深度学习面试题12：LeNet(手写数字识别)

痛定思痛。 2021-11-12 00:40 365阅读 0赞

# 目录 #

## 　　神经网络的卷积、池化、拉伸 ##

## 　　LeNet网络结构 ##

## 　　LeNet在MNIST数据集上应用 ##

## 　　参考资料 ##

--------------------

LeNet是卷积神经网络的祖师爷LeCun在1998年提出，用于解决手写数字识别的视觉任务。自那时起，CNN的最基本的架构就定下来了：卷积层、池化层、全连接层。如今各大深度学习框架中所使用的LeNet都是简化改进过的LeNet-5（-5表示具有5个层），和原始的LeNet有些许不同，比如把激活函数改为了现在很常用的ReLu。

<table> 
 <tbody> 
  <tr> 
   <td> <p style="text-align:left;"><strong><span style="font-family:'宋体';"><span style="color:#ffffff;"><span style="font-size:18pt;">神经网络的卷积、池化、拉伸</span></span></span></strong></p> </td> 
  </tr> 
 </tbody> 
</table>

前面讲了卷积和池化，卷积层可以从图像中提取特征，池化层可以进行特征压缩，拉伸是为了和全连接网络相连接。

![1244340-20190715193522618-1697212592.png][]

返回目录

<table> 
 <tbody> 
  <tr> 
   <td> <p style="text-align:left;"><strong><span style="font-family:'宋体';"><span style="color:#ffffff;"><span style="font-size:18pt;">LeNet网络结构</span></span></span></strong></p> </td> 
  </tr> 
 </tbody> 
</table>

LeNet是第一个成熟的卷积神经网络，是专门为处理MNIST数字字符集的分类问题而设计的网络，其网络如下：

![1244340-20190715193625239-576141641.png][]

返回目录

<table> 
 <tbody> 
  <tr> 
   <td> <p style="text-align:left;"><strong><span style="font-family:'宋体';"><span style="color:#ffffff;"><span style="font-size:18pt;">LeNet在MNIST数据集上应用</span></span></span></strong></p> </td> 
  </tr> 
 </tbody> 
</table>

**显示图像**

Mnist数据集中图像尺寸是28\*28的，为了贴合LeNet网络的设计，我们可以将其扩展为32\*32的，其实应用网络的变种也是很常见的。

im = X_train[0]
    from PIL import Image
    import numpy as np
    img = np.array(im)      # image类 转 numpy
    # img = img[:,:,0]        #第1通道
    im=Image.fromarray(img) # numpy 转 image类
    im.show()

![1244340-20190715193713389-370623398.png][]

可以将其上下左右增加0像素，扩展为32\*32

![1244340-20190715193722051-631495180.png][]

很明显，黑色的边框变大了一些

### **代码** ###

from keras import backend as K
    from keras.models import Sequential
    from keras.layers.convolutional import Conv2D
    from keras.layers.convolutional import MaxPooling2D
    from keras.layers.core import Activation
    from keras.layers.core import Flatten
    from keras.layers.core import Dense
    from keras.datasets import mnist
    from keras.utils import np_utils
    from keras.optimizers import SGD, RMSprop, Adam
    import numpy as np
    
    import matplotlib.pyplot as plt
    
    np.random.seed(1671)  # for reproducibility
    
    
    # define the convnet
    class LeNet:
        @staticmethod
        def build(input_shape, classes):
            model = Sequential()
            # CONV => RELU => POOL
            model.add(Conv2D(6, kernel_size=5, padding="valid",
                             input_shape=input_shape))
            model.add(Activation("relu"))
            model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
            # CONV => RELU => POOL
            model.add(Conv2D(16, kernel_size=5, padding="valid"))
            model.add(Activation("relu"))
            model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
            # Flatten => RELU layers
            model.add(Flatten())
            model.add(Dense(400))
            model.add(Activation("relu"))
            model.add(Dense(120))
            model.add(Activation("relu"))
            model.add(Dense(84))
            model.add(Activation("relu"))
            # a softmax classifier
            model.add(Dense(classes))
            model.add(Activation("softmax"))
    
            return model
    
    
    # network and training
    NB_EPOCH = 20
    BATCH_SIZE = 128
    VERBOSE = 1
    OPTIMIZER = Adam()
    VALIDATION_SPLIT = 0.2
    
    IMG_ROWS, IMG_COLS = 32, 32  # input image dimensions
    NB_CLASSES = 10  # number of outputs = number of digits
    INPUT_SHAPE = (1, IMG_ROWS, IMG_COLS)
    
    # data: shuffled and split between train and test sets
    (X_train, y_train), (X_test, y_test) = mnist.load_data(path='D:/mnist.npz')
    
    b = np.array([
        [[0]*28]*2
    ]*60000)
    X_train = np.hstack((b,X_train))
    X_train = np.hstack((X_train,b))
    b = np.array([
        [[0]*28]*2
    ]*10000)
    X_test = np.hstack((b,X_test))
    X_test = np.hstack((X_test,b))
    b = np.array([
        [[0]*2]*32
    ]*60000)
    X_train = np.c_[b,X_train]
    X_train = np.c_[X_train,b]
    b = np.array([
        [[0]*2]*32
    ]*10000)
    X_test = np.c_[b,X_test]
    X_test = np.c_[X_test,b]
    K.set_image_dim_ordering("th")
    
    # consider them as float and normalize
    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255
    X_test /= 255
    
    # we need a 60K x [1 x 28 x 28] shape as input to the CONVNET
    X_train = X_train[:, np.newaxis, :, :]
    X_test = X_test[:, np.newaxis, :, :]
    
    print(X_train.shape[0], 'train samples')
    print(X_test.shape[0], 'test samples')
    
    # convert class vectors to binary class matrices
    y_train = np_utils.to_categorical(y_train, NB_CLASSES)
    y_test = np_utils.to_categorical(y_test, NB_CLASSES)
    
    # initialize the optimizer and model
    model = LeNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
    model.compile(loss="categorical_crossentropy", optimizer=OPTIMIZER,
                  metrics=["accuracy"])
    
    history = model.fit(X_train, y_train,
                        batch_size=BATCH_SIZE, epochs=NB_EPOCH,
                        verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
    
    score = model.evaluate(X_test, y_test, verbose=VERBOSE)
    print("\nTest score:", score[0])
    print('Test accuracy:', score[1])
    
    # list all data in history
    print(history.history.keys())
    # summarize history for accuracy
    plt.plot(history.history['acc'])
    plt.plot(history.history['val_acc'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.show()
    # summarize history for loss
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.show()

运行结果：

zhaoyichen@ubuntu:~/dl1701/dl12_LeNet$ python test.py 
    /home/zhaoyichen/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating`type(float).type`.
      from ._conv import register_converters as _register_converters
    Using TensorFlow backend.
    WARNING:tensorflow:From /home/zhaoyichen/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python..
    Instructions for updating:
    Colocations handled automatically by placer.
    60000 train samples
    10000 test samples
    2019-07-15 15:24:35.248433: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX
    2019-07-15 15:24:37.223646: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56019785d2e0 executing computations on platform CUDA. Devices:
    2019-07-15 15:24:37.223826: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
    2019-07-15 15:24:37.223844: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (1): GeForce RTX 2080 Ti, Compute Capability 7.5
    2019-07-15 15:24:37.223877: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (2): GeForce RTX 2080 Ti, Compute Capability 7.5
    2019-07-15 15:24:37.223893: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (3): GeForce RTX 2080 Ti, Compute Capability 7.5
    2019-07-15 15:24:37.250787: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2194925000 Hz
    2019-07-15 15:24:37.256673: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5601979a1e10 executing computations on platform Host. Devices:
    2019-07-15 15:24:37.256804: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
    2019-07-15 15:24:37.257714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:83:00.0
    totalMemory: 10.76GiB freeMemory: 9.84GiB
    2019-07-15 15:24:37.257828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 1 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:84:00.0
    totalMemory: 10.76GiB freeMemory: 10.34GiB
    2019-07-15 15:24:37.257913: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 2 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:87:00.0
    totalMemory: 10.76GiB freeMemory: 10.60GiB
    2019-07-15 15:24:37.257995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 3 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:88:00.0
    totalMemory: 10.76GiB freeMemory: 10.60GiB
    2019-07-15 15:24:37.258834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1, 2, 3
    2019-07-15 15:24:37.268400: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
    2019-07-15 15:24:37.268438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 1 2 3 
    2019-07-15 15:24:37.268459: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N N N N 
    2019-07-15 15:24:37.268473: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1:   N N N N 
    2019-07-15 15:24:37.268487: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 2:   N N N N 
    2019-07-15 15:24:37.268500: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 3:   N N N N 
    2019-07-15 15:24:37.269057: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9568 M bus id: 0000:83:00.0, compute capability: 7.5)
    2019-07-15 15:24:37.270048: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10061 i bus id: 0000:84:00.0, compute capability: 7.5)
    2019-07-15 15:24:37.270770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 10310 i bus id: 0000:87:00.0, compute capability: 7.5)
    2019-07-15 15:24:37.271325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 10310 i bus id: 0000:88:00.0, compute capability: 7.5)
    WARNING:tensorflow:From /home/zhaoyichen/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is
    Instructions for updating:
    Use tf.cast instead.
    Train on 48000 samples, validate on 12000 samples
    Epoch 1/20
    2019-07-15 15:24:41.563183: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
    48000/48000 [==============================] - 16s 325us/step - loss: 0.2900 - acc: 0.9123 - val_loss: 0.0828 - val_acc: 0.9744
    Epoch 2/20
    48000/48000 [==============================] - 8s 174us/step - loss: 0.0735 - acc: 0.9774 - val_loss: 0.0625 - val_acc: 0.9820
    Epoch 3/20
    48000/48000 [==============================] - 9s 193us/step - loss: 0.0543 - acc: 0.9829 - val_loss: 0.0528 - val_acc: 0.9837
    Epoch 4/20
    48000/48000 [==============================] - 8s 171us/step - loss: 0.0400 - acc: 0.9873 - val_loss: 0.0488 - val_acc: 0.9861
    Epoch 5/20
    48000/48000 [==============================] - 7s 146us/step - loss: 0.0320 - acc: 0.9900 - val_loss: 0.0489 - val_acc: 0.9862
    Epoch 6/20
    48000/48000 [==============================] - 6s 128us/step - loss: 0.0269 - acc: 0.9915 - val_loss: 0.0428 - val_acc: 0.9881
    Epoch 7/20
    48000/48000 [==============================] - 8s 165us/step - loss: 0.0223 - acc: 0.9929 - val_loss: 0.0456 - val_acc: 0.9872
    Epoch 8/20
    48000/48000 [==============================] - 8s 173us/step - loss: 0.0187 - acc: 0.9936 - val_loss: 0.0490 - val_acc: 0.9868
    Epoch 9/20
    48000/48000 [==============================] - 6s 128us/step - loss: 0.0173 - acc: 0.9941 - val_loss: 0.0573 - val_acc: 0.9835
    Epoch 10/20
    48000/48000 [==============================] - 6s 133us/step - loss: 0.0148 - acc: 0.9952 - val_loss: 0.0568 - val_acc: 0.9862
    Epoch 11/20
    48000/48000 [==============================] - 6s 133us/step - loss: 0.0124 - acc: 0.9959 - val_loss: 0.0581 - val_acc: 0.9847
    Epoch 12/20
    48000/48000 [==============================] - 6s 118us/step - loss: 0.0107 - acc: 0.9968 - val_loss: 0.0528 - val_acc: 0.9873
    Epoch 13/20
    48000/48000 [==============================] - 6s 131us/step - loss: 0.0105 - acc: 0.9966 - val_loss: 0.0533 - val_acc: 0.9887
    Epoch 14/20
    48000/48000 [==============================] - 6s 130us/step - loss: 0.0113 - acc: 0.9964 - val_loss: 0.0489 - val_acc: 0.9879
    Epoch 15/20
    48000/48000 [==============================] - 6s 130us/step - loss: 0.0059 - acc: 0.9980 - val_loss: 0.0735 - val_acc: 0.9852
    Epoch 16/20
    48000/48000 [==============================] - 6s 130us/step - loss: 0.0117 - acc: 0.9959 - val_loss: 0.0559 - val_acc: 0.9881
    Epoch 17/20
    48000/48000 [==============================] - 6s 127us/step - loss: 0.0065 - acc: 0.9980 - val_loss: 0.0475 - val_acc: 0.9889
    Epoch 18/20
    48000/48000 [==============================] - 6s 118us/step - loss: 0.0074 - acc: 0.9975 - val_loss: 0.0593 - val_acc: 0.9876
    Epoch 19/20
    48000/48000 [==============================] - 6s 135us/step - loss: 0.0086 - acc: 0.9969 - val_loss: 0.0537 - val_acc: 0.9886
    Epoch 20/20
    48000/48000 [==============================] - 6s 133us/step - loss: 0.0056 - acc: 0.9984 - val_loss: 0.0555 - val_acc: 0.9880
    10000/10000 [==============================] - 1s 134us/step
    
    Test score: 0.041087062359685976
    Test accuracy: 0.9906

PS：代码中的激活函数选择的是ReLU，但是ReLU其实是AlexNet中提出的。

Mnist数据集如果下载比较慢的话，可以加入QQ群：537594183获取数据集

返回目录

[1244340-20190715193522618-1697212592.png]: /images/20211108/c8c8e62e7eb24d1c88b00c112f3787bf.png
[1244340-20190715193625239-576141641.png]: /images/20211108/3de4c1910e994d1ca2925b8f7503f42c.png
[1244340-20190715193713389-370623398.png]: /images/20211108/6451e7d04aa84c4a871136a7010b164d.png
[1244340-20190715193722051-631495180.png]: /images/20211108/755fb3f8734f4c3095e690aeb15626b4.png