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from tensorflow import keras
from tensorflow.keras import layers,models
import os, PIL, pathlib
import matplotlib.pyplot as plt
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GPU
tf.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")
gpusdata_dir = r'E:\T3\diwutian\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
roses = list(data_dir.glob('train/nike/*.jpg'))
PIL.Image.open(str(roses[0]))
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.youkuaiyun.com/article/details/117018789
"""
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
"E:/T3/diwutian/46-data/train/",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)Found 502 files belonging to 2 classes.
"""
关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.youkuaiyun.com/article/details/117018789
"""
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
"E:/T3/diwutian/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']
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)"""
关于卷积核的计算不懂的可以参考文章：https://blog.youkuaiyun.com/qq_38251616/article/details/114278995
layers.Dropout(0.4) 作用是防止过拟合，提高模型的泛化能力。
关于Dropout层的更多介绍可以参考文章：https://mtyjkh.blog.youkuaiyun.com/article/details/115826689
"""
model = models.Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.Conv2D(16, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)), # 卷积层1，卷积核3*3
layers.AveragePooling2D((2, 2)), # 池化层1，2*2采样
layers.Conv2D(32, (3, 3), activation='relu'), # 卷积层2，卷积核3*3
layers.AveragePooling2D((2, 2)), # 池化层2，2*2采样
layers.Dropout(0.3),
layers.Conv2D(64, (3, 3), activation='relu'), # 卷积层3，卷积核3*3
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() # 打印网络结构WARNING:tensorflow:From D:\dl\envs\pytorch_gpu\lib\site-packages\keras\src\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
rescaling (Rescaling) (None, 224, 224, 3) 0
conv2d (Conv2D) (None, 222, 222, 16) 448
average_pooling2d (Average (None, 111, 111, 16) 0
Pooling2D)
conv2d_1 (Conv2D) (None, 109, 109, 32) 4640
average_pooling2d_1 (Avera (None, 54, 54, 32) 0
gePooling2D)
dropout (Dropout) (None, 54, 54, 32) 0
conv2d_2 (Conv2D) (None, 52, 52, 64) 18496
dropout_1 (Dropout) (None, 52, 52, 64) 0
flatten (Flatten) (None, 173056) 0
dense (Dense) (None, 128) 22151296
dense_1 (Dense) (None, 2) 258
=================================================================
Total params: 22175138 (84.59 MB)
Trainable params: 22175138 (84.59 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
# 设置初始学习率
initial_learning_rate = 0.1
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.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 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5 16/16 [==============================] - 2s 130ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 2/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 2: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 126ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 3/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 3: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 123ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 4/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 4: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 118ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 5/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 5: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 121ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 6/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 6: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 120ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 7/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 7: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 120ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 8/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 8: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 120ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 9/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 9: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 123ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 10/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 10: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 126ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 11/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 11: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 125ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 12/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 12: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 123ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 13/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 13: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 124ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 14/50 16/16 [==============================] - ETA: 0s - loss: 0.6932 - accuracy: 0.5000 Epoch 14: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 125ms/step - loss: 0.6932 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 15/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 15: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 118ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 16/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 16: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 121ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 17/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 17: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 121ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 18/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 18: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 120ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 19/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 19: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 133ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 20/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000 Epoch 20: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 144ms/step - loss: 0.6931 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 21/50 16/16 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5020 Epoch 21: val_accuracy did not improve from 0.50000 16/16 [==============================] - 2s 146ms/step - loss: 0.6931 - accuracy: 0.5020 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 21: early stopping
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.h5')from PIL import Image
import numpy as np
# img = Image.open("./45-data/Monkeypox/M06_01_04.jpg") #这里选择你需要预测的图片
img = Image.open("E:/T3/diwutian/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)])1/1 [==============================] - 0s 124ms/step 预测结果为： nike
总结：代码运行遇到较大问题 早停现象未完全解决 会继续寻找问题
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