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tensorflow2.0之keras实现卷积神经网络

程序员文章站 2022-10-14 12:04:05
tf.keras实现卷积神经网络Keras 是一个用 Python 编写的高级神经网络 API，它能够以 TensorFlow, CNTK, 或者 Theano 作为后端运行。Keras可以很明确的定义了层的概念，反过来层与层之间的参数反倒是用户不需要关心的对象，所以构建神经网络的方法对于普通开发者来说，相对tensorflow，Keras更易上手。并且Keras也是tensorflow官方在tensorflow2.0开始极力推荐使用的。卷积神经网络（Convolutional Neural Ne...

tf.keras实现卷积神经网络

  • Keras 是一个用 Python 编写的高级神经网络 API，它能够以 TensorFlow, CNTK, 或者 Theano 作为后端运行。
    Keras可以很明确的定义了层的概念，反过来层与层之间的参数反倒是用户不需要关心的对象，所以构建神经网络的方法对于普通开发者来说，相对tensorflow，Keras更易上手。
    并且Keras也是tensorflow官方在tensorflow2.0开始极力推荐使用的。
  • 卷积神经网络（Convolutional Neural Networks, CNN）是一类包含卷积计算且具有深度结构的前馈神经网络（Feedforward Neural Networks），是深度学习（deep learning）的代表算法之一，对于图片（height，weight，channel）的输入数据，如果用DNN网络提取图片特征的话，那模型需要学习的参数每一层就有HWC这么多，这是一个呈几何倍数增长的数字，那么模型学习难度将会特别大，极易发生过拟合。考虑CNN网络，通过卷积核在图片上滑动进行卷积操作，参数量将会大大减少，并且卷积核可以提取图片特征向后传，最后通过全连接层对图片特征进行输出。
  • 首先导入一些需要使用的包，然后导入fashion_mnist数据集并分割好训练和测试集
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

(train_image,train_label),(test_image,test_label) = tf.keras.datasets.fashion_mnist.load_data()
print(train_image.shape)
print(train_label)
>> (60000, 28, 28)
   [9 0 0 ... 3 0 5]
  • 由于tf.keras的卷积神经网络的训练需要是一个四维（num,hight,weight,channel）的数据，所以下面对输入图片数据拓宽一个通道
# 把输入数据拓宽一个维度，转换成（num,hight,weight,channel）
train_images = np.expand_dims(train_image, -1) 
test_images = np.expand_dims(test_image, -1)

train_images.shape
>> (60000, 28, 28, 1)
  • 建立顺序模型
# 新建顺序模型
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32, (3,3), input_shape = train_images.shape[1: ], activation = 'relu'))    # 输入维度为3，取图片的后三维
model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2)))  #下采样，降低参数数量，同时增强下一层卷积的视野
model.add(tf.keras.layers.Conv2D(64, kernel_size = (3,3), activation = 'relu'))
model.add(tf.keras.layers.GlobalAveragePooling2D())    #把卷积之后的图片转换为2维，连到全连接层
model.add(tf.keras.layers.Dense(10, activation = 'softmax'))
          
model.summary()
>>
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_2 (Conv2D)            (None, 26, 26, 32)        320       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 11, 11, 64)        18496     
_________________________________________________________________
global_average_pooling2d_1 ( (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
=================================================================
Total params: 19,466
Trainable params: 19,466
Non-trainable params: 0
_________________________________________________________________
  • 编译训练模型
# 模型编译
model.compile(optimizer='adam',
              loss = 'sparse_categorical_crossentropy',
              metrics = ['acc']
)

history = model.fit(train_images, train_label, epochs=20, validation_data=(test_images,test_label))
>>
Train on 60000 samples, validate on 10000 samples
Epoch 1/20
60000/60000 [==============================] - 32s 527us/sample - loss: 0.7983 - acc: 0.7568 - val_loss: 0.5376 - val_acc: 0.8111
Epoch 2/20
60000/60000 [==============================] - 32s 529us/sample - loss: 0.4884 - acc: 0.8296 - val_loss: 0.4993 - val_acc: 0.8262
...
Epoch 14/20
60000/60000 [==============================] - 26s 441us/sample - loss: 0.2627 - acc: 0.9064 - val_loss: 0.3258 - val_acc: 0.8891
Epoch 15/20
60000/60000 [==============================] - 28s 460us/sample - loss: 0.2544 - acc: 0.9100 - val_loss: 0.3256 - val_acc: 0.8876
Epoch 16/20
60000/60000 [==============================] - 31s 524us/sample - loss: 0.2499 - acc: 0.9105 - val_loss: 0.3323 - val_acc: 0.8868
Epoch 17/20
60000/60000 [==============================] - 35s 576us/sample - loss: 0.2455 - acc: 0.9121 - val_loss: 0.3455 - val_acc: 0.8830
Epoch 18/20
60000/60000 [==============================] - 29s 491us/sample - loss: 0.2382 - acc: 0.9144 - val_loss: 0.3173 - val_acc: 0.8884
Epoch 19/20
60000/60000 [==============================] - 30s 500us/sample - loss: 0.2348 - acc: 0.9151 - val_loss: 0.3155 - val_acc: 0.8880
Epoch 20/20
60000/60000 [==============================] - 32s 531us/sample - loss: 0.2310 - acc: 0.9167 - val_loss: 0.3366 - val_acc: 0.8834
  • 画图更直观的查看模型的训练情况
plt.figure(figsize=(20,8),dpi = 200)
plt.plot(history.epoch, history.history.get('acc'), label = 'acc')
plt.plot(history.epoch, history.history.get('val_acc'), label = 'val_acc')
plt.legend()       
>> 
<matplotlib.legend.Legend at 0x18f7148ecc8>

tensorflow2.0之keras实现卷积神经网络
通过绘制训练epochs与训练数据集的准确率和测试数据集的准确率折线图的观察到，模型在训练数据集上的准确率比验证数据集上的准确率高，而且模型的准确率在最后还一直呈现上升趋势，说明模型过拟合了并且模型的没有完全达到状态，下面对其进行优化。

优化模型，增加卷积层，防止过拟合

  • 首先新建顺序模型，增加了一个隐藏层，并且每个隐藏层的输出通道数也增加了，因为通道数是用来传递数据集特征的，通道数过小有可能不能承接图片的全部特征导致部分特征丢失，模型效果变差。然后在每个卷积层之后添加一个Dropout层防止过拟合，最后利用softmax激活输出10中类别。
# 新建顺序模型
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(64, (3,3), 
                                 input_shape = train_images.shape[1: ], 
                                 activation = 'relu',padding = "same"))    # 输入维度为3，取图片的后三维
model.add(tf.keras.layers.Conv2D(64, kernel_size = (3,3), activation = 'relu', padding = 'same'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2)))                       # 下采样，降低参数数量，同时增强下一层卷积的视野
model.add(tf.keras.layers.Conv2D(128, kernel_size = (3,3), activation = 'relu',padding = 'same'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2)))                     
model.add(tf.keras.layers.Conv2D(256, kernel_size = (3,3), activation = 'relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.GlobalAveragePooling2D())          # 把卷积之后的图片转换为2维，连到全连接层
model.add(tf.keras.layers.Dense(10, activation = 'softmax'))
          
model.summary()
>> 
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_4 (Conv2D)            (None, 28, 28, 64)        640       
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 28, 28, 64)        36928     
_________________________________________________________________
dropout (Dropout)            (None, 28, 28, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 14, 14, 128)       73856     
_________________________________________________________________
dropout_1 (Dropout)          (None, 14, 14, 128)       0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 5, 5, 256)         295168    
_________________________________________________________________
dropout_2 (Dropout)          (None, 5, 5, 256)         0         
_________________________________________________________________
global_average_pooling2d_1 ( (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570      
=================================================================
Total params: 409,162
Trainable params: 409,162
Non-trainable params: 0
_________________________________________________________________
  • 模型编译
model.compile(
              optimizer='adam',
              loss = 'sparse_categorical_crossentropy',
              metrics = ['acc']
)

history = model.fit(train_images, train_label, epochs=20, validation_data=(test_images,test_label))

>>
Epoch 1/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.7331 - acc: 0.7903 - val_loss: 0.5045 - val_acc: 0.8555
Epoch 2/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3989 - acc: 0.8558 - val_loss: 0.4424 - val_acc: 0.8773
Epoch 3/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3585 - acc: 0.8683 - val_loss: 0.4049 - val_acc: 0.8851
Epoch 4/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3288 - acc: 0.8810 - val_loss: 0.3946 - val_acc: 0.8867
Epoch 5/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3084 - acc: 0.8876 - val_loss: 0.3423 - val_acc: 0.9027
Epoch 6/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2901 - acc: 0.8941 - val_loss: 0.3753 - val_acc: 0.8991
Epoch 7/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2826 - acc: 0.8963 - val_loss: 0.3452 - val_acc: 0.9002
Epoch 8/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2719 - acc: 0.9010 - val_loss: 0.3152 - val_acc: 0.9117
Epoch 9/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2663 - acc: 0.9027 - val_loss: 0.3082 - val_acc: 0.9103
Epoch 10/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2605 - acc: 0.9038 - val_loss: 0.2891 - val_acc: 0.9025
Epoch 11/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2544 - acc: 0.9065 - val_loss: 0.3096 - val_acc: 0.9154
Epoch 12/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2512 - acc: 0.9088 - val_loss: 0.2958 - val_acc: 0.9105
Epoch 13/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2469 - acc: 0.9092 - val_loss: 0.2882 - val_acc: 0.9178
Epoch 14/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2436 - acc: 0.9108 - val_loss: 0.2783 - val_acc: 0.9124
Epoch 15/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2400 - acc: 0.9123 - val_loss: 0.2788 - val_acc: 0.9185
Epoch 16/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2400 - acc: 0.9125 - val_loss: 0.2649 - val_acc: 0.9191
Epoch 17/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2376 - acc: 0.9136 - val_loss: 0.2707 - val_acc: 0.9193
Epoch 18/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2368 - acc: 0.9139 - val_loss: 0.2546 - val_acc: 0.9191
Epoch 19/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2327 - acc: 0.9155 - val_loss: 0.3030 - val_acc: 0.9174
Epoch 20/20
1875/1875 [==============================] - 8s 4ms/step - loss: 0.2314 - acc: 0.9154 - val_loss: 0.2615 - val_acc: 0.9239
  • 下面绘制训练轮数epochs与准确率之间的折线图
plt.figure(figsize=(20,8),dpi = 200)
plt.plot(history.epoch, history.history.get('acc'), label = 'acc')
plt.plot(history.epoch, history.history.get('val_acc'), label = 'val_acc')
plt.legend()                        
>>
<matplotlib.legend.Legend at 0x7f85d0736990>

tensorflow2.0之keras实现卷积神经网络
可以看到测试数据集的准确率一直在训练数据集之上，说明模型没有过拟合，但是训练数据集依然有上升的趋势，说明模型还是训练不足，可以考虑继续优化。

本文地址：https://blog.csdn.net/qq_44971458/article/details/107119309

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