【2019.09.14】基于TensorFlow2.0，使用Keras + mnist（手写数字）数据集进行深度学习实战

我就是我 2024-04-18 23:29 19阅读 0赞

# 基于TensorFlow2.0，使用Keras + mnist（手写数字） 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,)

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

# 2、构建模型
    model = Sequential([Dense(512, activation='relu', input_shape=(28*28,)),
                       Dense(256, activation='relu'),
                       Dense(10, activation='softmax')])

model.summary()

Model: "sequential"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    dense (Dense)                (None, 512)               401920    
    _________________________________________________________________
    dense_1 (Dense)              (None, 256)               131328    
    _________________________________________________________________
    dense_2 (Dense)              (None, 10)                2570      
    =================================================================
    Total params: 535,818
    Trainable params: 535,818
    Non-trainable params: 0
    _________________________________________________________________

# 3、编译模型
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])

在开始训练（拟合模型）之前，对数据进行预处理，将其变换为网络要求的形状

# 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)

# 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 29us/sample - loss: 0.0134 - acc: 0.9955 - val_loss: 0.0824 - val_acc: 0.9812
    Epoch 2/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0110 - acc: 0.9965 - val_loss: 0.0845 - val_acc: 0.9783
    Epoch 3/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0092 - acc: 0.9968 - val_loss: 0.0957 - val_acc: 0.9788
    Epoch 4/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0081 - acc: 0.9971 - val_loss: 0.0882 - val_acc: 0.9808
    Epoch 5/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0095 - acc: 0.9968 - val_loss: 0.0861 - val_acc: 0.9819
    Epoch 6/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0074 - acc: 0.9973 - val_loss: 0.0931 - val_acc: 0.9806
    Epoch 7/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0077 - acc: 0.9973 - val_loss: 0.0970 - val_acc: 0.9805
    Epoch 8/10
    60000/60000 [==============================] - 2s 28us/sample - loss: 0.0132 - acc: 0.9960 - val_loss: 0.0979 - val_acc: 0.9808
    Epoch 9/10
    60000/60000 [==============================] - 2s 29us/sample - loss: 0.0025 - acc: 0.9992 - val_loss: 0.0934 - val_acc: 0.9810
    Epoch 10/10
    60000/60000 [==============================] - 2s 30us/sample - loss: 0.0063 - acc: 0.9978 - val_loss: 0.0961 - val_acc: 0.9815

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

10000/10000 [==============================] - 1s 54us/sample - loss: 0.0961 - acc: 0.9815
    
    
    
    
    [0.09613426262197114, 0.9815]

# 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 0x2c202337a20>

![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 1]

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 2]

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>

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