欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识！
您现在的位置是： 首页  >  IT编程

tensorflow2.0之keras实现卷积神经网络

程序员文章站 2023-11-30 18:12:04
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

上一篇： ftp二进制上传 FTP设置二进制(binary)模式上传文件图文教程

下一篇： Python 数据可视化视图四类操作

推荐阅读