracy: 0.9513000249862671
補(bǔ)充知識(shí):Keras 搭建簡(jiǎn)單神經(jīng)網(wǎng)絡(luò):順序模型+回歸問(wèn)題
多層全連接神經(jīng)網(wǎng)絡(luò)
每層神經(jīng)元個(gè)數(shù)、神經(jīng)網(wǎng)絡(luò)層數(shù)、激活函數(shù)等可自由修改
使用不同的損失函數(shù)可適用于其他任務(wù),比如:分類問(wèn)題
這是Keras搭建神經(jīng)網(wǎng)絡(luò)模型最基礎(chǔ)的方法之一,Keras還有其他進(jìn)階的方法,官網(wǎng)給出了一些基本使用方法:Keras官網(wǎng)
# 這里搭建了一個(gè)4層全連接神經(jīng)網(wǎng)絡(luò)(不算輸入層),傳入函數(shù)以及函數(shù)內(nèi)部的參數(shù)均可自由修改 def ann(X, y): ''' X: 輸入的訓(xùn)練集數(shù)據(jù) y: 訓(xùn)練集對(duì)應(yīng)的標(biāo)簽 ''' '''初始化模型''' # 首先定義了一個(gè)順序模型作為框架,然后往這個(gè)框架里面添加網(wǎng)絡(luò)層 # 這是最基礎(chǔ)搭建神經(jīng)網(wǎng)絡(luò)的方法之一 model = Sequential() '''開(kāi)始添加網(wǎng)絡(luò)層''' # Dense表示全連接層,第一層需要我們提供輸入的維度 input_shape # Activation表示每層的激活函數(shù),可以傳入預(yù)定義的激活函數(shù),也可以傳入符合接口規(guī)則的其他高級(jí)激活函數(shù) model.add(Dense(64, input_shape=(X.shape[1],))) model.add(Activation('sigmoid')) model.add(Dense(256)) model.add(Activation('relu')) model.add(Dense(256)) model.add(Activation('tanh')) model.add(Dense(32)) model.add(Activation('tanh')) # 輸出層,輸出的維度大小由具體任務(wù)而定 # 這里是一維輸出的回歸問(wèn)題 model.add(Dense(1)) model.add(Activation('linear')) '''模型編譯''' # optimizer表示優(yōu)化器(可自由選擇),loss表示使用哪一種 model.compile(optimizer='rmsprop', loss='mean_squared_error') # 自定義學(xué)習(xí)率,也可以使用原始的基礎(chǔ)學(xué)習(xí)率 reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=10, verbose=0, mode='auto', min_delta=0.001, cooldown=0, min_lr=0) '''模型訓(xùn)練''' # 這里的模型也可以先從函數(shù)返回后,再進(jìn)行訓(xùn)練 # epochs表示訓(xùn)練的輪數(shù),batch_size表示每次訓(xùn)練的樣本數(shù)量(小批量學(xué)習(xí)),validation_split表示用作驗(yàn)證集的訓(xùn)練數(shù)據(jù)的比例 # callbacks表示回調(diào)函數(shù)的集合,用于模型訓(xùn)練時(shí)查看模型的內(nèi)在狀態(tài)和統(tǒng)計(jì)數(shù)據(jù),相應(yīng)的回調(diào)函數(shù)方法會(huì)在各自的階段被調(diào)用 # verbose表示輸出的詳細(xì)程度,值越大輸出越詳細(xì) model.fit(X, y, epochs=100, batch_size=50, validation_split=0.0, callbacks=[reduce_lr], verbose=0) # 打印模型結(jié)構(gòu) print(model.summary()) return model
下圖是此模型的結(jié)構(gòu)圖,其中下劃線后面的數(shù)字是根據(jù)調(diào)用次數(shù)而定
以上這篇Python實(shí)現(xiàn)Keras搭建神經(jīng)網(wǎng)絡(luò)訓(xùn)練分類模型教程就是小編分享給大家的全部?jī)?nèi)容了,希望能給大家一個(gè)參考,也希望大家多多支持腳本之家。
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