深度学习笔记17_TensorFlow实现咖啡豆识别

server/2024/9/20 13:16:32/
  •  🍨 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 | 接辅导、项目定制

一、我的环境

1.语言环境：Python 3.9

2.编译器：Pycharm

3.深度学习环境：TensorFlow 2.10.0

二、GPU设置

       若使用的是cpu则可忽略

import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")if gpus:gpu0 = gpus[0] #如果有多个GPU，仅使用第0个GPUtf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用tf.config.set_visible_devices([gpu0],"GPU")

三、导入数据

data_dir = "./data/"
data_dir = pathlib.Path(data_dir)image_count = len(list(data_dir.glob('*/*/*.jpg')))print("图片总数为：",image_count)
#图片总数为：1200

四、数据预处理

batch_size = 32
img_height = 224
img_width = 224"""
关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789
"""
train_ds = tf.keras.preprocessing.image_dataset_from_directory("./data/train/",seed=123,image_size=(img_height, img_width),batch_size=batch_size)"""
关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789
"""
val_ds = tf.keras.preprocessing.image_dataset_from_directory("./data/test/",seed=123,image_size=(img_height, img_width),batch_size=batch_size)
class_names = train_ds.class_names
print(class_names)

运行结果： 

['Dark', 'Green', 'Light', 'Medium']

五、可视化图片

plt.figure(figsize=(10, 4))  # 图形的宽为10高为5for images, labels in train_ds.take(1):for i in range(10):ax = plt.subplot(2, 5, i + 1)  plt.imshow(images[i].numpy().astype("uint8"))plt.title(class_names[labels[i]])plt.axis("off")
plt.show()

 运行结果：

​​

再次检查数据：

for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break

 运行结果：

(32, 224, 224, 3)
(32,)

六、配置数据集

  • shuffle()：打乱数据，关于此函数的详细介绍可以参考：https://zhuanlan.zhihu.com/p/42417456
  • prefetch()：预取数据，加速运行
  • cache()：将数据集缓存到内存当中，加速运行
AUTOTUNE = tf.data.AUTOTUNEtrain_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
val_ds   = val_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(val_ds))
first_image = image_batch[0]# 查看归一化后的数据
print(np.min(first_image), np.max(first_image))

七、自建模型

from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropoutdef VGG16(nb_classes, input_shape):input_tensor = Input(shape=input_shape)# 1st blockx = 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 blockx = 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 blockx = 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 blockx = 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 blockx = 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 connectionx = 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 modelmodel=VGG16(len(class_names), (img_width, img_height, 3))
model.summary()

运行结果：

_________________________________________________________________Layer (type)                Output Shape              Param #
=================================================================input_1 (InputLayer)        [(None, 224, 224, 3)]     0block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0flatten (Flatten)           (None, 25088)             0fc1 (Dense)                 (None, 4096)              102764544fc2 (Dense)                 (None, 4096)              16781312predictions (Dense)         (None, 4)                 16388=================================================================
Total params: 134,276,932
Trainable params: 134,276,932
Non-trainable params: 0
_________________________________________________________________

八、编译

        在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：

  • 损失函数（loss）：用于衡量模型在训练期间的准确率。
  • 优化器（optimizer）：决定模型如何根据其看到的数据和自身的损失函数进行更新。
  • 指标（metrics）：用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。
# 设置初始学习率
initial_learning_rate = 1e-4lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate, decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochsdecay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lrstaircase=True)# 设置优化器
opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate)model.compile(optimizer=opt,loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['accuracy'])

九、训练模型

epochs = 20history = model.fit(train_ds,validation_data=val_ds,epochs=epochs
)

运行结果：

Epoch 1/20
30/30 [==============================] - 38s 592ms/step - loss: 1.3814 - accuracy: 0.2573 - val_loss: 1.3019 - val_accuracy: 0.3083
Epoch 2/20
30/30 [==============================] - 15s 486ms/step - loss: 1.0376 - accuracy: 0.4719 - val_loss: 0.6470 - val_accuracy: 0.7458
Epoch 3/20
30/30 [==============================] - 14s 475ms/step - loss: 0.6289 - accuracy: 0.6542 - val_loss: 0.4882 - val_accuracy: 0.7500
Epoch 4/20
30/30 [==============================] - 15s 485ms/step - loss: 0.4762 - accuracy: 0.7979 - val_loss: 1.0989 - val_accuracy: 0.8000
Epoch 5/20
30/30 [==============================] - 14s 479ms/step - loss: 0.6664 - accuracy: 0.7260 - val_loss: 0.5444 - val_accuracy: 0.7750
Epoch 6/20
30/30 [==============================] - 14s 474ms/step - loss: 0.3893 - accuracy: 0.8448 - val_loss: 0.2358 - val_accuracy: 0.8875
Epoch 7/20
30/30 [==============================] - 14s 476ms/step - loss: 0.3163 - accuracy: 0.8969 - val_loss: 0.3107 - val_accuracy: 0.8667
Epoch 8/20
30/30 [==============================] - 14s 474ms/step - loss: 0.2634 - accuracy: 0.9062 - val_loss: 0.1829 - val_accuracy: 0.9333
Epoch 9/20
30/30 [==============================] - 14s 476ms/step - loss: 0.1136 - accuracy: 0.9646 - val_loss: 0.1342 - val_accuracy: 0.9458
Epoch 10/20
30/30 [==============================] - 14s 477ms/step - loss: 0.0828 - accuracy: 0.9760 - val_loss: 0.0664 - val_accuracy: 0.9833
Epoch 11/20
30/30 [==============================] - 14s 476ms/step - loss: 0.0683 - accuracy: 0.9729 - val_loss: 0.2063 - val_accuracy: 0.9458
Epoch 12/20
30/30 [==============================] - 14s 473ms/step - loss: 0.0537 - accuracy: 0.9823 - val_loss: 0.0288 - val_accuracy: 0.9917
Epoch 13/20
30/30 [==============================] - 14s 472ms/step - loss: 0.0404 - accuracy: 0.9865 - val_loss: 0.2180 - val_accuracy: 0.9458
Epoch 14/20
30/30 [==============================] - 14s 472ms/step - loss: 0.0382 - accuracy: 0.9917 - val_loss: 0.0738 - val_accuracy: 0.9750
Epoch 15/20
30/30 [==============================] - 14s 474ms/step - loss: 0.0152 - accuracy: 0.9969 - val_loss: 0.0499 - val_accuracy: 0.9750
Epoch 16/20
30/30 [==============================] - 15s 485ms/step - loss: 0.3555 - accuracy: 0.9167 - val_loss: 0.0507 - val_accuracy: 0.9875
Epoch 17/20
30/30 [==============================] - 15s 485ms/step - loss: 0.1555 - accuracy: 0.9552 - val_loss: 0.1155 - val_accuracy: 0.9667
Epoch 18/20
30/30 [==============================] - 15s 489ms/step - loss: 0.0767 - accuracy: 0.9688 - val_loss: 0.0613 - val_accuracy: 0.9875
Epoch 19/20
30/30 [==============================] - 15s 482ms/step - loss: 0.0432 - accuracy: 0.9812 - val_loss: 0.0915 - val_accuracy: 0.9750
Epoch 20/20
30/30 [==============================] - 14s 475ms/step - loss: 0.0367 - accuracy: 0.9906 - val_loss: 0.0337 - val_accuracy: 0.9833

 十、模型评估

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']loss = history.history['loss']
val_loss = history.history['val_loss']epochs_range = range(epochs)plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

全局平均池化代替全连接层

  • 极大的减少了网络的参数量（原始网络中全连接层参数量占到整个网络参数总量的80%作用）
  • 相当于在网络结构上做正则，防止模型发生过拟合
_________________________________________________________________Layer (type)                Output Shape              Param #
=================================================================input_1 (InputLayer)        [(None, 224, 224, 3)]     0block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0global_average_pooling2d (G  (None, 512)              0lobalAveragePooling2D)predictions (Dense)         (None, 4)                 2052=================================================================
Total params: 14,716,740
Trainable params: 14,716,740
Non-trainable params: 0
_________________________________________________________________
Epoch 1/20
30/30 [==============================] - 36s 561ms/step - loss: 1.3824 - accuracy: 0.2552 - val_loss: 1.3368 - val_accuracy: 0.2125
Epoch 2/20
30/30 [==============================] - 14s 451ms/step - loss: 1.2286 - accuracy: 0.3667 - val_loss: 0.9773 - val_accuracy: 0.5500
Epoch 3/20
30/30 [==============================] - 14s 452ms/step - loss: 0.8348 - accuracy: 0.6021 - val_loss: 0.7338 - val_accuracy: 0.6625
Epoch 4/20
30/30 [==============================] - 14s 450ms/step - loss: 0.6489 - accuracy: 0.7333 - val_loss: 0.8191 - val_accuracy: 0.6542
Epoch 5/20
30/30 [==============================] - 14s 451ms/step - loss: 0.6889 - accuracy: 0.7188 - val_loss: 0.4738 - val_accuracy: 0.8167
Epoch 6/20
30/30 [==============================] - 14s 452ms/step - loss: 0.3798 - accuracy: 0.8479 - val_loss: 0.3068 - val_accuracy: 0.8667
Epoch 7/20
30/30 [==============================] - 14s 453ms/step - loss: 0.3275 - accuracy: 0.8906 - val_loss: 0.2464 - val_accuracy: 0.9000
Epoch 8/20
30/30 [==============================] - 14s 460ms/step - loss: 0.4658 - accuracy: 0.8271 - val_loss: 0.6661 - val_accuracy: 0.7500
Epoch 9/20
30/30 [==============================] - 14s 462ms/step - loss: 0.2678 - accuracy: 0.9031 - val_loss: 0.2194 - val_accuracy: 0.9208
Epoch 10/20
30/30 [==============================] - 14s 456ms/step - loss: 0.2523 - accuracy: 0.9187 - val_loss: 0.2138 - val_accuracy: 0.9250
Epoch 11/20
30/30 [==============================] - 14s 460ms/step - loss: 0.1870 - accuracy: 0.9354 - val_loss: 0.2064 - val_accuracy: 0.9125
Epoch 12/20
30/30 [==============================] - 14s 456ms/step - loss: 0.2718 - accuracy: 0.9135 - val_loss: 0.6631 - val_accuracy: 0.7500
Epoch 13/20
30/30 [==============================] - 14s 458ms/step - loss: 0.3490 - accuracy: 0.8740 - val_loss: 0.1596 - val_accuracy: 0.9458
Epoch 14/20
30/30 [==============================] - 14s 463ms/step - loss: 0.1525 - accuracy: 0.9563 - val_loss: 0.1226 - val_accuracy: 0.9625
Epoch 15/20
30/30 [==============================] - 14s 454ms/step - loss: 0.1136 - accuracy: 0.9656 - val_loss: 0.2463 - val_accuracy: 0.8958
Epoch 16/20
30/30 [==============================] - 14s 453ms/step - loss: 0.0945 - accuracy: 0.9646 - val_loss: 0.2166 - val_accuracy: 0.9250
Epoch 17/20
30/30 [==============================] - 14s 453ms/step - loss: 0.1903 - accuracy: 0.9333 - val_loss: 0.0848 - val_accuracy: 0.9625
Epoch 18/20
30/30 [==============================] - 14s 455ms/step - loss: 0.1039 - accuracy: 0.9729 - val_loss: 0.1146 - val_accuracy: 0.9542
Epoch 19/20
30/30 [==============================] - 14s 453ms/step - loss: 0.0801 - accuracy: 0.9781 - val_loss: 0.0763 - val_accuracy: 0.9708
Epoch 20/20
30/30 [==============================] - 14s 453ms/step - loss: 0.0769 - accuracy: 0.9750 - val_loss: 0.0492 - val_accuracy: 0.9708

十一、总结

       本周通过tenserflow框架创建VGG16网络模型进行猴痘病识别学习，学习如何搭建VGG16网络模型，学习在不影响准确率的前提下轻量化模型；通过使用全局平均池化代替全连接层，极大的减少了网络的参数量。


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