Python深度学习笔记07--使用Keras建立卷积神经网络

 1 from keras.datasets import mnist
 2 from keras.utils import to_categorical
 3 
 4 #1. 获取数据
 5 (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
 6 
 7 #2. 处理数据
 8 train_images = train_images.reshape((60000, 28, 28, 1))
 9 train_images = train_images.astype('float32') / 255
10 
11 test_images = test_images.reshape((10000, 28, 28, 1))
12 test_images = test_images.astype('float32') / 255
13 
14 train_labels = to_categorical(train_labels)
15 test_labels = to_categorical(test_labels)
16 
17 from keras import layers
18 from keras import models
19 
20 #3. 建立网络模型
21 model = models.Sequential()
22 model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
23 model.add(layers.MaxPooling2D((2, 2)))
24 model.add(layers.Conv2D(64, (3, 3), activation='relu'))
25 model.add(layers.MaxPooling2D((2, 2)))
26 model.add(layers.Conv2D(64, (3, 3), activation='relu'))
27 
28 model.add(layers.Flatten())
29 model.add(layers.Dense(64, activation='relu'))
30 model.add(layers.Dense(10, activation='softmax'))
31 
32 # print(model.summary())
33 
34 '''
35 #网络参数计算方式
36 1.conv2d有320个参数，计算方式：(3 * 3 * 1 + 1) * 32
37 2.conv2d_1有18496个参数，计算方式：(3 * 3 * 32 + 1) * 64
38 3.conv2d_2有36928个参数，计算方式：(3 * 3 * 64 + 1) * 64
39 4.dense有36928个参数，计算方式：(576 + 1) * 64
40 5.dense_1有650个参数，计算方式：(64 + 1) * 10
41 '''
42 #4. 设置编译参数
43 model.compile(optimizer='rmsprop',
44               loss='categorical_crossentropy',
45               metrics=['accuracy'])
46 
47 #5. 设置训练条件并训练             
48 model.fit(train_images, train_labels, epochs=5, batch_size=64)
49 
50 #6. 评估模型
51 test_loss, test_acc = model.evaluate(test_images, test_labels)
52 #313/313 [==============================] - 1s 3ms/step - loss: 0.0324 - accuracy: 0.9898
53 
54 print(test_acc)
55 #0.989799976348877

5.1 卷积神经网络简介

5.1.1 卷积运算

卷积神经网络有以下两个性质：

(1)卷积神经网络学到的模式具有平移不变性。

(2)卷积神经网络可以学到模式的空间层次结构i。

卷积的两个关键参数：

(1)从输入中提取的图块尺寸

(2)输出特征的深度

Keras的API为：Conv2D(output_depth, window_height, window_width)

注意：书上这部分对卷积过程的描述不是很容易理解，建议看吴恩达的视频来学习卷积网络相关的概念。

吴恩达卷积神经网络课程地址

5.1.2 最大池化运算

最大池化是从输入特征图中提取窗口，并输出每个通道的最大值。

最大池化通常使用2 * 2的窗口和步幅2，其目的是将特征图下采样2倍。

5.2 在小型数据集上从头开始训练一个卷积神经网络

卷积神经网络代码如下：

  1 import os, shutil
  2 
  3 # The path to the directory where the original
  4 # dataset was uncompressed
  5 # original_dataset_dir = '/Users/fchollet/Downloads/kaggle_original_data'
  6 original_dataset_dir = 'E:\desktop\code\data\dogs-vs-cats\train'
  7 
  8 # The directory where we will
  9 # store our smaller dataset
 10 # base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'
 11 base_dir = 'E:\desktop\code\data\dogs-vs-cats\cats_and_dogs_small'
 12 # os.mkdir(base_dir)
 13 
 14 # Directories for our training,
 15 # validation and test splits
 16 train_dir = os.path.join(base_dir, 'train')
 17 # os.mkdir(train_dir)
 18 validation_dir = os.path.join(base_dir, 'validation')
 19 # os.mkdir(validation_dir)
 20 test_dir = os.path.join(base_dir, 'test')
 21 # os.mkdir(test_dir)
 22 
 23 # Directory with our training cat pictures
 24 train_cats_dir = os.path.join(train_dir, 'cats')
 25 # os.mkdir(train_cats_dir)
 26 
 27 # Directory with our training dog pictures
 28 train_dogs_dir = os.path.join(train_dir, 'dogs')
 29 # os.mkdir(train_dogs_dir)
 30 
 31 # Directory with our validation cat pictures
 32 validation_cats_dir = os.path.join(validation_dir, 'cats')
 33 # os.mkdir(validation_cats_dir)
 34 
 35 # Directory with our validation dog pictures
 36 validation_dogs_dir = os.path.join(validation_dir, 'dogs')
 37 # os.mkdir(validation_dogs_dir)
 38 
 39 # Directory with our validation cat pictures
 40 test_cats_dir = os.path.join(test_dir, 'cats')
 41 # os.mkdir(test_cats_dir)
 42 
 43 # Directory with our validation dog pictures
 44 test_dogs_dir = os.path.join(test_dir, 'dogs')
 45 # os.mkdir(test_dogs_dir)
 46 
 47 
 48 # #验证图片存放是否正确
 49 # print('total training cat images:', len(os.listdir(train_cats_dir)))
 50 
 51 # print('total training dog images:', len(os.listdir(train_dogs_dir)))
 52 
 53 # print('total validation cat images:', len(os.listdir(validation_cats_dir)))
 54 
 55 # print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
 56 
 57 # print('total test cat images:', len(os.listdir(test_cats_dir)))
 58 
 59 # print('total test dog images:', len(os.listdir(test_dogs_dir)))
 60 
 61 
 62 from keras import layers
 63 from keras import models
 64 
 65 model = models.Sequential()
 66 model.add(layers.Conv2D(32, (3, 3), activation='relu',
 67                         input_shape=(150, 150, 3)))
 68 model.add(layers.MaxPooling2D((2, 2)))
 69 model.add(layers.Conv2D(64, (3, 3), activation='relu'))
 70 model.add(layers.MaxPooling2D((2, 2)))
 71 model.add(layers.Conv2D(128, (3, 3), activation='relu'))
 72 model.add(layers.MaxPooling2D((2, 2)))
 73 model.add(layers.Conv2D(128, (3, 3), activation='relu'))
 74 model.add(layers.MaxPooling2D((2, 2)))
 75 model.add(layers.Flatten())
 76 model.add(layers.Dense(512, activation='relu'))
 77 model.add(layers.Dense(1, activation='sigmoid'))
 78 
 79 from keras import optimizers
 80 
 81 model.compile(loss='binary_crossentropy',
 82               optimizer=optimizers.RMSprop(lr=1e-4),
 83               metrics=['acc'])
 84 
 85 
 86 from keras.preprocessing.image import ImageDataGenerator
 87 
 88 # All images will be rescaled by 1./255
 89 train_datagen = ImageDataGenerator(rescale=1./255)
 90 test_datagen = ImageDataGenerator(rescale=1./255)
 91 
 92 train_generator = train_datagen.flow_from_directory(
 93         # This is the target directory
 94         train_dir,
 95         # All images will be resized to 150x150
 96         target_size=(150, 150),
 97         batch_size=20,
 98         # Since we use binary_crossentropy loss, we need binary labels
 99         class_mode='binary')
100 
101 validation_generator = test_datagen.flow_from_directory(
102         validation_dir,
103         target_size=(150, 150),
104         batch_size=20,
105         class_mode='binary')        
106 
107 history = model.fit_generator(
108       train_generator,
109       steps_per_epoch=100,
110       epochs=30,
111       validation_data=validation_generator,
112       validation_steps=50)       
113 
114 model.save('cats_and_dogs_small_1.h5')
115 # model.load_weights('cats_and_dogs_small_1.h5')
116 
117 
118 
119 import matplotlib.pyplot as plt
120 
121 # #dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
122 acc = history.history['acc']
123 val_acc = history.history['val_acc']
124 loss = history.history['loss']
125 val_loss = history.history['val_loss']
126 
127 epochs = range(len(acc))
128 
129 plt.plot(epochs, acc, 'bo', label='Training acc')
130 plt.plot(epochs, val_acc, 'b', label='Validation acc')
131 plt.title('Training and validation accuracy')
132 plt.legend()
133 
134 plt.figure()
135 
136 plt.plot(epochs, loss, 'bo', label='Training loss')
137 plt.plot(epochs, val_loss, 'b', label='Validation loss')
138 plt.title('Training and validation loss')
139 plt.legend()
140 
141 plt.show()

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原文地址：https://www.cnblogs.com/asenyang/p/14321973.html