##### # prg8 (完整的) #prg4 顯示多張訓練資料的圖片與值 from keras.datasets import mnist #讀取MNIST資料集 (train_feature, train_label), (test_feature, test_label) = mnist.load_data() # 查看訓練資料 print(len(train_feature), len(train_label)) #60000 60000 # 查看維度 print(train_feature.shape, train_label.shape) # (60000, 28, 28) (60000,) # 顯示圖片與值 import matplotlib.pyplot as plt def show_image(image): fig = plt.gcf() fig.set_size_inches(2,2) #數字圖片大小 plt.imshow(image, cmap = 'binary') #黑白灰階顯示 plt.show() # 顯示多張資料副程式,最多顯示 25張 show_image(train_feature[0]) #顯示訓練資料第1個數字 print(train_label[0]) #顯示第1個訓練資料圖片真實值 # 顯示多張圖片與值(最多25張) def show_images_labels_predictions(images,labels, predictions,start_id,num=10): plt.gcf().set_size_inches(12, 14) if num>25: num=25 for i in range(0, num): ax=plt.subplot(5,5, 1+i) #顯示黑白圖片 ax.imshow(images[start_id], cmap='binary') # 有 AI 預測結果資料, 才在標題顯示預測結果 if( len(predictions) > 0 ) : title = 'ai = ' + str(predictions[i]) # 預測正確顯示(o), 錯誤顯示(x) title += (' (o)' if predictions[i]==labels[i] else ' (x)') title += '\nlabel = ' + str(labels[i]) # 沒有 AI 預測結果資料, 只在標題顯示真實數值 else : title = 'label = ' + str(labels[i]) # X, Y 軸不顯示刻度 ax.set_title(title,fontsize=12) ax.set_xticks([]);ax.set_yticks([]) start_id+=1 plt.show() # 以reshape()函數將28*28的數字圖片轉換成784個數字的一維向量,再以astype將每個數字都轉換為float數字 # 以reshape()函數將28*28的數字圖片轉換成784個數字的一維向量,再以astype將每個數字都轉換為float數字 train_feature_vector = train_feature.reshape(len(train_feature),784).astype('float32') test_feature_vector = test_feature.reshape(len(test_feature),784).astype('float32') # ----------------------------------------------------- #查看資料 print(train_feature_vector.shape, test_feature_vector.shape) # 顯示第1筆image資料內容。顯示0~255的浮點數。數字代表圖片中美一個點的灰階值 print(train_feature_vector[0]) # ----------------------------------------------------- # Image標準化 train_feature_normalize = train_feature_vector/255 test_feature_normalize = test_feature_vector/255 #顯示第1筆的image正規化 print(train_feature_normalize[0]) # ----------------------------------------------------- # Prg7 # One-Hot Encoding import numpy as np from keras.utils import np_utils np.random.seed(10) train_label_onehot = np_utils.to_categorical(train_label) test_label_onehot = np_utils.to_categorical(test_label) print(train_label_onehot[0:5]) # ----------------------------------------------------- # Prg8 #建立模型 import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense #建立模型 model = Sequential() #輸入層:784, 隱藏層:256,輸出層:10 model.add(Dense(units=256, input_dim=784, kernel_initializer='normal', activation='relu')) model.add(Dense(units=10, kernel_initializer='normal', activation='softmax')) #定義訓練方式 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) #以(train_feature_normalize,train_label_onehot)資料訓練, #訓練資料保留 20% 作驗證,訓練10次、每批次讀取200筆資料,顯示簡易訓練過程 train_history =model.fit(x=train_feature_normalize, y=train_label_onehot,validation_split=0.2, epochs=20, batch_size=200,verbose=2) #預測 prediction=model.predict_classes(test_feature_normalize) #顯示圖像、預測值、真實值 show_images_labels_predictions(test_feature,test_label,prediction,0)