- 🍨 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
🚀我的环境：
- 语言环境：python 3.12.6
- 编译器：jupyter lab
- 深度学习环境：TensorFlow 2.17.0
前期工作
import pathlib
data_dir = "d:/Users/yxy/Desktop/46-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： 578
from PIL import Image
roses = list(data_dir.glob('train/nike/*.jpg'))
Image.open(str(roses[0])) # 打开第一张图片
数据预处理
batch_size = 32
img_height = 224
img_width = 224
import tensorflow as tf
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
"D:/Users/yxy/Desktop/46-data/train",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size
)
Found 502 files belonging to 2 classes.
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
"D:/Users/yxy/Desktop/46-data/test",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
Found 76 files belonging to 2 classes.
class_names = train_ds.class_names
print(class_names)
['adidas', 'nike']
可视化数据
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 10))
for images, labels in train_ds.take(1):
for i in range(20):
ax = plt.subplot(5, 10, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
(32, 224, 224, 3)
(32,)
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
构建CNN网络
from tensorflow.keras import models, layers
model = models.Sequential([
layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)), # 直接使用 Rescaling 层
layers.Conv2D(16, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)), # 卷积层1，卷积核3x3
layers.AveragePooling2D((2, 2)), # 池化层1，2x2采样
layers.Conv2D(32, (3, 3), activation='relu'), # 卷积层2，卷积核3x3
layers.AveragePooling2D((2, 2)), # 池化层2，2x2采样
layers.Dropout(0.3),
layers.Conv2D(64, (3, 3), activation='relu'), # 卷积层3，卷积核3x3
layers.Dropout(0.3),
layers.Flatten(), # Flatten层，连接卷积层与全连接层
layers.Dense(128, activation='relu'), # 全连接层，特征进一步提取
layers.Dense(len(class_names)) # 输出层，输出预期结果
])
model.summary()
C:\Users\yxy\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\layers\preprocessing\tf_data_layer.py:19: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(**kwargs)
C:\Users\yxy\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\layers\convolutional\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ rescaling (Rescaling) │ (None, 224, 224, 3) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d (Conv2D) │ (None, 222, 222, 16) │ 448 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ average_pooling2d (AveragePooling2D) │ (None, 111, 111, 16) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_1 (Conv2D) │ (None, 109, 109, 32) │ 4,640 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ average_pooling2d_1 │ (None, 54, 54, 32) │ 0 │ │ (AveragePooling2D) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout (Dropout) │ (None, 54, 54, 32) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_2 (Conv2D) │ (None, 52, 52, 64) │ 18,496 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_1 (Dropout) │ (None, 52, 52, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ flatten (Flatten) │ (None, 173056) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense (Dense) │ (None, 128) │ 22,151,296 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_1 (Dense) │ (None, 2) │ 258 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 22,175,138 (84.59 MB)
Trainable params: 22,175,138 (84.59 MB)
Non-trainable params: 0 (0.00 B)
训练模型
# 设置初始学习率
initial_learning_rate = 0.001
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate,
decay_steps=10, # 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)
# 将指数衰减学习率送入优化器
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
model.compile(optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
epochs = 50
# 保存最佳模型参数
checkpointer = ModelCheckpoint('best_model.weights.h5',
monitor='val_accuracy',
verbose=1,
save_best_only=True,
save_weights_only=True)
# 设置早停
earlystopper = EarlyStopping(monitor='val_accuracy',
min_delta=0.001,
patience=20,
verbose=1)
history = model.fit(train_ds,
validation_data=val_ds,
epochs=epochs,
callbacks=[checkpointer, earlystopper])
Epoch 1/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 411ms/step - accuracy: 0.5541 - loss: 5.8099
Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 460ms/step - accuracy: 0.5537 - loss: 5.6647 - val_accuracy: 0.5000 - val_loss: 0.6931
Epoch 2/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 408ms/step - accuracy: 0.4979 - loss: 0.6936
Epoch 2: val_accuracy did not improve from 0.50000
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 420ms/step - accuracy: 0.4971 - loss: 0.6939 - val_accuracy: 0.4605 - val_loss: 0.6930
Epoch 3/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 425ms/step - accuracy: 0.5060 - loss: 0.6938
Epoch 3: val_accuracy improved from 0.50000 to 0.55263, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 468ms/step - accuracy: 0.5058 - loss: 0.6938 - val_accuracy: 0.5526 - val_loss: 0.6931
Epoch 4/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 412ms/step - accuracy: 0.5399 - loss: 0.6913
Epoch 4: val_accuracy improved from 0.55263 to 0.56579, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 450ms/step - accuracy: 0.5406 - loss: 0.6913 - val_accuracy: 0.5658 - val_loss: 0.6917
Epoch 5/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 407ms/step - accuracy: 0.5821 - loss: 0.6855
Epoch 5: val_accuracy improved from 0.56579 to 0.63158, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 448ms/step - accuracy: 0.5799 - loss: 0.6855 - val_accuracy: 0.6316 - val_loss: 0.6870
Epoch 6/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 416ms/step - accuracy: 0.5698 - loss: 0.6791
Epoch 6: val_accuracy improved from 0.63158 to 0.67105, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 455ms/step - accuracy: 0.5696 - loss: 0.6791 - val_accuracy: 0.6711 - val_loss: 0.6833
Epoch 7/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 412ms/step - accuracy: 0.6049 - loss: 0.6698
Epoch 7: val_accuracy did not improve from 0.67105
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 424ms/step - accuracy: 0.6066 - loss: 0.6695 - val_accuracy: 0.6711 - val_loss: 0.6445
Epoch 8/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 410ms/step - accuracy: 0.6611 - loss: 0.6372
Epoch 8: val_accuracy improved from 0.67105 to 0.75000, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 452ms/step - accuracy: 0.6584 - loss: 0.6375 - val_accuracy: 0.7500 - val_loss: 0.6167
Epoch 9/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 411ms/step - accuracy: 0.7043 - loss: 0.5872
Epoch 9: val_accuracy did not improve from 0.75000
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 422ms/step - accuracy: 0.7046 - loss: 0.5875 - val_accuracy: 0.6974 - val_loss: 0.5772
Epoch 10/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 409ms/step - accuracy: 0.7388 - loss: 0.5560
Epoch 10: val_accuracy improved from 0.75000 to 0.77632, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 449ms/step - accuracy: 0.7386 - loss: 0.5557 - val_accuracy: 0.7763 - val_loss: 0.5483
Epoch 11/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 415ms/step - accuracy: 0.7943 - loss: 0.5053
Epoch 11: val_accuracy did not improve from 0.77632
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 426ms/step - accuracy: 0.7934 - loss: 0.5049 - val_accuracy: 0.7632 - val_loss: 0.5209
Epoch 12/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 415ms/step - accuracy: 0.7719 - loss: 0.4902
Epoch 12: val_accuracy did not improve from 0.77632
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 426ms/step - accuracy: 0.7728 - loss: 0.4890 - val_accuracy: 0.7500 - val_loss: 0.5054
Epoch 13/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 410ms/step - accuracy: 0.8129 - loss: 0.4532
Epoch 13: val_accuracy did not improve from 0.77632
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 423ms/step - accuracy: 0.8135 - loss: 0.4520 - val_accuracy: 0.7632 - val_loss: 0.4917
Epoch 14/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 427ms/step - accuracy: 0.8315 - loss: 0.3951
Epoch 14: val_accuracy did not improve from 0.77632
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 439ms/step - accuracy: 0.8309 - loss: 0.3961 - val_accuracy: 0.7632 - val_loss: 0.4821
Epoch 15/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 426ms/step - accuracy: 0.8367 - loss: 0.4152
Epoch 15: val_accuracy improved from 0.77632 to 0.78947, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 472ms/step - accuracy: 0.8371 - loss: 0.4144 - val_accuracy: 0.7895 - val_loss: 0.4953
Epoch 16/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 460ms/step - accuracy: 0.8420 - loss: 0.3586
Epoch 16: val_accuracy improved from 0.78947 to 0.80263, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 505ms/step - accuracy: 0.8418 - loss: 0.3591 - val_accuracy: 0.8026 - val_loss: 0.4772
Epoch 17/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 441ms/step - accuracy: 0.8359 - loss: 0.3863
Epoch 17: val_accuracy did not improve from 0.80263
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 453ms/step - accuracy: 0.8364 - loss: 0.3853 - val_accuracy: 0.7632 - val_loss: 0.5029
Epoch 18/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 427ms/step - accuracy: 0.8850 - loss: 0.3219
Epoch 18: val_accuracy improved from 0.80263 to 0.81579, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 465ms/step - accuracy: 0.8845 - loss: 0.3231 - val_accuracy: 0.8158 - val_loss: 0.4786
Epoch 19/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 422ms/step - accuracy: 0.8582 - loss: 0.3478
Epoch 19: val_accuracy improved from 0.81579 to 0.82895, saving model to best_model.weights.h5
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 462ms/step - accuracy: 0.8593 - loss: 0.3461 - val_accuracy: 0.8289 - val_loss: 0.4648
Epoch 20/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 425ms/step - accuracy: 0.8693 - loss: 0.3204
Epoch 20: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 438ms/step - accuracy: 0.8698 - loss: 0.3193 - val_accuracy: 0.8026 - val_loss: 0.4564
Epoch 21/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 489ms/step - accuracy: 0.8672 - loss: 0.3290
Epoch 21: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 503ms/step - accuracy: 0.8682 - loss: 0.3275 - val_accuracy: 0.7763 - val_loss: 0.4983
Epoch 22/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 463ms/step - accuracy: 0.8535 - loss: 0.3189
Epoch 22: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 478ms/step - accuracy: 0.8551 - loss: 0.3171 - val_accuracy: 0.7895 - val_loss: 0.4728
Epoch 23/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 455ms/step - accuracy: 0.9011 - loss: 0.2765
Epoch 23: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 470ms/step - accuracy: 0.9007 - loss: 0.2764 - val_accuracy: 0.8158 - val_loss: 0.4643
Epoch 24/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 450ms/step - accuracy: 0.8878 - loss: 0.2709
Epoch 24: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 463ms/step - accuracy: 0.8882 - loss: 0.2710 - val_accuracy: 0.8158 - val_loss: 0.4693
Epoch 25/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 459ms/step - accuracy: 0.9058 - loss: 0.2548
Epoch 25: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 473ms/step - accuracy: 0.9050 - loss: 0.2562 - val_accuracy: 0.8026 - val_loss: 0.4730
Epoch 26/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 454ms/step - accuracy: 0.9084 - loss: 0.2763
Epoch 26: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 467ms/step - accuracy: 0.9088 - loss: 0.2755 - val_accuracy: 0.8026 - val_loss: 0.4728
Epoch 27/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 461ms/step - accuracy: 0.8923 - loss: 0.2752
Epoch 27: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 475ms/step - accuracy: 0.8935 - loss: 0.2738 - val_accuracy: 0.8158 - val_loss: 0.4632
Epoch 28/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 459ms/step - accuracy: 0.8985 - loss: 0.2627
Epoch 28: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 474ms/step - accuracy: 0.8986 - loss: 0.2630 - val_accuracy: 0.8026 - val_loss: 0.4755
Epoch 29/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 466ms/step - accuracy: 0.8870 - loss: 0.2625
Epoch 29: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 481ms/step - accuracy: 0.8879 - loss: 0.2616 - val_accuracy: 0.8026 - val_loss: 0.4675
Epoch 30/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 448ms/step - accuracy: 0.9007 - loss: 0.2513
Epoch 30: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 462ms/step - accuracy: 0.9008 - loss: 0.2515 - val_accuracy: 0.8026 - val_loss: 0.4756
Epoch 31/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 459ms/step - accuracy: 0.9216 - loss: 0.2491
Epoch 31: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 475ms/step - accuracy: 0.9213 - loss: 0.2490 - val_accuracy: 0.8026 - val_loss: 0.4635
Epoch 32/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 451ms/step - accuracy: 0.8993 - loss: 0.2606
Epoch 32: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 463ms/step - accuracy: 0.8997 - loss: 0.2602 - val_accuracy: 0.8026 - val_loss: 0.4764
Epoch 33/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 446ms/step - accuracy: 0.9017 - loss: 0.2619
Epoch 33: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 458ms/step - accuracy: 0.9029 - loss: 0.2604 - val_accuracy: 0.8026 - val_loss: 0.4736
Epoch 34/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 429ms/step - accuracy: 0.9106 - loss: 0.2577
Epoch 34: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 441ms/step - accuracy: 0.9115 - loss: 0.2564 - val_accuracy: 0.8026 - val_loss: 0.4719
Epoch 35/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 430ms/step - accuracy: 0.8971 - loss: 0.2470
Epoch 35: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 443ms/step - accuracy: 0.8972 - loss: 0.2467 - val_accuracy: 0.8026 - val_loss: 0.4761
Epoch 36/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 436ms/step - accuracy: 0.8976 - loss: 0.2609
Epoch 36: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 449ms/step - accuracy: 0.8983 - loss: 0.2605 - val_accuracy: 0.7895 - val_loss: 0.4766
Epoch 37/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 429ms/step - accuracy: 0.9269 - loss: 0.2091
Epoch 37: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 444ms/step - accuracy: 0.9257 - loss: 0.2110 - val_accuracy: 0.8026 - val_loss: 0.4761
Epoch 38/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 441ms/step - accuracy: 0.9272 - loss: 0.2325
Epoch 38: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 454ms/step - accuracy: 0.9264 - loss: 0.2332 - val_accuracy: 0.8026 - val_loss: 0.4745
Epoch 39/50
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 453ms/step - accuracy: 0.9357 - loss: 0.2122
Epoch 39: val_accuracy did not improve from 0.82895
[1m16/16[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 467ms/step - accuracy: 0.9350 - loss: 0.2133 - val_accuracy: 0.8026 - val_loss: 0.4719
Epoch 39: early stopping
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(len(loss))
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()
# 加载效果最好的模型权重
model.load_weights('best_model.weights.h5')
from PIL import Image
import numpy as np
img = Image.open("d:/Users/yxy/Desktop/46-data/test/nike/1.jpg") #这里选择你需要预测的图片
image = tf.image.resize(img, [img_height, img_width])
img_array = tf.expand_dims(image, 0) #/255.0 # 记得做归一化处理（与训练集处理方式保持一致）
predictions = model.predict(img_array) # 这里选用你已经训练好的模型
print("预测结果为：",class_names[np.argmax(predictions)])
[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 119ms/step
预测结果为： nike
总结
本周进行了运动鞋品牌识别的练习，这是一个经典的图像分类问题，通过深度学习技术能够有效地对运动鞋品牌进行识别。此外，学习了学习率大与学习率小的优缺点对比，
学习率大的优点：
○ 1、加快学习速率。
○ 2、有助于跳出局部最优值。
缺点：
○ 1、导致模型训练不收敛。
○ 2、单单使用大学习率容易导致模型不精确。
学习率小的优点：
○ 1、有助于模型收敛、模型细化。
○ 2、提高模型精度。
缺点：
○ 1、很难跳出局部最优值。
○ 2、收敛缓慢。
扫一扫
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
