第T9周：Tensorflow实现猫狗识别(2)

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

具体实现

（一）环境

语言环境：Python 3.10
编译器: PyCharm
框架: Tensorflow 2.10.0

（二）具体步骤

from absl.logging import warning  
import tensorflow as tf  
from tensorflow.python.data import AUTOTUNE  
  
from utils import GPU_ON  
import matplotlib.pyplot as plt  
# 目标：主要学习数据增强的方式方法

# 第一步：准备环境  
# 查询tensorflow版本
    print("Tensorflow Version：", tf.__version__)
    # print(tf.config.experimental.list_physical_devices('GPU'))

    # 设置使用GPU
    gpus = tf.config.list_physical_devices("GPU")
    print(gpus)

    if gpus:
        gpu0 = gpus[0]  # 如果有多个GPU，仅使用第0个GPU
        tf.config.experimental.set_memory_growth(gpu0, True)  # 设置GPU显存按需使用
        tf.config.set_visible_devices([gpu0], "GPU")>)
  
# ##########output#############################################  
# Tensorflow Version： 2.10.0# [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]  
# [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]  
# ##########end output##########################################  
# 支持中文  
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来显示中文标签  
plt.rcParams['axes.unicode_minus'] = False     # 用来正常显示负号  
  
import os, PIL, pathlib  
  
# 隐藏警告  
import warnings  
warnings.filterwarnings('ignore')  
  
# 第二步：导入数据  
data_dir = "./datasets/365-7-data"  
data_dir = pathlib.Path(data_dir)  
image_count = len(list(data_dir.glob('*/*')))  
print("图片总数为：", image_count)  
# ########output##############################################  
# 图片总数为： 3400# ########end output##########################################  
  
# 第三步：数据预处理  
batch_size = 8  
img_height, img_width = 224, 224  
train_ds = tf.keras.preprocessing.image_dataset_from_directory(  
    data_dir,  
    validation_split=0.2,  
    subset="training",  
    seed=123,  
    image_size=(img_height, img_width),  
    batch_size=batch_size,  
)  
# ############output##########################################  
# Found 3400 files belonging to 2 classes.  
# Using 2720 files for training.  
##############end output######################################  
  
val_ds = tf.keras.preprocessing.image_dataset_from_directory(  
    data_dir,  
    validation_split=0.2,  
    subset="validation",  
    seed=123,  
    image_size=(img_height, img_width),  
    batch_size=batch_size,  
)  
# ############output##########################################  
# Found 3400 files belonging to 2 classes.  
# Using 680 files for validation.  
# ###############end output##################################  
# 获取名称标签  
class_names = train_ds.class_names  
print(class_names)  
# #################output######################################  
# ['cat', 'dog']  
###################end output###################################  
# 检查一下数据  
for image_batch, labels_batch in train_ds:  
    print(image_batch.shape)  
    print(labels_batch.shape)  
    break  
# #############output########################################  
# (8, 224, 224, 3)  ---每一批8张图片，长224，宽224，RGB彩色通道（3）  
# (8,) --- 标签就是一批8张图片的标签  
# #############end output###################################  
# 预处理  
AUTOTUNE = tf.data.AUTOTUNE  
  
  
def preprocess_image(image, label):  
    return (image / 255.0, label)  
  
  
# 归一化处理  
train_ds = train_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)  
val_ds = val_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)  
  
# cache() ----将数据集缓存到内存当中 加速运行  
# shuffle() ----打乱数据  
# prefetch() ----预取数据，加速运行  
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)  
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)  
  
# 可视化数据  
plt.figure(figsize=(15, 10))    # 创建一个顶层容器，大小是15*20英寸  
for images, labels in train_ds.take(1):  
    for i in range(8):  
	    # 向当前图添加坐标轴， 我们想在1行显示8张图片，所以是1行8列  
        ax = plt.subplot(1, 8, i + 1)   
        print(images[i])  
        # imshow()--将数据显示为图像，支持的数据类型（M,N)标量数据/（M,N,3)RGB数据/（M,N,4)RGBA数据。本例中是RGB  
        plt.imshow(images[i])       
        plt.title(class_names[labels[i]])   # 显示坐标轴标签  
        plt.axis('off')     # 隐藏所有的轴信息  
  
plt.show()

# 第四步：构建VGG16网络模型  
from tensorflow.keras import layers, models, Input  
from tensorflow.keras.models import Model  
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout  
  
def VGG16(nb_classes, input_shape):  
    input_tensor = Input(shape=input_shape)  
    # 1st block  
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv1')(input_tensor)  
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv2')(x)  
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block1_pool')(x)  
    # 2nd block  
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv1')(x)  
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv2')(x)  
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block2_pool')(x)  
    # 3rd block  
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv1')(x)  
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv2')(x)  
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv3')(x)  
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block3_pool')(x)  
    # 4th block  
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv1')(x)  
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv2')(x)  
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv3')(x)  
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block4_pool')(x)  
    # 5th block  
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv1')(x)  
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv2')(x)  
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv3')(x)  
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block5_pool')(x)  
    # full connection  
    x = Flatten()(x)  
    x = Dense(4096, activation='relu',  name='fc1')(x)  
    x = Dense(4096, activation='relu', name='fc2')(x)  
    output_tensor = Dense(nb_classes, activation='softmax', name='predictions')(x)  
  
    model = Model(input_tensor, output_tensor)  
    return model  
  
model=VGG16(1000, (img_width, img_height, 3))  
model.summary()

  
# 第五步：编译  
model.compile(loss='sparse_categorical_crossentropy',  # 损失函数  
              optimizer='adam',                 # 优化函数  
              metrics=['accuracy'])             # 模型评估的指标，一般是accuracy  
# 第六步：训练模型  
from tqdm import tqdm  
import tensorflow.keras.backend as K  
  
epochs = 10  
lr = 1e-4  
  
# 记录训练数据，方便后面分析  
history_train_loss = []  
history_val_loss = []  
history_train_accuracy = []  
history_val_accuracy = []  
  
for epoch in range(epochs):  
    train_total = len(train_ds)  
    val_total = len(val_ds)  
  
    """  
    total: 预期的迭代数目  
    ncols: 控制进度条宽度  
    mininterval: 进度条更新最小间隔，以秒为单位（默认为0.1）  
    """    with tqdm(total=train_total,  
              desc=f'Epoch {epoch + 1}/{epochs}',  
              mininterval=1,  
              ncols=100) as pbar:  
        lr = lr * 0.92  
        K.set_value(model.optimizer.lr, lr)  
  
        for image, label in train_ds:  
            history = model.train_on_batch(image, label)  
            train_loss = history[0]  
            train_accuracy = history[1]  
  
            pbar.set_postfix({  
                "loss": "%.4f" % train_loss,  
                "accuracy": "%.4f" % train_accuracy,  
                "lr": K.get_value(model.optimizer.lr),  
            })  
            pbar.update(1)  
        history_train_loss.append(train_loss)  
        history_train_accuracy.append(train_accuracy)  
  
    print('开始验证!')  
  
    with tqdm(total=val_total,  
              desc=f'Epoch {epoch + 1}/{epochs}',  
              mininterval=0.3,  
              ncols=100) as pbar:  
        for image, label in val_ds:  
            history = model.test_on_batch(image, label)  
            val_loss = history[0]  
            val_accuracy = history[1]  
  
            pbar.set_postfix({  
                "loss": "%.4f" % val_loss,  
                "accuracy": "%.4f" % val_accuracy  
            })  
            pbar.update(1)  
        history_val_loss.append(val_loss)  
        history_val_accuracy.append(val_accuracy)  
  
    print('结束验证！')  
    print('验证loss为：%.4f'%val_loss)  
    print('验证准确率为：%.4f'%val_accuracy)

# 第七步：评估模型  
 
[# 采用加载的模型（new_model）来看预测结果  
plt.figure(figsize=(18, 3))  # 图形的宽为18高为5  
plt.suptitle("预测结果展示")  
  
for images, labels in val_ds.take(1):  
    for i in range(8):  
        ax = plt.subplot(1, 8, i + 1)  
  
        # 显示图片  
        plt.imshow(images[i].numpy())  
  
        # 需要给图片增加一个维度  
        img_array = tf.expand_dims(images[i], 0)  
  
        # 使用模型预测图片中的人物  
        predictions = model.predict(img_array)  
        plt.title(class_names[np.argmax(predictions)])  
  
        plt.axis("off")  
plt.show()](<epochs_range = range(epochs)

plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, history_train_accuracy, label='Training Accuracy')
plt.plot(epochs_range, history_val_accuracy, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, history_train_loss, label='Training Loss')
plt.plot(epochs_range, history_val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()>)

  
# 第八步：预测  
import numpy as np  
# 采用加载的模型（new_model）来看预测结果  
plt.figure(figsize=(18, 3))  # 图形的宽为18高为5  
plt.suptitle("预测结果展示")  
  
for images, labels in val_ds.take(1):  
    for i in range(8):  
        ax = plt.subplot(1, 8, i + 1)  
  
        # 显示图片  
        plt.imshow(images[i].numpy())  
  
        # 需要给图片增加一个维度  
        img_array = tf.expand_dims(images[i], 0)  
  
        # 使用模型预测图片中的人物  
        predictions = model.predict(img_array)  
        plt.title(class_names[np.argmax(predictions)])  
  
        plt.axis("off")
plt.show()

总结
1. 我的GPU是nvidia RTX 4060 laptop,当batch设定为64时，报错：W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.03GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available. 但字面理解为显存不足. batch改为32则无问题。那么，对于 batch的大小，如何来界定？

第T9周：Tensorflow实现猫狗识别(2)

具体实现

（一）环境

（二）具体步骤

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