欢迎来到CNN实战，尽管我们刚刚开始，但还是要往前看！让我们开始吧！
链接：https://pan.baidu.com/s/1zztS32iuNynepLq7jiF6RA
提取码：ilxh，请下载好数据，在开始
导入库
首先导入我们所需要的库
import tensorflow as tf from tensorflow.keras import layers, Sequential, optimizers import glob import random import os BATCH_SIZE = 32 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' os.environ["CUDA_VISIBLE_DEVICES"] = '2'
预处理
让我们进行数据分类
def load_data(): global all_images, all_label, name_label_type, color_label_type, name_labels, color_labels all_images = glob.glob('./dataset/*/*') # 打乱图片顺序 random.shuffle(all_images) #以‘/’为分隔符得到所有信息 all_labels = [item.split('/')[-1] for item in all_images] #以‘_’为分隔符得到all_labels里面的第一个信息，第一层文件夹 name_labels = [item.split('_')[0] for item in all_labels] #以‘_’为分隔符得到all_labels里面的第二个信息，第二层文件夹 color_labels = [item.split('_')[1] for item in all_labels] #将列表转换为集合 name_label_type = set(item for item in name_labels) #将列表转换为集合 color_label_type = set(item for item in color_labels) #将集合转换为字典，并提取标号 name_lable_index = dict((name, index) for index, name in enumerate(name_label_type)) #将集合转换为字典，并提取标号 color_lable_index = dict((color, index) for index, color in enumerate(color_label_type)) #输出为{name||index} print(name_lable_index) #输出个数，为3 print(len(name_lable_index)) #输出为{name//index} print(color_lable_index) #输出个数，为1812 print(len(color_lable_index)) #有没有都行 name_labels = [name_lable_index[name] for name in name_labels] color_labels = [color_lable_index[color] for color in color_labels]
准备工作
def load_preprocess_images(path): image = tf.io.read_file(path) # 将表示一张图像的三维矩阵重新按照jpeg格式编码并存入文件中。打开这张图像就可以得到和原始图像一样的图像 #转换为tensor image = tf.image.decode_jpeg(image, channels=3) # 调整图像大小 image = tf.image.resize(image, [224, 224]) #转换为浮点类型 image = tf.cast(image, tf.float32) #转换到0~1之间 image = image / 255.0 #例如 0.4✖2 - 1 = -0.2 # 0.6 ✖ 2 - 1 = 0.2 image = 2 * image - 1 return image
进行预处理
[
train_dataset = multi_pred_dataset.take(train_num).repeat().shuffle(train_num).batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)
]
这里的repeat()不要简单地想为重复，而是每次进行batch都要重新扫描数据
def to_dataset(): global train_dataset, valid_dataset, train_num #用于数据集的构建与预处理 AUTOTUNE = tf.data.experimental.AUTOTUNE # 图片路径列表直接转数据集 path_dataset = tf.data.Dataset.from_tensor_slices(all_images) # 把路径数据转成图片tensor数据 image_dataset = path_dataset.map(load_preprocess_images, num_parallel_calls=AUTOTUNE) #进行特征切片，就是把每一个color_labels和name_labels分为一组 label_dataset = tf.data.Dataset.from_tensor_slices((color_labels, name_labels)) #将图片数据集和标签数据集压缩打包一下 multi_pred_dataset = tf.data.Dataset.zip((image_dataset, label_dataset)) #计算一下个数 all_images_num = len(all_images) #设置80%为训练集 train_num = int(all_images_num * 0.8) #提取80%的训练集 train_dataset = multi_pred_dataset.take(train_num).repeat().shuffle(train_num).batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE) #提取剩下的数据集当作（训练开发集） valid_dataset = multi_pred_dataset.skip(train_num).batch(BATCH_SIZE)
建立模型
def train(): #以顺序连接型设置卷积层 model = Sequential([ layers.Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)), layers.BatchNormalization(), layers.Conv2D(64, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), layers.Conv2D(128, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Conv2D(128, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), layers.Conv2D(256, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Conv2D(256, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), layers.Conv2D(512, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Conv2D(512, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), ]) inputs = tf.keras.Input(shape=(224, 224, 3)) x = model(inputs) #压缩为单像素的多张图片 x = layers.GlobalAveragePooling2D()(x) #全连接层 x1 = layers.Dense(1024, activation='relu')(x) #反向随机失活 x1 = layers.Dropout(0.5)(x1) #以softmax进行输出 out_color = layers.Dense(len(color_label_type), activation='softmax', name='out_color')(x1) x2 = layers.Dense(1024, activation='relu')(x) x2 = layers.Dropout(0.5)(x2) out_name = layers.Dense(len(name_label_type), activation='softmax', name='out_name')(x2) model = tf.keras.Model(inputs=inputs, outputs=[out_color, out_name]) model.summary() #进行编译 model.compile( optimizer=optimizers.Adam(learning_rate=0.0001), loss={ 'out_color': 'sparse_categorical_crossentropy', 'out_name': 'sparse_categorical_crossentropy', }, metrics=['acc'] ) #fit函数返回一个History的对象 model.fit( train_dataset, epochs=15, steps_per_epoch=train_num // BATCH_SIZE, validation_data=valid_dataset, validation_steps=1 ) model.save('multi_pred.h5')
测试一下吧
if __name__ == '__main__': load_data() to_dataset() train()
{'dataset\\black': 0, 'dataset\\red': 1, 'dataset\\blue': 2}
3
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1812
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 224, 224, 3 0 []
)]
sequential_1 (Sequential) (None, 10, 10, 512) 4693056 ['input_2[0][0]']
global_average_pooling2d_1 (Gl (None, 512) 0 ['sequential_1[0][0]']
obalAveragePooling2D)
dense_2 (Dense) (None, 1024) 525312 ['global_average_pooling2d_1[0][0
]']
dense_3 (Dense) (None, 1024) 525312 ['global_average_pooling2d_1[0][0
]']
dropout_2 (Dropout) (None, 1024) 0 ['dense_2[0][0]']
dropout_3 (Dropout) (None, 1024) 0 ['dense_3[0][0]']
out_color (Dense) (None, 1812) 1857300 ['dropout_2[0][0]']
out_name (Dense) (None, 3) 3075 ['dropout_3[0][0]']
==================================================================================================
Total params: 7,604,055
Trainable params: 7,600,215
Non-trainable params: 3,840
__________________________________________________________________________________________________
Epoch 1/15
63/63 [==============================] - 551s 9s/step - loss: 7.7720 - out_color_loss: 7.6270 - out_name_loss: 0.1449 - out_color_acc: 4.9603e-04 - out_name_acc: 0.9489 - val_loss: 8.8415 - val_out_color_loss: 7.6047 - val_out_name_loss: 1.2368 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.3438
Epoch 2/15
63/63 [==============================] - 544s 9s/step - loss: 7.4915 - out_color_loss: 7.4127 - out_name_loss: 0.0788 - out_color_acc: 0.0035 - out_name_acc: 0.9742 - val_loss: 9.4525 - val_out_color_loss: 7.8520 - val_out_name_loss: 1.6005 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.3438
Epoch 3/15
63/63 [==============================] - 550s 9s/step - loss: 7.1851 - out_color_loss: 7.0988 - out_name_loss: 0.0863 - out_color_acc: 0.0050 - out_name_acc: 0.9712 - val_loss: 9.8440 - val_out_color_loss: 8.2164 - val_out_name_loss: 1.6276 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.4375
Epoch 4/15
63/63 [==============================] - 559s 9s/step - loss: 6.7626 - out_color_loss: 6.6860 - out_name_loss: 0.0766 - out_color_acc: 0.0079 - out_name_acc: 0.9727 - val_loss: 10.4185 - val_out_color_loss: 8.6171 - val_out_name_loss: 1.8014 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.6562
Epoch 5/15
63/63 [==============================] - 545s 9s/step - loss: 6.3178 - out_color_loss: 6.2775 - out_name_loss: 0.0403 - out_color_acc: 0.0184 - out_name_acc: 0.9871 - val_loss: 10.2609 - val_out_color_loss: 9.2650 - val_out_name_loss: 0.9959 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.6562
Epoch 6/15
63/63 [==============================] - 542s 9s/step - loss: 5.9863 - out_color_loss: 5.9314 - out_name_loss: 0.0549 - out_color_acc: 0.0243 - out_name_acc: 0.9816 - val_loss: 10.6269 - val_out_color_loss: 9.6827 - val_out_name_loss: 0.9443 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.6562
Epoch 7/15
63/63 [==============================] - 543s 9s/step - loss: 5.6085 - out_color_loss: 5.5616 - out_name_loss: 0.0469 - out_color_acc: 0.0367 - out_name_acc: 0.9836 - val_loss: 10.2469 - val_out_color_loss: 9.7589 - val_out_name_loss: 0.4880 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.7812
Epoch 8/15
63/63 [==============================] - 552s 9s/step - loss: 5.2854 - out_color_loss: 5.2536 - out_name_loss: 0.0318 - out_color_acc: 0.0352 - out_name_acc: 0.9871 - val_loss: 11.0552 - val_out_color_loss: 10.1455 - val_out_name_loss: 0.9096 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.7188
Epoch 9/15
63/63 [==============================] - 553s 9s/step - loss: 5.0149 - out_color_loss: 4.9681 - out_name_loss: 0.0468 - out_color_acc: 0.0501 - out_name_acc: 0.9836 - val_loss: 10.6914 - val_out_color_loss: 10.4317 - val_out_name_loss: 0.2597 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9062
Epoch 10/15
63/63 [==============================] - 543s 9s/step - loss: 4.8085 - out_color_loss: 4.7618 - out_name_loss: 0.0467 - out_color_acc: 0.0635 - out_name_acc: 0.9856 - val_loss: 11.1986 - val_out_color_loss: 11.1860 - val_out_name_loss: 0.0126 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 1.0000
Epoch 11/15
63/63 [==============================] - 539s 9s/step - loss: 4.5344 - out_color_loss: 4.4990 - out_name_loss: 0.0355 - out_color_acc: 0.0843 - out_name_acc: 0.9851 - val_loss: 11.4026 - val_out_color_loss: 11.2238 - val_out_name_loss: 0.1788 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9375
Epoch 12/15
63/63 [==============================] - 541s 9s/step - loss: 4.3190 - out_color_loss: 4.2725 - out_name_loss: 0.0465 - out_color_acc: 0.1086 - out_name_acc: 0.9836 - val_loss: 12.0516 - val_out_color_loss: 11.9839 - val_out_name_loss: 0.0677 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9688
Epoch 13/15
63/63 [==============================] - 537s 9s/step - loss: 4.1122 - out_color_loss: 4.0877 - out_name_loss: 0.0245 - out_color_acc: 0.1314 - out_name_acc: 0.9916 - val_loss: 12.1688 - val_out_color_loss: 11.9726 - val_out_name_loss: 0.1962 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9375
Epoch 14/15
63/63 [==============================] - 537s 9s/step - loss: 3.9003 - out_color_loss: 3.8640 - out_name_loss: 0.0363 - out_color_acc: 0.1567 - out_name_acc: 0.9876 - val_loss: 12.8494 - val_out_color_loss: 12.7712 - val_out_name_loss: 0.0782 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9688
Epoch 15/15
63/63 [==============================] - 534s 8s/step - loss: 3.7464 - out_color_loss: 3.7241 - out_name_loss: 0.0223 - out_color_acc: 0.1716 - out_name_acc: 0.9906 - val_loss: 13.4787 - val_out_color_loss: 13.3881 - val_out_name_loss: 0.0906 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9688