我就廢話(huà)不多說(shuō)了，大家還是直接看代碼吧~

注釋講解版：

# Classifier exampleimport numpy as np# for reproducibilitynp.random.seed(1337)# from keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Dense, Activationfrom keras.optimizers import RMSprop# 程序中用到的數(shù)據(jù)是經(jīng)典的手寫(xiě)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) / 255X_test = X_test.reshape(X_test.shape[0], -1) / 255# 分類(lèi)標(biāo)簽編碼# 將y轉(zhuǎn)化為one-hot vectory_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RMSproprmsprop = 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/106503685model.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 [===>..........................] - ETA: 4s - loss: 0.7546 - accuracy: 0.8099 8960/60000 [===>..........................] - ETA: 4s - loss: 0.7196 - accuracy: 0.8183 9568/60000 [===>..........................] - ETA: 4s - loss: 0.6987 - accuracy: 0.823010144/60000 [====>.........................] - ETA: 4s - loss: 0.6812 - accuracy: 0.826210784/60000 [====>.........................] - ETA: 4s - loss: 0.6640 - accuracy: 0.829711456/60000 [====>.........................] - ETA: 4s - loss: 0.6462 - accuracy: 0.832912128/60000 [=====>........................] - ETA: 4s - loss: 0.6297 - accuracy: 0.836612704/60000 [=====>........................] - ETA: 4s - loss: 0.6156 - accuracy: 0.840513408/60000 [=====>........................] - ETA: 3s - loss: 0.6009 - accuracy: 0.843014112/60000 [======>.......................] - ETA: 3s - loss: 0.5888 - accuracy: 0.845714816/60000 [======>.......................] - ETA: 3s - loss: 0.5772 - accuracy: 0.848715488/60000 [======>.......................] - 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ETA: 0s - loss: 0.3499 - accuracy: 0.902958112/60000 [============================>.] - ETA: 0s - loss: 0.3482 - accuracy: 0.903358880/60000 [============================>.] - ETA: 0s - loss: 0.3459 - accuracy: 0.903959584/60000 [============================>.] - ETA: 0s - loss: 0.3444 - accuracy: 0.904360000/60000 [==============================] - 5s 87us/step - loss: 0.3435 - accuracy: 0.9046Epoch 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 [>.............................] - ETA: 4s - loss: 0.2280 - accuracy: 0.9379 3040/60000 [>.............................] - ETA: 4s - loss: 0.2374 - accuracy: 0.9368 3808/60000 [>.............................] - ETA: 4s - loss: 0.2251 - 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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: 0s10000/10000 [==============================] - 0s 47us/steptest loss: 0.17407772153392434test 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(lèi)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(lèi)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好吧啦網(wǎng)。