第T7周：使用TensorFlow实现咖啡豆识别

oufoc2024-08-07 17:04

电脑环境：

语言环境：Python 3.8.0

编译器：Jupyter Notebook

深度学习环境：tensorflow 2.15.0

一、前期工作

1.设置GPU（如果使用的是CPU可以忽略这步）

python 复制代码 
import tensorflow as tf

gpus = tf.config.list_physical_devices("GPU")

if gpus:
    tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpus[0]],"GPU")

2. 导入数据

python 复制代码 
from tensorflow       import keras
from tensorflow.keras import layers,models
import numpy             as np
import matplotlib.pyplot as plt
import os,PIL,pathlib

data_dir = "./49-data/"
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.png')))

print("图片总数为：",image_count)

输出：图片总数为： 1200

二、数据预处理

1、加载数据

使用image_dataset_from_directory方法将磁盘中的数据加载到tf.data.Dataset中。

python 复制代码 
batch_size = 32
img_height = 224
img_width = 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)
    
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)

我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。

python 复制代码 
class_names = train_ds.class_names
print(class_names)

输出：

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

2、数据可视化

python 复制代码 
plt.figure(figsize=(10, 4))  # 图形的宽为10高为5

for 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")
python 复制代码 
for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break

输出：

(32, 224, 224, 3)

(32,)

3、配置数据集

python 复制代码 
AUTOTUNE = tf.data.AUTOTUNE
train_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))

输出：

0.0 1.0

三、构建CNN网络

调用官方的VGG-16网络框架：

python 复制代码 
from keras.applications import VGG16

VGG16 = VGG16(weights='imagenet')
VGG16.summary()

网络结构：

python 复制代码 
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 fc1 (Dense)                 (None, 4096)              102764544 
                                                                 
 fc2 (Dense)                 (None, 4096)              16781312  
                                                                 
 predictions (Dense)         (None, 1000)              4097000   
                                                                 
=================================================================
Total params: 138357544 (527.79 MB)
Trainable params: 138357544 (527.79 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

四、编译

python 复制代码 
# 设置初始学习率
initial_learning_rate = 1e-4

lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate, 
        decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochs
        decay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lr
        staircase=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=True),
              metrics=['accuracy'])

五、训练模型

python 复制代码 
epochs = 20

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

输出：

python 复制代码 
30/30 ━━━━━━━━━━━━━━━━━━━━ 250s 2s/step - accuracy: 0.2618 - loss: 2.4494 - val_accuracy: 0.5917 - val_loss: 0.9642
Epoch 2/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 33s 574ms/step - accuracy: 0.5156 - loss: 0.9331 - val_accuracy: 0.7167 - val_loss: 0.5675
Epoch 3/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 20s 567ms/step - accuracy: 0.7658 - loss: 0.4992 - val_accuracy: 0.8542 - val_loss: 0.3884
Epoch 4/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 20s 557ms/step - accuracy: 0.8599 - loss: 0.3491 - val_accuracy: 0.9458 - val_loss: 0.2667
Epoch 5/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 20s 556ms/step - accuracy: 0.9275 - loss: 0.2271 - val_accuracy: 0.9708 - val_loss: 0.1413
Epoch 6/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 563ms/step - accuracy: 0.9844 - loss: 0.0544 - val_accuracy: 0.9750 - val_loss: 0.0923
Epoch 7/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 571ms/step - accuracy: 0.9813 - loss: 0.0494 - val_accuracy: 0.9833 - val_loss: 0.0411
Epoch 8/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 20s 560ms/step - accuracy: 0.9852 - loss: 0.0428 - val_accuracy: 0.9958 - val_loss: 0.0133
Epoch 9/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 568ms/step - accuracy: 0.9824 - loss: 0.0479 - val_accuracy: 0.9875 - val_loss: 0.0341
Epoch 10/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 569ms/step - accuracy: 0.9967 - loss: 0.0119 - val_accuracy: 0.9875 - val_loss: 0.0725
Epoch 11/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 20s 571ms/step - accuracy: 0.9833 - loss: 0.0462 - val_accuracy: 0.9583 - val_loss: 0.1175
Epoch 12/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 17s 562ms/step - accuracy: 0.9858 - loss: 0.0534 - val_accuracy: 0.9500 - val_loss: 0.1280
Epoch 13/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 567ms/step - accuracy: 0.9805 - loss: 0.0719 - val_accuracy: 0.9917 - val_loss: 0.0282
Epoch 14/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 17s 561ms/step - accuracy: 0.9886 - loss: 0.0376 - val_accuracy: 0.9625 - val_loss: 0.1005
Epoch 15/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 567ms/step - accuracy: 0.9901 - loss: 0.0305 - val_accuracy: 0.9917 - val_loss: 0.0467
Epoch 16/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 569ms/step - accuracy: 1.0000 - loss: 0.0024 - val_accuracy: 0.9917 - val_loss: 0.0475
Epoch 17/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 17s 571ms/step - accuracy: 0.9955 - loss: 0.0090 - val_accuracy: 0.9625 - val_loss: 0.1122
Epoch 18/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 20s 559ms/step - accuracy: 0.9949 - loss: 0.0186 - val_accuracy: 0.9917 - val_loss: 0.0140
Epoch 19/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 568ms/step - accuracy: 0.9992 - loss: 0.0022 - val_accuracy: 0.9958 - val_loss: 0.0140
Epoch 20/20
30/30 ━━━━━━━━━━━━━━━━━━━━ 20s 569ms/step - accuracy: 1.0000 - loss: 4.4589e-04 - val_accuracy: 1.0000 - val_loss: 0.0025

六、可视化结果

python 复制代码 
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()

七、轻量化模型

上边我们可以看到官方的VGG16模型的Total params: 138 357 544 (527.79 MB)。

1、冻结VGG16网络

现在尝试只加载下图的除去绿色的部分，并且冻结模型的卷基层的权重参数，让它们不参加训练，手动加上自定义的全连接层和Dropout层。

python 复制代码 
VGG16 = tf.keras.applications.VGG16(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
VGG16.trainable = False
# 创建输入层
inputs = tf.keras.Input(shape=(224, 224, 3))

# 使用 VGG16 作为卷积基
x = VGG16(inputs, training=False)

# 添加自定义的全连接层
x = layers.Flatten()(x)
x = layers.Dense(256, activation='relu')(x)
x = layers.Dropout(0.4)(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dropout(0.4)(x)
outputs = layers.Dense(len(class_names))(x)  

# 创建完整的模型
model = tf.keras.Model(inputs, outputs)

# 查看模型结构
model.summary()

输出：

python 复制代码 
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ input_layer_11 (InputLayer)          │ (None, 224, 224, 3)         │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ vgg16 (Functional)                   │ (None, 7, 7, 512)           │      14,714,688 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ flatten_6 (Flatten)                  │ (None, 25088)               │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_12 (Dense)                     │ (None, 256)                 │       6,422,784 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_6 (Dropout)                  │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_13 (Dense)                     │ (None, 128)                 │          32,896 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_7 (Dropout)                  │ (None, 128)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_14 (Dense)                     │ (None, 4)                   │             516 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 21,170,884 (80.76 MB)
 Trainable params: 6,456,196 (24.63 MB)
 Non-trainable params: 14,714,688 (56.13 MB)

这里咱们可以看到Total params: 21,170,884 (80.76 MB)，相比于原模型，降低了很多。使用这个模型重新训练。当然要重新编译一次，并且增加了epochs=30。

python 复制代码 
# 设置初始学习率
initial_learning_rate = 1e-4

lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate, 
        decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochs
        decay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lr
        staircase=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=True),
              metrics=['accuracy'])
epochs = 30

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

输出：

python 复制代码 
Epoch 1/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 14s 317ms/step - accuracy: 0.2922 - loss: 1.5063 - val_accuracy: 0.7542 - val_loss: 0.9573
Epoch 2/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 14s 163ms/step - accuracy: 0.6025 - loss: 1.0074 - val_accuracy: 0.8042 - val_loss: 0.7020
Epoch 3/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 179ms/step - accuracy: 0.7010 - loss: 0.7734 - val_accuracy: 0.8042 - val_loss: 0.5710
Epoch 4/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 174ms/step - accuracy: 0.7596 - loss: 0.6712 - val_accuracy: 0.8417 - val_loss: 0.4787
Epoch 5/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 166ms/step - accuracy: 0.8236 - loss: 0.5016 - val_accuracy: 0.8625 - val_loss: 0.4037
Epoch 6/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 161ms/step - accuracy: 0.8319 - loss: 0.4423 - val_accuracy: 0.8875 - val_loss: 0.3518
Epoch 7/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.8855 - loss: 0.3870 - val_accuracy: 0.9208 - val_loss: 0.3057
Epoch 8/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 164ms/step - accuracy: 0.8768 - loss: 0.3587 - val_accuracy: 0.9042 - val_loss: 0.2942
Epoch 9/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.9260 - loss: 0.2672 - val_accuracy: 0.9167 - val_loss: 0.2513
Epoch 10/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 174ms/step - accuracy: 0.9284 - loss: 0.2532 - val_accuracy: 0.9083 - val_loss: 0.2328
Epoch 11/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 172ms/step - accuracy: 0.9358 - loss: 0.2266 - val_accuracy: 0.9083 - val_loss: 0.2321
Epoch 12/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 164ms/step - accuracy: 0.9291 - loss: 0.2178 - val_accuracy: 0.9167 - val_loss: 0.2157
Epoch 13/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.9431 - loss: 0.1964 - val_accuracy: 0.9042 - val_loss: 0.2257
Epoch 14/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 172ms/step - accuracy: 0.9477 - loss: 0.1889 - val_accuracy: 0.9208 - val_loss: 0.2035
Epoch 15/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 165ms/step - accuracy: 0.9608 - loss: 0.1353 - val_accuracy: 0.9417 - val_loss: 0.1697
Epoch 16/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 173ms/step - accuracy: 0.9588 - loss: 0.1484 - val_accuracy: 0.9458 - val_loss: 0.1746
Epoch 17/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 160ms/step - accuracy: 0.9762 - loss: 0.1211 - val_accuracy: 0.9458 - val_loss: 0.1554
Epoch 18/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 172ms/step - accuracy: 0.9661 - loss: 0.1170 - val_accuracy: 0.9250 - val_loss: 0.1851
Epoch 19/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 166ms/step - accuracy: 0.9814 - loss: 0.0967 - val_accuracy: 0.9458 - val_loss: 0.1436
Epoch 20/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 163ms/step - accuracy: 0.9648 - loss: 0.1073 - val_accuracy: 0.9375 - val_loss: 0.1661
Epoch 21/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 175ms/step - accuracy: 0.9728 - loss: 0.1074 - val_accuracy: 0.9375 - val_loss: 0.1564
Epoch 22/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.9784 - loss: 0.0851 - val_accuracy: 0.9458 - val_loss: 0.1421
Epoch 23/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 176ms/step - accuracy: 0.9789 - loss: 0.0706 - val_accuracy: 0.9500 - val_loss: 0.1287
Epoch 24/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 172ms/step - accuracy: 0.9859 - loss: 0.0609 - val_accuracy: 0.9458 - val_loss: 0.1368
Epoch 25/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 175ms/step - accuracy: 0.9770 - loss: 0.0786 - val_accuracy: 0.9500 - val_loss: 0.1299
Epoch 26/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 172ms/step - accuracy: 0.9870 - loss: 0.0650 - val_accuracy: 0.9417 - val_loss: 0.1297
Epoch 27/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 173ms/step - accuracy: 0.9949 - loss: 0.0503 - val_accuracy: 0.9500 - val_loss: 0.1228
Epoch 28/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 176ms/step - accuracy: 0.9891 - loss: 0.0494 - val_accuracy: 0.9500 - val_loss: 0.1257
Epoch 29/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 174ms/step - accuracy: 0.9915 - loss: 0.0540 - val_accuracy: 0.9583 - val_loss: 0.1188
Epoch 30/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 5s 176ms/step - accuracy: 0.9966 - loss: 0.0372 - val_accuracy: 0.9500 - val_loss: 0.1244

输出结果val_accuracy稍微有点降低。

2、模型微调

在上个冻结了模型所有卷基层的基础上，解冻最后的三个卷基层Conv5-1 ~ Conv5-3。就是只冻结下图的Conv1-1 ~ Conv4-3的卷基层权重参数，让最后三个卷基层加上全连接层的权重参数加入训练。

python 复制代码 
VGG16.trainable = True

set_trainable = False
for layer in VGG16.layers:
    if layer.name == 'block5_conv1':
        set_trainable = True
    if set_trainable:
        layer.trainable = True
    else:
        layer.trainable = False

把学习率调成1e-5。

python 复制代码 
# 设置初始学习率
initial_learning_rate = 1e-5

lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate, 
        decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochs
        decay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lr
        staircase=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=True),
              metrics=['accuracy'])
              
epochs = 30

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

输出：

python 复制代码 
Epoch 1/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 21s 346ms/step - accuracy: 0.3300 - loss: 1.4454 - val_accuracy: 0.7750 - val_loss: 0.8242
Epoch 2/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 287ms/step - accuracy: 0.6962 - loss: 0.7934 - val_accuracy: 0.8500 - val_loss: 0.3991
Epoch 3/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 276ms/step - accuracy: 0.9098 - loss: 0.3187 - val_accuracy: 0.9542 - val_loss: 0.1491
Epoch 4/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 11s 288ms/step - accuracy: 0.9697 - loss: 0.1277 - val_accuracy: 0.9625 - val_loss: 0.0942
Epoch 5/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 274ms/step - accuracy: 0.9853 - loss: 0.0623 - val_accuracy: 0.9792 - val_loss: 0.0659
Epoch 6/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 282ms/step - accuracy: 0.9858 - loss: 0.0599 - val_accuracy: 0.9917 - val_loss: 0.0354
Epoch 7/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 282ms/step - accuracy: 0.9999 - loss: 0.0190 - val_accuracy: 0.9958 - val_loss: 0.0305
Epoch 8/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 272ms/step - accuracy: 0.9956 - loss: 0.0168 - val_accuracy: 0.9917 - val_loss: 0.0269
Epoch 9/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 273ms/step - accuracy: 0.9986 - loss: 0.0110 - val_accuracy: 0.9917 - val_loss: 0.0347
Epoch 10/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 11s 284ms/step - accuracy: 0.9961 - loss: 0.0134 - val_accuracy: 0.9917 - val_loss: 0.0341
Epoch 11/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 9s 284ms/step - accuracy: 0.9977 - loss: 0.0107 - val_accuracy: 0.9750 - val_loss: 0.0644
Epoch 12/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 275ms/step - accuracy: 0.9986 - loss: 0.0110 - val_accuracy: 0.9958 - val_loss: 0.0176
Epoch 13/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 274ms/step - accuracy: 0.9992 - loss: 0.0046 - val_accuracy: 0.9875 - val_loss: 0.0300
Epoch 14/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 272ms/step - accuracy: 1.0000 - loss: 0.0041 - val_accuracy: 0.9958 - val_loss: 0.0173
Epoch 15/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 272ms/step - accuracy: 0.9978 - loss: 0.0038 - val_accuracy: 0.9917 - val_loss: 0.0214
Epoch 16/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 9s 286ms/step - accuracy: 0.9980 - loss: 0.0032 - val_accuracy: 0.9958 - val_loss: 0.0172
Epoch 17/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 274ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9958 - val_loss: 0.0159
Epoch 18/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 284ms/step - accuracy: 1.0000 - loss: 7.7434e-04 - val_accuracy: 0.9958 - val_loss: 0.0117
Epoch 19/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 284ms/step - accuracy: 0.9999 - loss: 9.1304e-04 - val_accuracy: 0.9875 - val_loss: 0.0346
Epoch 20/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 274ms/step - accuracy: 0.9979 - loss: 0.0085 - val_accuracy: 0.9792 - val_loss: 0.0438
Epoch 21/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 274ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9958 - val_loss: 0.0127
Epoch 22/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 273ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9958 - val_loss: 0.0208
Epoch 23/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 272ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.9958 - val_loss: 0.0110
Epoch 24/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 11s 284ms/step - accuracy: 1.0000 - loss: 6.0428e-04 - val_accuracy: 0.9958 - val_loss: 0.0131
Epoch 25/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 286ms/step - accuracy: 1.0000 - loss: 6.6759e-04 - val_accuracy: 0.9958 - val_loss: 0.0158
Epoch 26/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 286ms/step - accuracy: 1.0000 - loss: 4.9527e-04 - val_accuracy: 0.9917 - val_loss: 0.0167
Epoch 27/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 273ms/step - accuracy: 1.0000 - loss: 5.7670e-04 - val_accuracy: 0.9917 - val_loss: 0.0248
Epoch 28/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 272ms/step - accuracy: 1.0000 - loss: 9.7004e-04 - val_accuracy: 0.9958 - val_loss: 0.0109
Epoch 29/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 11s 285ms/step - accuracy: 1.0000 - loss: 2.0821e-04 - val_accuracy: 0.9958 - val_loss: 0.0136
Epoch 30/30
30/30 ━━━━━━━━━━━━━━━━━━━━ 10s 286ms/step - accuracy: 1.0000 - loss: 1.2448e-04 - val_accuracy: 0.9958 - val_loss: 0.0149

从输出结果看，val_accuracy最高为0.9958，接近1，精度损失这样的程度下，但是模型大小是降到了接近原模型的1/7，还算是成功。

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