深度学习之利用TensorFlow实现简单的全连接层网络（MNIST数据集）
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深度学习之利用TensorFlow实现简单的全连接层网络（MNIST数据集）
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Tensorflow是一個基于數據流編程（Dataflow Programming）的符號數學系統，被廣泛應用于各類機器學習（Machine Learning）算法的編程實現，其前身是谷歌的神經網絡算法庫DistBelief。MNIST數據集是機器學習領域中非常經典的一個數據集，由60000個訓練樣本和10000個測試樣本組成，每個樣本都是一張28*28像素的灰度手寫數字圖片。
完整代碼（TensorFlow 2.0.0-RC0）如下：
#!/usr/bin/env python import os os.environ["TF_CPP_MIN_LOG_LEVEL"]='2'import tensorflow as tf# 打印TensorFlow版本號 print(tf.__name__, tf.__version__)# 下載MNIST數據集 (x_train_all, y_train_all), (x_test, y_test) = tf.keras.datasets.mnist.load_data()# 預處理數據 [0, 255] => [0.0, 1.0] x_train_all, x_test = x_train_all / 255.0, x_test / 255.0# 分割數據60000條數據集，前5000條為驗證數據集，后55000條為訓練數據集 x_valid, x_train = x_train_all[:5000], x_train_all[5000:] y_valid, y_train = y_train_all[:5000], y_train_all[5000:]# 構建全連接神經網絡模型 model = tf.keras.models.Sequential() model.add(tf.keras.layers.Flatten(input_shape=(28, 28))) model.add(tf.keras.layers.Dense(256, activation='relu')) model.add(tf.keras.layers.Dense(128, activation='relu')) model.add(tf.keras.layers.Dropout(0.2)) model.add(tf.keras.layers.Dense(10, activation='softmax'))# 編譯模型 model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])# 訓練模型 model.fit(x_train, y_train, epochs=10, validation_data=(x_valid, y_valid))# 評估模型 model.evaluate(x_test, y_test)運行結果如下：
tensorflow 2.0.0-rc0 Train on 55000 samples, validate on 5000 samples Epoch 1/1032/55000 [..............................] - ETA: 12:36 - loss: 2.3241 - accuracy: 0.0312736/55000 [..............................] - ETA: 36s - loss: 1.6744 - accuracy: 0.4837 1472/55000 [..............................] - ETA: 19s - loss: 1.2319 - accuracy: 0.62572208/55000 [>.............................] - ETA: 14s - loss: 1.0136 - accuracy: 0.68932944/55000 [>.............................] - ETA: 11s - loss: 0.8826 - accuracy: 0.72793712/55000 [=>............................] - ETA: 9s - loss: 0.7942 - accuracy: 0.7548 4416/55000 [=>............................] - ETA: 8s - loss: 0.7364 - accuracy: 0.77205152/55000 [=>............................] - ETA: 7s - loss: 0.6807 - accuracy: 0.78695920/55000 [==>...........................] - ETA: 7s - loss: 0.6410 - accuracy: 0.80086720/55000 [==>...........................] - ETA: 6s - loss: 0.6054 - accuracy: 0.81297488/55000 [===>..........................] - ETA: 6s - loss: 0.5759 - accuracy: 0.82338288/55000 [===>..........................] - ETA: 5s - loss: 0.5529 - accuracy: 0.83079088/55000 [===>..........................] - ETA: 5s - loss: 0.5291 - accuracy: 0.83799888/55000 [====>.........................] - ETA: 5s - loss: 0.5088 - accuracy: 0.8439 10688/55000 [====>.........................] - ETA: 4s - loss: 0.4923 - accuracy: 0.8502 11488/55000 [=====>........................] - ETA: 4s - loss: 0.4774 - accuracy: 0.8549 12288/55000 [=====>........................] - ETA: 4s - loss: 0.4635 - accuracy: 0.8587 13088/55000 [======>.......................] - ETA: 4s - loss: 0.4506 - accuracy: 0.8632 13856/55000 [======>.......................] - ETA: 4s - loss: 0.4418 - accuracy: 0.8662 14656/55000 [======>.......................] - ETA: 3s - loss: 0.4293 - accuracy: 0.8697 15456/55000 [=======>......................] - ETA: 3s - loss: 0.4201 - accuracy: 0.8727 16256/55000 [=======>......................] - ETA: 3s - loss: 0.4108 - accuracy: 0.8754 16992/55000 [========>.....................] - ETA: 3s - loss: 0.4016 - accuracy: 0.8784 17792/55000 [========>.....................] - ETA: 3s - loss: 0.3926 - accuracy: 0.8808 18592/55000 [=========>....................] - ETA: 3s - loss: 0.3851 - accuracy: 0.8832 19360/55000 [=========>....................] - ETA: 3s - loss: 0.3780 - accuracy: 0.8854 20128/55000 [=========>....................] - ETA: 3s - loss: 0.3704 - accuracy: 0.8875 20928/55000 [==========>...................] - ETA: 2s - loss: 0.3649 - accuracy: 0.8891 21696/55000 [==========>...................] - ETA: 2s - loss: 0.3605 - accuracy: 0.8906 22496/55000 [===========>..................] - ETA: 2s - loss: 0.3541 - accuracy: 0.8926 23264/55000 [===========>..................] - ETA: 2s - loss: 0.3497 - accuracy: 0.8940 24064/55000 [============>.................] - ETA: 2s - loss: 0.3437 - accuracy: 0.8957 24864/55000 [============>.................] - ETA: 2s - loss: 0.3372 - accuracy: 0.8978 25632/55000 [============>.................] - ETA: 2s - loss: 0.3329 - accuracy: 0.8992 26400/55000 [=============>................] - ETA: 2s - loss: 0.3290 - accuracy: 0.9002 27200/55000 [=============>................] - ETA: 2s - loss: 0.3248 - accuracy: 0.9015 28000/55000 [==============>...............] - ETA: 2s - loss: 0.3206 - accuracy: 0.9028 28768/55000 [==============>...............] - ETA: 2s - loss: 0.3176 - accuracy: 0.9035 29536/55000 [===============>..............] - ETA: 2s - loss: 0.3143 - accuracy: 0.9046 30336/55000 [===============>..............] - ETA: 1s - loss: 0.3102 - accuracy: 0.9060 31136/55000 [===============>..............] - ETA: 1s - loss: 0.3064 - accuracy: 0.9071 31936/55000 [================>.............] - ETA: 1s - loss: 0.3033 - accuracy: 0.9082 32736/55000 [================>.............] - ETA: 1s - loss: 0.3001 - accuracy: 0.9092 33504/55000 [=================>............] - ETA: 1s - loss: 0.2968 - accuracy: 0.9102 34176/55000 [=================>............] - ETA: 1s - loss: 0.2932 - accuracy: 0.9114 34848/55000 [==================>...........] - 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ETA: 0s - loss: 0.2469 - accuracy: 0.9256 52832/55000 [===========================>..] - ETA: 0s - loss: 0.2451 - accuracy: 0.9261 53600/55000 [============================>.] - ETA: 0s - loss: 0.2437 - accuracy: 0.9266 54400/55000 [============================>.] - ETA: 0s - loss: 0.2421 - accuracy: 0.9271 55000/55000 [==============================] - 4s 80us/sample - loss: 0.2409 - accuracy: 0.9275 - val_loss: 0.1166 - val_accuracy: 0.9646 Epoch 2/1032/55000 [..............................] - ETA: 5s - loss: 0.1135 - accuracy: 0.9688736/55000 [..............................] - ETA: 3s - loss: 0.1157 - accuracy: 0.97011440/55000 [..............................] - ETA: 3s - loss: 0.1066 - accuracy: 0.97012144/55000 [>.............................] - ETA: 3s - loss: 0.1001 - accuracy: 0.97062880/55000 [>.............................] - ETA: 3s - loss: 0.1073 - accuracy: 0.96743648/55000 [>.............................] - ETA: 3s - loss: 0.1026 - accuracy: 0.96824416/55000 [=>............................] - 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ETA: 0s - loss: 0.0994 - accuracy: 0.9693 50432/55000 [==========================>...] - ETA: 0s - loss: 0.0996 - accuracy: 0.9693 51232/55000 [==========================>...] - ETA: 0s - loss: 0.0999 - accuracy: 0.9692 52032/55000 [===========================>..] - ETA: 0s - loss: 0.1004 - accuracy: 0.9691 52832/55000 [===========================>..] - ETA: 0s - loss: 0.1005 - accuracy: 0.9690 53600/55000 [============================>.] - ETA: 0s - loss: 0.1013 - accuracy: 0.9689 54400/55000 [============================>.] - ETA: 0s - loss: 0.1013 - accuracy: 0.9689 55000/55000 [==============================] - 4s 69us/sample - loss: 0.1013 - accuracy: 0.9688 - val_loss: 0.0875 - val_accuracy: 0.9730 Epoch 3/1032/55000 [..............................] - ETA: 5s - loss: 0.0327 - accuracy: 1.0000800/55000 [..............................] - ETA: 3s - loss: 0.0693 - accuracy: 0.98001600/55000 [..............................] - ETA: 3s - loss: 0.0682 - accuracy: 0.97752304/55000 [>.............................] - 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ETA: 0s - loss: 0.0709 - accuracy: 0.9775 48160/55000 [=========================>....] - ETA: 0s - loss: 0.0710 - accuracy: 0.9775 48960/55000 [=========================>....] - ETA: 0s - loss: 0.0708 - accuracy: 0.9775 49760/55000 [==========================>...] - ETA: 0s - loss: 0.0706 - accuracy: 0.9776 50496/55000 [==========================>...] - ETA: 0s - loss: 0.0705 - accuracy: 0.9776 51264/55000 [==========================>...] - ETA: 0s - loss: 0.0705 - accuracy: 0.9776 52064/55000 [===========================>..] - ETA: 0s - loss: 0.0705 - accuracy: 0.9777 52864/55000 [===========================>..] - ETA: 0s - loss: 0.0710 - accuracy: 0.9777 53664/55000 [============================>.] - ETA: 0s - loss: 0.0711 - accuracy: 0.9776 54464/55000 [============================>.] - ETA: 0s - loss: 0.0709 - accuracy: 0.9777 55000/55000 [==============================] - 4s 69us/sample - loss: 0.0708 - accuracy: 0.9777 - val_loss: 0.0758 - val_accuracy: 0.9790 Epoch 4/1032/55000 [..............................] - ETA: 5s - loss: 0.0532 - accuracy: 0.9688800/55000 [..............................] - ETA: 3s - loss: 0.0421 - accuracy: 0.98251600/55000 [..............................] - ETA: 3s - loss: 0.0350 - accuracy: 0.98752400/55000 [>.............................] - ETA: 3s - loss: 0.0384 - accuracy: 0.98623200/55000 [>.............................] - ETA: 3s - loss: 0.0374 - accuracy: 0.98694000/55000 [=>............................] - ETA: 3s - loss: 0.0381 - accuracy: 0.98754800/55000 [=>............................] - ETA: 3s - loss: 0.0349 - accuracy: 0.98945600/55000 [==>...........................] - ETA: 3s - loss: 0.0364 - accuracy: 0.98876400/55000 [==>...........................] - ETA: 3s - loss: 0.0380 - accuracy: 0.98837200/55000 [==>...........................] - ETA: 3s - loss: 0.0387 - accuracy: 0.98788000/55000 [===>..........................] - ETA: 3s - loss: 0.0390 - accuracy: 0.98728800/55000 [===>..........................] - ETA: 2s - loss: 0.0398 - accuracy: 0.98729600/55000 [====>.........................] - 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accuracy: 0.9823 55000/55000 [==============================] - 4s 68us/sample - loss: 0.0538 - accuracy: 0.9823 - val_loss: 0.0793 - val_accuracy: 0.9792 Epoch 5/1032/55000 [..............................] - ETA: 5s - loss: 0.0048 - accuracy: 1.0000832/55000 [..............................] - ETA: 3s - loss: 0.0287 - accuracy: 0.98921632/55000 [..............................] - ETA: 3s - loss: 0.0304 - accuracy: 0.99022432/55000 [>.............................] - ETA: 3s - loss: 0.0308 - accuracy: 0.98933232/55000 [>.............................] - ETA: 3s - loss: 0.0311 - accuracy: 0.99014032/55000 [=>............................] - ETA: 3s - loss: 0.0370 - accuracy: 0.98834800/55000 [=>............................] - ETA: 3s - loss: 0.0380 - accuracy: 0.98795600/55000 [==>...........................] - ETA: 3s - loss: 0.0363 - accuracy: 0.98826400/55000 [==>...........................] - ETA: 3s - loss: 0.0360 - accuracy: 0.98847200/55000 [==>...........................] - ETA: 3s - 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accuracy: 0.9923 - val_loss: 0.1497 - val_accuracy: 0.9720 Epoch 10/1032/55000 [..............................] - ETA: 5s - loss: 0.0166 - accuracy: 1.0000832/55000 [..............................] - ETA: 3s - loss: 0.0260 - accuracy: 0.99041632/55000 [..............................] - ETA: 3s - loss: 0.0246 - accuracy: 0.99022432/55000 [>.............................] - ETA: 3s - loss: 0.0186 - accuracy: 0.99263232/55000 [>.............................] - ETA: 3s - loss: 0.0182 - accuracy: 0.99354000/55000 [=>............................] - ETA: 3s - loss: 0.0191 - accuracy: 0.99304768/55000 [=>............................] - ETA: 3s - loss: 0.0233 - accuracy: 0.99225440/55000 [=>............................] - ETA: 3s - loss: 0.0247 - accuracy: 0.99176240/55000 [==>...........................] - ETA: 3s - loss: 0.0249 - accuracy: 0.99127040/55000 [==>...........................] - ETA: 3s - loss: 0.0235 - accuracy: 0.99167840/55000 [===>..........................] - ETA: 3s - loss: 0.0241 - 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ETA: 0s - loss: 0.0225 - accuracy: 0.9928 54752/55000 [============================>.] - ETA: 0s - loss: 0.0223 - accuracy: 0.9928 55000/55000 [==============================] - 4s 68us/sample - loss: 0.0223 - accuracy: 0.9928 - val_loss: 0.0845 - val_accuracy: 0.978610000/10000 [==============================] - 0s 31us/sample - loss: 0.0463 - accuracy: 0.9801 [Finished in 42.1s]?
總結
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