keras實現手寫體數字識別功能的CNN
阿新 • • 發佈：2019-02-02
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資料為框架自帶的數字手寫體
中間的數值為灰度值，注意灰度值和RGB值不是一個概念，灰度值是介於白和黑之間的值，表示範圍0-255.可以理解成黑的程度。所以圖片只需要一層就OK。
資料集包含60000張圖片，大小均為28x28.
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程式碼如下:
# -*- coding: utf-8 -*- """ Created on Fri Nov 9 15:50:33 2018 """ #from __future__ import print_function import keras from keras.datasets import mnist #自帶手寫資料集 from keras.models import Sequential #序貫模型 from keras.layers import Dense #全連線層 from keras.layers import Dropout #隨機失活層 from keras.layers import Flatten #展平層，從卷積層到全連線層必須展平 from keras.layers import Conv2D #二維卷積層，多用於影象 from keras.layers import MaxPooling2D #最大值池化 from keras import backend as k batch_size = 128 #一批訓練樣本128張圖片 num_classes = 10 #有10個類別 epochs = 12 #一共迭代12輪 img_rows, img_cols = 28, 28 #圖片寬，高。 (x_train, y_train),(x_test, y_test) = mnist.load_data() #後端可以跑在不同的模型上，比如tensorflow， 不同的工具庫對資料的組織形式不同。 #======================================================================= if k.image_data_format() == 'channels_first': x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) #reshape()不明白可以參考博文：https://blog.csdn.net/u010916338/article/details/84066369 x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) #================================================================================= x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) model = Sequential() #序貫模型，一個架子 model.add(Conv2D(32, kernel_size=(3,3), activation='relu',input_shape=input_shape)) #卷積層， 32個神經元， 卷積核3x3 model.add(Conv2D(64, (3,3), activation='relu')) #卷積層， 64個神經元， 卷積核3x3 model.add(MaxPooling2D(pool_size=(2, 2))) #池化層 model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) #全連線層， 128神經元 model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) #編譯，損失函式， 優化函式， 評價標註是準確率 model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy']) #執行 ， verbose步長 model.fit(x_train, y_train, batch_size= batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) stop = time.time() print(str(stop-start) + "秒")
- 執行效果
Train on 60000 samples, validate on 10000 samples Epoch 1/12 60000/60000 [==============================] - 87s 1ms/step - loss: 0.2595 - acc: 0.9187 - val_loss: 0.0619 - val_acc: 0.9802 Epoch 2/12 60000/60000 [==============================] - 82s 1ms/step - loss: 0.0928 - acc: 0.9721 - val_loss: 0.0401 - val_acc: 0.9859 Epoch 3/12 60000/60000 [==============================] - 80s 1ms/step - loss: 0.0692 - acc: 0.9795 - val_loss: 0.0336 - val_acc: 0.9883 Epoch 4/12 60000/60000 [==============================] - 81s 1ms/step - loss: 0.0550 - acc: 0.9840 - val_loss: 0.0325 - val_acc: 0.9886 Epoch 5/12 60000/60000 [==============================] - 82s 1ms/step - loss: 0.0475 - acc: 0.9854 - val_loss: 0.0336 - val_acc: 0.9877 Epoch 6/12 60000/60000 [==============================] - 82s 1ms/step - loss: 0.0434 - acc: 0.9870 - val_loss: 0.0292 - val_acc: 0.9902 Epoch 7/12 60000/60000 [==============================] - 86s 1ms/step - loss: 0.0382 - acc: 0.9889 - val_loss: 0.0272 - val_acc: 0.9906 Epoch 8/12 60000/60000 [==============================] - 86s 1ms/step - loss: 0.0346 - acc: 0.9896 - val_loss: 0.0257 - val_acc: 0.9916 Epoch 9/12 60000/60000 [==============================] - 85s 1ms/step - loss: 0.0310 - acc: 0.9905 - val_loss: 0.0283 - val_acc: 0.9917 Epoch 10/12 60000/60000 [==============================] - 88s 1ms/step - loss: 0.0307 - acc: 0.9905 - val_loss: 0.0277 - val_acc: 0.9906 Epoch 11/12 60000/60000 [==============================] - 86s 1ms/step - loss: 0.0276 - acc: 0.9917 - val_loss: 0.0253 - val_acc: 0.9919 Epoch 12/12 60000/60000 [==============================] - 81s 1ms/step - loss: 0.0272 - acc: 0.9921 - val_loss: 0.0266 - val_acc: 0.9919 Test loss: 0.026634473627798978 Test accuracy: 0.9919 1011.8610150814056秒