2019.09.15】基于TensorFlow2.0，使用Keras + mnist（手写数字）数据集进行深度学习实战，数据归一化与不归一化结果对比

ゝ一世哀愁。 2024-04-18 23:59 19阅读 0赞

# 基于TensorFlow2.0，使用Keras + mnist 数据集进行深度学习实战 #

import pandas as pd 
    import numpy as np
    import tensorflow as tf
    import matplotlib.pyplot as plt
    from tensorflow.keras import Sequential
    from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout
    from tensorflow.keras import datasets
    %matplotlib inline

# 1、加载数据
    (train_images,train_labels),(test_images,test_labels) = datasets.mnist.load_data()

print(train_images.shape)
    print(train_labels.shape)
    print(test_images.shape)
    print(test_labels.shape)

(60000, 28, 28)
    (60000,)
    (10000, 28, 28)
    (10000,)

plt.imshow(train_images[1009]),
    train_labels[1009]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70]

可以看到训练数据集由60000张28\*28的图片组成,Label就是每个图像对应的手写字体的数字

(train_images,train_labels),(test_images,test_labels) = datasets.mnist.load_data()
    
    # 2、构建模型(顺序)
    model = Sequential([Flatten(input_shape=(28,28)), # （60000， 28. 28）-----》（60000， 28*28）
                       Dense(64, activation='relu'), 
                       Dense(10, activation='softmax')])
    # 3、编译模型
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    # 4、拟合模型
    history = model.fit(train_images, train_labels, 
                        epochs=50, batch_size=512,
                        validation_data=(test_images, test_labels))

Train on 60000 samples, validate on 10000 samples
    Epoch 1/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 12.8318 - acc: 0.7595 - val_loss: 3.0141 - val_acc: 0.8560
    Epoch 2/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 1.9891 - acc: 0.8371 - val_loss: 1.2640 - val_acc: 0.8252
    Epoch 3/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.9736 - acc: 0.8264 - val_loss: 0.9058 - val_acc: 0.8334
    Epoch 4/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.7063 - acc: 0.8540 - val_loss: 0.7601 - val_acc: 0.8576
    Epoch 5/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.5713 - acc: 0.8728 - val_loss: 0.6955 - val_acc: 0.8706
    Epoch 6/50
    60000/60000 [==============================] - 0s 8us/sample - loss: 0.4809 - acc: 0.8869 - val_loss: 0.6461 - val_acc: 0.8801
    Epoch 7/50
    60000/60000 [==============================] - 0s 8us/sample - loss: 0.4170 - acc: 0.9004 - val_loss: 0.6040 - val_acc: 0.8920
    Epoch 8/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.3727 - acc: 0.9104 - val_loss: 0.5777 - val_acc: 0.8954
    Epoch 9/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.3327 - acc: 0.9165 - val_loss: 0.5469 - val_acc: 0.9010
    Epoch 10/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.2992 - acc: 0.9240 - val_loss: 0.5124 - val_acc: 0.9074
    Epoch 11/50
    60000/60000 [==============================] - 0s 8us/sample - loss: 0.2726 - acc: 0.9290 - val_loss: 0.5123 - val_acc: 0.9093
    Epoch 12/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.2519 - acc: 0.9325 - val_loss: 0.4877 - val_acc: 0.9111
    Epoch 13/50
    60000/60000 [==============================] - 0s 8us/sample - loss: 0.2308 - acc: 0.9369 - val_loss: 0.4719 - val_acc: 0.9158
    Epoch 14/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.2192 - acc: 0.9388 - val_loss: 0.4744 - val_acc: 0.9161
    Epoch 15/50
    60000/60000 [==============================] - 0s 8us/sample - loss: 0.2034 - acc: 0.9435 - val_loss: 0.4513 - val_acc: 0.9173
    Epoch 16/50
    60000/60000 [==============================] - 0s 8us/sample - loss: 0.1920 - acc: 0.9457 - val_loss: 0.4389 - val_acc: 0.9219
    Epoch 17/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1829 - acc: 0.9478 - val_loss: 0.4270 - val_acc: 0.9193
    Epoch 18/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1739 - acc: 0.9496 - val_loss: 0.4385 - val_acc: 0.9241
    Epoch 19/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.1632 - acc: 0.9525 - val_loss: 0.4111 - val_acc: 0.9294
    Epoch 20/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1543 - acc: 0.9547 - val_loss: 0.4115 - val_acc: 0.9278
    Epoch 21/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.1484 - acc: 0.9559 - val_loss: 0.4159 - val_acc: 0.9269
    Epoch 22/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.1463 - acc: 0.9566 - val_loss: 0.4046 - val_acc: 0.9287
    Epoch 23/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1372 - acc: 0.9586 - val_loss: 0.3985 - val_acc: 0.9327
    Epoch 24/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.1321 - acc: 0.9601 - val_loss: 0.4131 - val_acc: 0.9304
    Epoch 25/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.1234 - acc: 0.9622 - val_loss: 0.3863 - val_acc: 0.9330
    Epoch 26/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.1195 - acc: 0.9631 - val_loss: 0.3987 - val_acc: 0.9348
    Epoch 27/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1148 - acc: 0.9650 - val_loss: 0.4004 - val_acc: 0.9365
    Epoch 28/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1159 - acc: 0.9643 - val_loss: 0.3818 - val_acc: 0.9371
    Epoch 29/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1149 - acc: 0.9644 - val_loss: 0.3950 - val_acc: 0.9376
    Epoch 30/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1090 - acc: 0.9666 - val_loss: 0.3915 - val_acc: 0.9393
    Epoch 31/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1102 - acc: 0.9662 - val_loss: 0.3970 - val_acc: 0.9374
    Epoch 32/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.1022 - acc: 0.9679 - val_loss: 0.3845 - val_acc: 0.9425
    Epoch 33/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0977 - acc: 0.9691 - val_loss: 0.3771 - val_acc: 0.9397
    Epoch 34/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0996 - acc: 0.9681 - val_loss: 0.3786 - val_acc: 0.9407
    Epoch 35/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.1004 - acc: 0.9683 - val_loss: 0.3778 - val_acc: 0.9384
    Epoch 36/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0987 - acc: 0.9695 - val_loss: 0.3819 - val_acc: 0.9417
    Epoch 37/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0971 - acc: 0.9691 - val_loss: 0.3689 - val_acc: 0.9421
    Epoch 38/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0890 - acc: 0.9714 - val_loss: 0.3723 - val_acc: 0.9415
    Epoch 39/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0880 - acc: 0.9731 - val_loss: 0.3725 - val_acc: 0.9411
    Epoch 40/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0852 - acc: 0.9729 - val_loss: 0.3966 - val_acc: 0.9406
    Epoch 41/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0862 - acc: 0.9726 - val_loss: 0.3680 - val_acc: 0.9433
    Epoch 42/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0880 - acc: 0.9732 - val_loss: 0.3706 - val_acc: 0.9428
    Epoch 43/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0884 - acc: 0.9723 - val_loss: 0.3719 - val_acc: 0.9439
    Epoch 44/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0884 - acc: 0.9725 - val_loss: 0.3815 - val_acc: 0.9434
    Epoch 45/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0837 - acc: 0.9736 - val_loss: 0.3519 - val_acc: 0.9459
    Epoch 46/50
    60000/60000 [==============================] - 1s 9us/sample - loss: 0.0853 - acc: 0.9739 - val_loss: 0.3639 - val_acc: 0.9450
    Epoch 47/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0776 - acc: 0.9755 - val_loss: 0.3533 - val_acc: 0.9458
    Epoch 48/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0721 - acc: 0.9769 - val_loss: 0.3485 - val_acc: 0.9467
    Epoch 49/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0787 - acc: 0.9756 - val_loss: 0.3649 - val_acc: 0.9479
    Epoch 50/50
    60000/60000 [==============================] - 1s 8us/sample - loss: 0.0778 - acc: 0.9765 - val_loss: 0.3624 - val_acc: 0.9468

# 5、评估模型
    model.evaluate(test_images, test_labels)

10000/10000 [==============================] - 1s 52us/sample - loss: 0.3624 - acc: 0.9468
    
    
    
    
    
    [0.36239219328409233, 0.9468]

# 结果可视化
    acc = history.history.get('acc')
    val_acc = history.history.get('val_acc')
    loss = history.history.get('loss')
    val_loss = history.history.get('val_loss')
    
    epochs = range(1, len(acc)+1)
    plt.figure(figsize=(8,4),dpi=100)
    plt.subplot(1, 2, 1)
    plt.plot(epochs, acc, 'bo', label='Traing acc')
    plt.plot(epochs, val_acc, 'r', label='Test acc')
    plt.legend()
    
    plt.subplot(1, 2, 2)
    plt.plot(epochs, loss, 'bo', label='Traing loss')
    plt.plot(epochs, val_loss, 'r', label='Test val_loss')
    plt.legend()

<matplotlib.legend.Legend at 0x1529ee151d0>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 1]

测试准确率为 0.9468

# 增大网络容量，添加隐含层 #

(train_images,train_labels),(test_images,test_labels) = datasets.mnist.load_data()
    
    # 2、构建模型(顺序)
    model = Sequential([Flatten(input_shape=(28,28)), # （60000， 28. 28）-----》（60000， 28*28）
                       Dense(64, activation='relu'), 
                        Dense(64, activation='relu'), 
                        Dense(64, activation='relu'), 
                       Dense(10, activation='softmax')])
    # 3、编译模型
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    # 4、拟合模型
    history = model.fit(train_images, train_labels, 
                        epochs=50, batch_size=512,
                        validation_data=(test_images, test_labels))
    
    # 结果可视化
    acc = history.history.get('acc')
    val_acc = history.history.get('val_acc')
    loss = history.history.get('loss')
    val_loss = history.history.get('val_loss')
    
    epochs = range(1, len(acc)+1)
    plt.figure(figsize=(8,4),dpi=100)
    plt.subplot(1, 2, 1)
    plt.plot(epochs, acc, 'bo', label='Traing acc')
    plt.plot(epochs, val_acc, 'r', label='Test acc')
    plt.legend()
    
    plt.subplot(1, 2, 2)
    plt.plot(epochs, loss, 'bo', label='Traing loss')
    plt.plot(epochs, val_loss, 'r', label='Test val_loss')
    plt.legend()

Train on 60000 samples, validate on 10000 samples
    Epoch 1/50
    60000/60000 [==============================] - 1s 13us/sample - loss: 4.8376 - acc: 0.7013 - val_loss: 0.9137 - val_acc: 0.8342
    Epoch 2/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.6739 - acc: 0.8600 - val_loss: 0.5719 - val_acc: 0.8735
    Epoch 3/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.4421 - acc: 0.8936 - val_loss: 0.4463 - val_acc: 0.8954
    Epoch 4/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.3424 - acc: 0.9126 - val_loss: 0.3728 - val_acc: 0.9125
    Epoch 5/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.2755 - acc: 0.9263 - val_loss: 0.3248 - val_acc: 0.9195
    Epoch 6/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.2321 - acc: 0.9356 - val_loss: 0.3045 - val_acc: 0.9226
    Epoch 7/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.2043 - acc: 0.9421 - val_loss: 0.2869 - val_acc: 0.9265
    Epoch 8/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1824 - acc: 0.9476 - val_loss: 0.2783 - val_acc: 0.9297
    Epoch 9/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1636 - acc: 0.9525 - val_loss: 0.2641 - val_acc: 0.9351
    Epoch 10/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1486 - acc: 0.9558 - val_loss: 0.2649 - val_acc: 0.9328
    Epoch 11/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1401 - acc: 0.9578 - val_loss: 0.2570 - val_acc: 0.9372
    Epoch 12/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1275 - acc: 0.9622 - val_loss: 0.2429 - val_acc: 0.9396
    Epoch 13/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1212 - acc: 0.9638 - val_loss: 0.2492 - val_acc: 0.9401
    Epoch 14/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1134 - acc: 0.9658 - val_loss: 0.2414 - val_acc: 0.9428
    Epoch 15/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.1035 - acc: 0.9679 - val_loss: 0.2363 - val_acc: 0.9447
    Epoch 16/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0968 - acc: 0.9702 - val_loss: 0.2293 - val_acc: 0.9472
    Epoch 17/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0907 - acc: 0.9724 - val_loss: 0.2332 - val_acc: 0.9476
    Epoch 18/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0881 - acc: 0.9725 - val_loss: 0.2350 - val_acc: 0.9468
    Epoch 19/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0812 - acc: 0.9740 - val_loss: 0.2354 - val_acc: 0.9461
    Epoch 20/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0784 - acc: 0.9758 - val_loss: 0.2341 - val_acc: 0.9471
    Epoch 21/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0711 - acc: 0.9777 - val_loss: 0.2248 - val_acc: 0.9489
    Epoch 22/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0634 - acc: 0.9797 - val_loss: 0.2339 - val_acc: 0.9511
    Epoch 23/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0631 - acc: 0.9804 - val_loss: 0.2341 - val_acc: 0.9487
    Epoch 24/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0642 - acc: 0.9793 - val_loss: 0.2302 - val_acc: 0.9516
    Epoch 25/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0579 - acc: 0.9813 - val_loss: 0.2313 - val_acc: 0.9528
    Epoch 26/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0584 - acc: 0.9807 - val_loss: 0.2342 - val_acc: 0.9513
    Epoch 27/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0565 - acc: 0.9813 - val_loss: 0.2348 - val_acc: 0.9490
    Epoch 28/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0578 - acc: 0.9807 - val_loss: 0.2454 - val_acc: 0.9529
    Epoch 29/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0517 - acc: 0.9822 - val_loss: 0.2413 - val_acc: 0.9511
    Epoch 30/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0500 - acc: 0.9830 - val_loss: 0.2522 - val_acc: 0.9506
    Epoch 31/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0551 - acc: 0.9824 - val_loss: 0.2253 - val_acc: 0.9555
    Epoch 32/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0414 - acc: 0.9864 - val_loss: 0.2535 - val_acc: 0.9532
    Epoch 33/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0426 - acc: 0.9858 - val_loss: 0.2353 - val_acc: 0.9539
    Epoch 34/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0392 - acc: 0.9870 - val_loss: 0.2474 - val_acc: 0.9516
    Epoch 35/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0417 - acc: 0.9861 - val_loss: 0.2578 - val_acc: 0.9546
    Epoch 36/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0360 - acc: 0.9879 - val_loss: 0.2543 - val_acc: 0.9532
    Epoch 37/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0389 - acc: 0.9869 - val_loss: 0.2431 - val_acc: 0.9578
    Epoch 38/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0471 - acc: 0.9842 - val_loss: 0.2825 - val_acc: 0.9533
    Epoch 39/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0535 - acc: 0.9834 - val_loss: 0.2601 - val_acc: 0.9545
    Epoch 40/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0438 - acc: 0.9851 - val_loss: 0.2737 - val_acc: 0.9545
    Epoch 41/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0431 - acc: 0.9857 - val_loss: 0.2576 - val_acc: 0.9553
    Epoch 42/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0365 - acc: 0.9875 - val_loss: 0.2803 - val_acc: 0.9543
    Epoch 43/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0368 - acc: 0.9882 - val_loss: 0.2766 - val_acc: 0.9553
    Epoch 44/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0526 - acc: 0.9838 - val_loss: 0.2644 - val_acc: 0.9582
    Epoch 45/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0351 - acc: 0.9886 - val_loss: 0.2738 - val_acc: 0.9558
    Epoch 46/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0321 - acc: 0.9893 - val_loss: 0.2816 - val_acc: 0.9571
    Epoch 47/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0304 - acc: 0.9900 - val_loss: 0.2606 - val_acc: 0.9603
    Epoch 48/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0286 - acc: 0.9906 - val_loss: 0.2603 - val_acc: 0.9602
    Epoch 49/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0236 - acc: 0.9918 - val_loss: 0.2772 - val_acc: 0.9595
    Epoch 50/50
    60000/60000 [==============================] - 1s 10us/sample - loss: 0.0271 - acc: 0.9909 - val_loss: 0.2683 - val_acc: 0.9610
    
    
    
    
    
    <matplotlib.legend.Legend at 0x152a3f6c160>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 2]

测试准确率为 0.9610

#对数据进行编码后训练
    
    (train_images,train_labels),(test_images,test_labels) = datasets.mnist.load_data()
    # 训练图像保存在一个uint8 类型的数组中，其形状为(60000, 28, 28)，取值区间为[0, 255]。我们需要将其变换为一个float32 数组，其形状为(60000, 28 * 28)，取值范围为0~1。
    train_images = train_images.reshape(60000, 28*28).astype('float32') / 255
    test_images = test_images.reshape(10000, 28*28).astype('float32') / 255
    
    # 对标签进行分类编码
    train_labels = tf.keras.utils.to_categorical(train_labels)
    test_labels = tf.keras.utils.to_categorical(test_labels)
    
    # 2、构建模型()
    model = Sequential([Dense(512, activation='relu', input_shape=(28*28,)),
                       Dense(64, activation='relu'),
                       Dense(10, activation='softmax')])
    
    # 3、编译模型
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['acc'])
    
    # 4、拟合模型
    history = model.fit(train_images, train_labels, 
                        epochs=50, batch_size=512, 
                        validation_data=(test_images, test_labels))

Train on 60000 samples, validate on 10000 samples
    Epoch 1/50
    60000/60000 [==============================] - 1s 15us/sample - loss: 0.3874 - acc: 0.8941 - val_loss: 0.1747 - val_acc: 0.9483
    Epoch 2/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.1482 - acc: 0.9561 - val_loss: 0.1190 - val_acc: 0.9656
    Epoch 3/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0980 - acc: 0.9714 - val_loss: 0.1008 - val_acc: 0.9671
    Epoch 4/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0736 - acc: 0.9777 - val_loss: 0.0878 - val_acc: 0.9725
    Epoch 5/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0549 - acc: 0.9841 - val_loss: 0.0724 - val_acc: 0.9778
    Epoch 6/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0408 - acc: 0.9886 - val_loss: 0.0810 - val_acc: 0.9753
    Epoch 7/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0324 - acc: 0.9909 - val_loss: 0.0646 - val_acc: 0.9799
    Epoch 8/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0261 - acc: 0.9927 - val_loss: 0.0705 - val_acc: 0.9780
    Epoch 9/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0212 - acc: 0.9946 - val_loss: 0.0610 - val_acc: 0.9812
    Epoch 10/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0161 - acc: 0.9962 - val_loss: 0.0635 - val_acc: 0.9816
    Epoch 11/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0107 - acc: 0.9981 - val_loss: 0.0654 - val_acc: 0.9804
    Epoch 12/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0094 - acc: 0.9984 - val_loss: 0.0646 - val_acc: 0.9820
    Epoch 13/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0079 - acc: 0.9985 - val_loss: 0.0643 - val_acc: 0.9824
    Epoch 14/50
    60000/60000 [==============================] - 1s 12us/sample - loss: 0.0049 - acc: 0.9995 - val_loss: 0.0682 - val_acc: 0.9815
    Epoch 15/50
    60000/60000 [==============================] - 1s 12us/sample - loss: 0.0045 - acc: 0.9993 - val_loss: 0.0691 - val_acc: 0.9817
    Epoch 16/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0038 - acc: 0.9996 - val_loss: 0.0681 - val_acc: 0.9830
    Epoch 17/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0029 - acc: 0.9998 - val_loss: 0.0647 - val_acc: 0.9825
    Epoch 18/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0017 - acc: 0.9999 - val_loss: 0.0668 - val_acc: 0.9828
    Epoch 19/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0672 - val_acc: 0.9827
    Epoch 20/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0655 - val_acc: 0.9837
    Epoch 21/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 8.7679e-04 - acc: 1.0000 - val_loss: 0.0693 - val_acc: 0.9832
    Epoch 22/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 7.6021e-04 - acc: 1.0000 - val_loss: 0.0679 - val_acc: 0.9839
    Epoch 23/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 6.5421e-04 - acc: 1.0000 - val_loss: 0.0692 - val_acc: 0.9834
    Epoch 24/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 5.7870e-04 - acc: 1.0000 - val_loss: 0.0712 - val_acc: 0.9832
    Epoch 25/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 4.9977e-04 - acc: 1.0000 - val_loss: 0.0692 - val_acc: 0.9835
    Epoch 26/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 4.4865e-04 - acc: 1.0000 - val_loss: 0.0719 - val_acc: 0.9830
    Epoch 27/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 3.9904e-04 - acc: 1.0000 - val_loss: 0.0729 - val_acc: 0.9833
    Epoch 28/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 3.6224e-04 - acc: 1.0000 - val_loss: 0.0726 - val_acc: 0.9832
    Epoch 29/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 3.4489e-04 - acc: 1.0000 - val_loss: 0.0732 - val_acc: 0.9827
    Epoch 30/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 2.9672e-04 - acc: 1.0000 - val_loss: 0.0731 - val_acc: 0.9829
    Epoch 31/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 2.6151e-04 - acc: 1.0000 - val_loss: 0.0748 - val_acc: 0.9829
    Epoch 32/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 2.3749e-04 - acc: 1.0000 - val_loss: 0.0746 - val_acc: 0.9830
    Epoch 33/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 2.1668e-04 - acc: 1.0000 - val_loss: 0.0751 - val_acc: 0.9833
    Epoch 34/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.9664e-04 - acc: 1.0000 - val_loss: 0.0762 - val_acc: 0.9835
    Epoch 35/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.7997e-04 - acc: 1.0000 - val_loss: 0.0773 - val_acc: 0.9829
    Epoch 36/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.6535e-04 - acc: 1.0000 - val_loss: 0.0772 - val_acc: 0.9829
    Epoch 37/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.4918e-04 - acc: 1.0000 - val_loss: 0.0773 - val_acc: 0.9826
    Epoch 38/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.3834e-04 - acc: 1.0000 - val_loss: 0.0788 - val_acc: 0.9826
    Epoch 39/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.2273e-04 - acc: 1.0000 - val_loss: 0.0790 - val_acc: 0.9828
    Epoch 40/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.1330e-04 - acc: 1.0000 - val_loss: 0.0794 - val_acc: 0.9830
    Epoch 41/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 1.0385e-04 - acc: 1.0000 - val_loss: 0.0804 - val_acc: 0.9829
    Epoch 42/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 9.3831e-05 - acc: 1.0000 - val_loss: 0.0803 - val_acc: 0.9825
    Epoch 43/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 8.6219e-05 - acc: 1.0000 - val_loss: 0.0810 - val_acc: 0.9830
    Epoch 44/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 7.9767e-05 - acc: 1.0000 - val_loss: 0.0813 - val_acc: 0.9833
    Epoch 45/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 7.2581e-05 - acc: 1.0000 - val_loss: 0.0825 - val_acc: 0.9831
    Epoch 46/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 6.8911e-05 - acc: 1.0000 - val_loss: 0.0829 - val_acc: 0.9828
    Epoch 47/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 6.0209e-05 - acc: 1.0000 - val_loss: 0.0841 - val_acc: 0.9833
    Epoch 48/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 5.6985e-05 - acc: 1.0000 - val_loss: 0.0848 - val_acc: 0.9826
    Epoch 49/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 5.1581e-05 - acc: 1.0000 - val_loss: 0.0848 - val_acc: 0.9827
    Epoch 50/50
    60000/60000 [==============================] - 1s 11us/sample - loss: 4.8771e-05 - acc: 1.0000 - val_loss: 0.0851 - val_acc: 0.9824

# 5、评估模型
    # model.predict(test_images)
    # model.predict_class(test_images)
    model.evaluate(test_images, test_labels)

10000/10000 [==============================] - 1s 56us/sample - loss: 0.0851 - acc: 0.9824
    
    
    
    
    
    [0.08507671587202557, 0.9824]

<matplotlib.legend.Legend at 0x152a0df74e0>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 3]

可以看出，准确率还是可以，不过测试数据比训练数据的结果还是要差一些，可能是因为有点过拟合了，我们只是一个3层的模型呀，增加一个Dropout层看看结果如何

# 2、构建模型
    model = Sequential([Dense(512, activation='relu', input_shape=(28*28,)),
                       Dense(256, activation='relu'),
                        Dropout(0.5),
                       Dense(10, activation='softmax')])
    # 3、编译模型
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    # 4、拟合模型
    history = model.fit(train_images, train_labels, 
                        epochs=10, batch_size=128,
                       validation_data=(test_images, test_labels))

Train on 60000 samples, validate on 10000 samples
    Epoch 1/10
    60000/60000 [==============================] - 2s 36us/sample - loss: 0.2852 - acc: 0.9139 - val_loss: 0.1057 - val_acc: 0.9678
    Epoch 2/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.1090 - acc: 0.9674 - val_loss: 0.0758 - val_acc: 0.9769
    Epoch 3/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0730 - acc: 0.9775 - val_loss: 0.0704 - val_acc: 0.9767
    Epoch 4/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0530 - acc: 0.9837 - val_loss: 0.0580 - val_acc: 0.9817
    Epoch 5/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0415 - acc: 0.9871 - val_loss: 0.0650 - val_acc: 0.9800
    Epoch 6/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0345 - acc: 0.9886 - val_loss: 0.0584 - val_acc: 0.9831
    Epoch 7/10
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0282 - acc: 0.9912 - val_loss: 0.0603 - val_acc: 0.9831
    Epoch 8/10
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0238 - acc: 0.9918 - val_loss: 0.0727 - val_acc: 0.9821
    Epoch 9/10
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0211 - acc: 0.9929 - val_loss: 0.0742 - val_acc: 0.9818
    Epoch 10/10
    60000/60000 [==============================] - 2s 31us/sample - loss: 0.0188 - acc: 0.9935 - val_loss: 0.0703 - val_acc: 0.9825

<matplotlib.legend.Legend at 0x2c25c87e0b8>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 4]

结果的趋势还没有很明显，增加epochs看看

history = model.fit(train_images, train_labels, 
                        epochs=20, batch_size=128,
                       validation_data=(test_images, test_labels))

Train on 60000 samples, validate on 10000 samples
    Epoch 1/20
    60000/60000 [==============================] - 2s 31us/sample - loss: 0.0154 - acc: 0.9952 - val_loss: 0.0694 - val_acc: 0.9842
    Epoch 2/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0167 - acc: 0.9944 - val_loss: 0.0651 - val_acc: 0.9837
    Epoch 3/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0142 - acc: 0.9953 - val_loss: 0.0829 - val_acc: 0.9823
    Epoch 4/20
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0126 - acc: 0.9956 - val_loss: 0.0928 - val_acc: 0.9788
    Epoch 5/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0109 - acc: 0.9963 - val_loss: 0.0758 - val_acc: 0.9832
    Epoch 6/20
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0114 - acc: 0.9961 - val_loss: 0.0850 - val_acc: 0.9821
    Epoch 7/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0141 - acc: 0.9957 - val_loss: 0.0962 - val_acc: 0.9815
    Epoch 8/20
    60000/60000 [==============================] - 2s 31us/sample - loss: 0.0099 - acc: 0.9965 - val_loss: 0.0815 - val_acc: 0.9852
    Epoch 9/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0108 - acc: 0.9964 - val_loss: 0.0870 - val_acc: 0.9839
    Epoch 10/20
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0095 - acc: 0.9968 - val_loss: 0.0848 - val_acc: 0.9841
    Epoch 11/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0102 - acc: 0.9966 - val_loss: 0.0864 - val_acc: 0.9826
    Epoch 12/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0070 - acc: 0.9977 - val_loss: 0.0897 - val_acc: 0.9843
    Epoch 13/20
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0092 - acc: 0.9973 - val_loss: 0.0935 - val_acc: 0.9834
    Epoch 14/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0077 - acc: 0.9976 - val_loss: 0.0928 - val_acc: 0.9840
    Epoch 15/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0085 - acc: 0.9974 - val_loss: 0.0921 - val_acc: 0.9850
    Epoch 16/20
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0085 - acc: 0.9973 - val_loss: 0.1007 - val_acc: 0.9829
    Epoch 17/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0090 - acc: 0.9972 - val_loss: 0.1169 - val_acc: 0.9813
    Epoch 18/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0066 - acc: 0.9978 - val_loss: 0.0962 - val_acc: 0.9838
    Epoch 19/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0077 - acc: 0.9977 - val_loss: 0.1108 - val_acc: 0.9812
    Epoch 20/20
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0074 - acc: 0.9977 - val_loss: 0.1012 - val_acc: 0.9836

<matplotlib.legend.Legend at 0x2c25c943f98>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 5]

看样子又出现过拟合情况了，接下来大家可以调整各个超参数和网络层数，继续试试

# 全部代码
    
    import pandas as pd 
    import numpy as np
    import tensorflow as tf
    import matplotlib.pyplot as plt
    from tensorflow.keras import Sequential
    from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout
    from tensorflow.keras import datasets
    %matplotlib inline
    
    # 1、加载数据
    (train_images,train_labels),(test_images,test_labels) = datasets.mnist.load_data()
    # 训练图像保存在一个uint8 类型的数组中，其形状为(60000, 28, 28)，取值区间为[0, 255]。我们需要将其变换为一个float32 数组，其形状为(60000, 28 * 28)，取值范围为0~1。
    train_images = train_images.reshape(60000, 28*28).astype('float32') / 255
    test_images = test_images.reshape(10000, 28*28).astype('float32') / 255
    # 对标签进行分类编码
    train_labels = tf.keras.utils.to_categorical(train_labels)
    test_labels = tf.keras.utils.to_categorical(test_labels)
    
    # 2、构建模型
    model = Sequential([Dense(512, activation='relu', input_shape=(28*28,)),
                       Dense(256, activation='relu'),
                       Dropout(0.4),
                       Dense(10, activation='softmax')])
    # 3、编译模型
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    # 4、拟合模型
    history = model.fit(train_images, train_labels, 
                        epochs=10, batch_size=128,
                       validation_data=(test_images, test_labels))
    
    # 5、评估模型
    # model.predict(test_images)
    # model.predict_class(test_images)
    model.evaluate(test_images, test_labels)
    
    # 结果可视化
    acc = history.history.get('acc')
    val_acc = history.history.get('val_acc')
    loss = history.history.get('loss')
    val_loss = history.history.get('val_loss')
    
    epochs = range(1, len(acc)+1)
    plt.figure(figsize=(8,4),dpi=100)
    plt.subplot(1, 2, 1)
    plt.plot(epochs, acc, 'bo', label='Traing acc')
    plt.plot(epochs, val_acc, 'r', label='Test acc')
    plt.legend()
    
    plt.subplot(1, 2, 2)
    plt.plot(epochs, loss, 'bo', label='Traing loss')
    plt.plot(epochs, val_loss, 'r', label='Test val_loss')
    plt.legend()

Train on 60000 samples, validate on 10000 samples
    Epoch 1/10
    60000/60000 [==============================] - 2s 33us/sample - loss: 0.2642 - acc: 0.9219 - val_loss: 0.1211 - val_acc: 0.9613
    Epoch 2/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.1001 - acc: 0.9698 - val_loss: 0.0777 - val_acc: 0.9770
    Epoch 3/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0665 - acc: 0.9794 - val_loss: 0.0769 - val_acc: 0.9758
    Epoch 4/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0488 - acc: 0.9849 - val_loss: 0.0749 - val_acc: 0.9772
    Epoch 5/10
    60000/60000 [==============================] - 2s 31us/sample - loss: 0.0384 - acc: 0.9877 - val_loss: 0.0714 - val_acc: 0.9797
    Epoch 6/10
    60000/60000 [==============================] - 2s 32us/sample - loss: 0.0285 - acc: 0.9908 - val_loss: 0.0722 - val_acc: 0.9786
    Epoch 7/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0247 - acc: 0.9916 - val_loss: 0.0683 - val_acc: 0.9816
    Epoch 8/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0221 - acc: 0.9924 - val_loss: 0.0653 - val_acc: 0.9818
    Epoch 9/10
    60000/60000 [==============================] - 2s 27us/sample - loss: 0.0182 - acc: 0.9939 - val_loss: 0.0738 - val_acc: 0.9806
    Epoch 10/10
    60000/60000 [==============================] - 2s 31us/sample - loss: 0.0175 - acc: 0.9943 - val_loss: 0.0761 - val_acc: 0.9820
    10000/10000 [==============================] - 1s 55us/sample - loss: 0.0761 - acc: 0.9820
    
    
    
    
    
    <matplotlib.legend.Legend at 0x2c27f922390>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 6]

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 1]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/ef453b2f02de4eacb355dd6d35046289.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 2]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/85db5641538941b3badebf045343f8c1.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 6]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/5995b206250a4f3087c8557d694ca028.png