我的环境：
- 言语环境：Python3.10.11
- 编译器：Jupyter Notebook
- 深度学习框架：TensorFlow2.4.1
- 显卡（GPU）：NVIDIA GeForce RTX 4070
相关教程：
- 编译器教程：【新手入门深度学习 | 1-2：编译器Jupyter Notebook】
- 深度学习环境配置教程：【新手入门深度学习 | 1-1：配置深度学习环境】
- 一个深度学习小白需要的所有资料我都放这儿了：【新手入门深度学习 | 目录】
主张你学习本文之前先看看下面这篇入门文章，以便你能够更好的理解本文： 新手入门深度学习 | 2-1：图画数据建模流程示例
强烈主张咱们运用Jupyter Notebook编译器翻开源码，你接下来的操作将会十分快捷的！
- 假如你是
深度学习小白，阅读本文前主张先学习一下 《新手入门深度学习》 - 假如你有一定根底，可是
缺乏实战经验，可通过 《深度学习100例》 补齐根底 - 另外，咱们正在通过 365天深度学习练习营 抱团学习，营内为咱们提供体系的学习教案与专业的辅导、十分良好的学习气氛，欢迎你的加入
一、预备数据
1. 设置GPU
import matplotlib.pyplot as plt
import numpy as np
#隐藏正告
import warnings
warnings.filterwarnings("ignore") #忽略正告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显现中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显现负号
from tensorflow.keras import layers
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显存用量按需运用
tf.config.set_visible_devices([gpus[0]],"GPU")
# 打印显卡信息，确认GPU可用
print(gpus)
[]
2. 加载数据
data_dir = "E:/Jupyter Lab/dataK/37-dataK-facial_expression/"
img_height = 224
img_width = 224
batch_size = 32
"""
关于image_dataset_from_directory()的详细介绍能够参阅文章：https://mtyjkh.blog.csdn.net/article/details/117018789
"""
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.3,
subset="training",
label_mode = "categorical",
seed=12,
image_size=(img_height, img_width),
batch_size=batch_size)
Found 14023 files belonging to 4 classes.
Using 9817 files for training.
"""
关于image_dataset_from_directory()的详细介绍能够参阅文章：https://mtyjkh.blog.csdn.net/article/details/117018789
"""
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.3,
subset="training",
label_mode = "categorical",
seed=12,
image_size=(img_height, img_width),
batch_size=batch_size)
Found 14023 files belonging to 4 classes.
Using 9817 files for training.
因为原始数据集不包含测验集，因此需要创立一个。运用 tf.data.experimental.cardinality 确定验证会集有多少批次的数据，然后将其中的 20% 移至测验集。
val_batches = tf.data.experimental.cardinality(val_ds)
test_ds = val_ds.take(val_batches // 5)
val_ds = val_ds.skip(val_batches // 5)
print('Number of validation batches: %d' % tf.data.experimental.cardinality(val_ds))
print('Number of test batches: %d' % tf.data.experimental.cardinality(test_ds))
Number of validation batches: 246
Number of test batches: 61
class_names = train_ds.class_names
print(class_names)
['Angry', 'Disgust', 'Fear', 'Happy']
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)
test_ds = test_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
plt.figure(figsize=(15, 10)) # 图形的宽为15高为10
plt.suptitle('关注微信大众号（K同学啊）获取源码')
for images, labels in train_ds.take(1):
for i in range(32):
ax = plt.subplot(5, 8, i + 1)
plt.imshow(images[i])
plt.title(class_names[np.argmax(labels[i])])
plt.axis("off")
二、构建模型
model = tf.keras.Sequential([
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(len(class_names) ,activation='softmax')
])
在预备对模型进行练习之前，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：
- 丢失函数（loss）：用于衡量模型在练习期间的准确率。
- 优化器（optimizer）：决议模型如何依据其看到的数据和本身的丢失函数进行更新。
- 评价函数（metrics）：用于监控练习和测验步骤。以下示例运用了准确率，即被正确分类的图画的比率。
"""
关于评价目标的相关内容能够参阅文章：https://mtyjkh.blog.csdn.net/article/details/123786871
"""
METRICS = [
tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.FalsePositives(name='fp'),
tf.keras.metrics.TrueNegatives(name='tn'),
tf.keras.metrics.FalseNegatives(name='fn'),
tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve
]
model.compile(optimizer="adam",
loss='categorical_crossentropy',
metrics=METRICS)
三、练习模型
epochs = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
Epoch 1/10
307/307 [==============================] - 250s 810ms/step - loss: 1.1943 - tp: 233.0000 - fp: 250.0000 - tn: 29201.0000 - fn: 9584.0000 - accuracy: 0.3956 - precision: 0.4824 - recall: 0.0237 - auc: 0.6951 - prc: 0.3833 - val_loss: 1.1629 - val_tp: 220.0000 - val_fp: 164.0000 - val_tn: 23431.0000 - val_fn: 7645.0000 - val_accuracy: 0.4356 - val_precision: 0.5729 - val_recall: 0.0280 - val_auc: 0.7283 - val_prc: 0.4336
Epoch 2/10
307/307 [==============================] - 250s 814ms/step - loss: 1.0757 - tp: 2495.0000 - fp: 1399.0000 - tn: 28052.0000 - fn: 7322.0000 - accuracy: 0.5060 - precision: 0.6407 - recall: 0.2542 - auc: 0.7840 - prc: 0.5401 - val_loss: 0.9535 - val_tp: 3113.0000 - val_fp: 1328.0000 - val_tn: 22267.0000 - val_fn: 4752.0000 - val_accuracy: 0.5880 - val_precision: 0.7010 - val_recall: 0.3958 - val_auc: 0.8378 - val_prc: 0.6482
Epoch 3/10
307/307 [==============================] - 244s 795ms/step - loss: 0.9184 - tp: 4262.0000 - fp: 1802.0000 - tn: 27649.0000 - fn: 5555.0000 - accuracy: 0.5994 - precision: 0.7028 - recall: 0.4341 - auc: 0.8488 - prc: 0.6715 - val_loss: 0.8377 - val_tp: 4064.0000 - val_fp: 1476.0000 - val_tn: 22119.0000 - val_fn: 3801.0000 - val_accuracy: 0.6437 - val_precision: 0.7336 - val_recall: 0.5167 - val_auc: 0.8774 - val_prc: 0.7299
Epoch 4/10
307/307 [==============================] - 234s 763ms/step - loss: 0.8155 - tp: 5139.0000 - fp: 1865.0000 - tn: 27586.0000 - fn: 4678.0000 - accuracy: 0.6469 - precision: 0.7337 - recall: 0.5235 - auc: 0.8822 - prc: 0.7363 - val_loss: 0.7145 - val_tp: 4791.0000 - val_fp: 1486.0000 - val_tn: 22109.0000 - val_fn: 3074.0000 - val_accuracy: 0.6941 - val_precision: 0.7633 - val_recall: 0.6092 - val_auc: 0.9105 - val_prc: 0.7961
Epoch 5/10
307/307 [==============================] - 237s 773ms/step - loss: 0.7253 - tp: 5798.0000 - fp: 1812.0000 - tn: 27639.0000 - fn: 4019.0000 - accuracy: 0.6902 - precision: 0.7619 - recall: 0.5906 - auc: 0.9074 - prc: 0.7875 - val_loss: 0.6365 - val_tp: 5219.0000 - val_fp: 1400.0000 - val_tn: 22195.0000 - val_fn: 2646.0000 - val_accuracy: 0.7362 - val_precision: 0.7885 - val_recall: 0.6636 - val_auc: 0.9292 - val_prc: 0.8344
Epoch 6/10
307/307 [==============================] - 242s 789ms/step - loss: 0.6383 - tp: 6470.0000 - fp: 1721.0000 - tn: 27730.0000 - fn: 3347.0000 - accuracy: 0.7332 - precision: 0.7899 - recall: 0.6591 - auc: 0.9286 - prc: 0.8322 - val_loss: 0.6005 - val_tp: 5485.0000 - val_fp: 1401.0000 - val_tn: 22194.0000 - val_fn: 2380.0000 - val_accuracy: 0.7533 - val_precision: 0.7965 - val_recall: 0.6974 - val_auc: 0.9371 - val_prc: 0.8506
Epoch 7/10
307/307 [==============================] - 242s 788ms/step - loss: 0.5609 - tp: 7001.0000 - fp: 1562.0000 - tn: 27889.0000 - fn: 2816.0000 - accuracy: 0.7712 - precision: 0.8176 - recall: 0.7132 - auc: 0.9449 - prc: 0.8669 - val_loss: 0.5107 - val_tp: 5954.0000 - val_fp: 1278.0000 - val_tn: 22317.0000 - val_fn: 1911.0000 - val_accuracy: 0.7898 - val_precision: 0.8233 - val_recall: 0.7570 - val_auc: 0.9541 - val_prc: 0.8882
Epoch 8/10
307/307 [==============================] - 245s 797ms/step - loss: 0.4748 - tp: 7506.0000 - fp: 1395.0000 - tn: 28056.0000 - fn: 2311.0000 - accuracy: 0.8082 - precision: 0.8433 - recall: 0.7646 - auc: 0.9605 - prc: 0.9027 - val_loss: 0.4831 - val_tp: 6182.0000 - val_fp: 1248.0000 - val_tn: 22347.0000 - val_fn: 1683.0000 - val_accuracy: 0.8095 - val_precision: 0.8320 - val_recall: 0.7860 - val_auc: 0.9598 - val_prc: 0.9022
Epoch 9/10
307/307 [==============================] - 240s 782ms/step - loss: 0.3949 - tp: 8014.0000 - fp: 1199.0000 - tn: 28252.0000 - fn: 1803.0000 - accuracy: 0.8442 - precision: 0.8699 - recall: 0.8163 - auc: 0.9722 - prc: 0.9307 - val_loss: 0.4707 - val_tp: 6357.0000 - val_fp: 1213.0000 - val_tn: 22382.0000 - val_fn: 1508.0000 - val_accuracy: 0.8226 - val_precision: 0.8398 - val_recall: 0.8083 - val_auc: 0.9632 - val_prc: 0.9109
Epoch 10/10
307/307 [==============================] - 237s 771ms/step - loss: 0.3336 - tp: 8348.0000 - fp: 1051.0000 - tn: 28400.0000 - fn: 1469.0000 - accuracy: 0.8707 - precision: 0.8882 - recall: 0.8504 - auc: 0.9796 - prc: 0.9484 - val_loss: 0.5240 - val_tp: 6415.0000 - val_fp: 1277.0000 - val_tn: 22318.0000 - val_fn: 1450.0000 - val_accuracy: 0.8234 - val_precision: 0.8340 - val_recall: 0.8156 - val_auc: 0.9597 - val_prc: 0.9036
eva = model.evaluate(test_ds)
print("\n模型的辨认准确率为：", eva[5])
61/61 [==============================] - 12s 197ms/step - loss: 0.5483 - tp: 1551.0000 - fp: 357.0000 - tn: 5499.0000 - fn: 401.0000 - accuracy: 0.8038 - precision: 0.8129 - recall: 0.7946 - auc: 0.9557 - prc: 0.8932
模型的辨认准确率为： 0.8037909865379333
四、模型评价
eva = model.evaluate(test_ds)
print("\n模型的辨认准确率为：", eva[5])
61/61 [==============================] - 12s 196ms/step - loss: 0.5606 - tp: 1541.0000 - fp: 365.0000 - tn: 5491.0000 - fn: 411.0000 - accuracy: 0.7992 - precision: 0.8085 - recall: 0.7894 - auc: 0.9541 - prc: 0.8905
模型的辨认准确率为： 0.7991803288459778
1. Accuracy与Loss图
"""
关于Matplotlib画图的内容能够参阅我的专栏《Matplotlib实例教程》
专栏地址：https://blog.csdn.net/qq_38251616/category_11351625.html
"""
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.suptitle('关注微信大众号（K同学啊）获取源码')
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()
[外链图片转存失败,源站可能有防盗链机制,主张将图片保存下来直接上传(img-7YNLEnEK-1688087191069)(output_22_0.png)]
2. 混杂矩阵
from sklearn.metrics import confusion_matrix
import seaborn as sns
import pandas as pd
# 定义一个绘制混杂矩阵图的函数
def plot_cm(labels, predictions):
# 生成混杂矩阵
conf_numpy = confusion_matrix(labels, predictions)
# 将矩阵转化为 DataFrame
conf_df = pd.DataFrame(conf_numpy, index=class_names ,columns=class_names)
plt.figure(figsize=(8,7))
plt.suptitle('关注微信大众号（K同学啊）获取源码')
sns.heatmap(conf_df, annot=True, fmt="d", cmap="icefire_r")
plt.title('混杂矩阵',fontsize=15)
plt.ylabel('实在值',fontsize=14)
plt.xlabel('猜测值',fontsize=14)
val_pre = []
val_label = []
for images, labels in val_ds.take(1):#这儿能够取部分验证数据（.take(1)）生成混杂矩阵
for image, label in zip(images, labels):
# 需要给图片添加一个维度
img_array = tf.expand_dims(image, 0)
# 运用模型猜测图片中的人物
prediction = model.predict(img_array)
val_pre.append(np.argmax(prediction))
val_label.append([np.argmax(one_hot) for one_hot in [label]][0])
plot_cm(val_label, val_pre)
3. Loss/Precision/Recall/prc
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
plt.figure(figsize=(12,8))
def plot_metrics(history):
metrics = ['loss', 'prc', 'precision', 'recall']
for n, metric in enumerate(metrics):
name = metric.replace("_"," ").capitalize()
plt.subplot(2,2,n+1)
plt.plot(history.epoch, history.history[metric], color=colors[1], label='Train')
plt.plot(history.epoch, history.history['val_'+metric],color=colors[2],
linestyle="--", label='Val')
plt.xlabel('Epoch',fontsize=12)
plt.ylabel(name,fontsize=12)
plt.legend()
plot_metrics(history)
4. ROC曲线
关于ROC曲线的更多内容能够看我这篇文章：blog.csdn.net/qq_38251616…
关于ROC曲线应用的其他实例：
- 深度学习100例 | 第35天：脑肿瘤辨认
import sklearn
from tensorflow.keras.utils import to_categorical
def plot_roc( labels, predictions):
fpr = dict()
tpr = dict()
roc_auc = dict()
temp = class_names
for i, item in enumerate(temp):
fpr[i], tpr[i], _ = sklearn.metrics.roc_curve(labels[:, i], predictions[:, i])
roc_auc[i] = sklearn.metrics.auc(fpr[i], tpr[i])
plt.subplots(figsize=(7, 7))
for i, item in enumerate(temp):
plt.plot(100*fpr[i], 100*tpr[i], label=f'ROC curve {item} (area = {roc_auc[i]:.2f})', linewidth=2, linestyle="--")
plt.xlabel('False positives [%]',fontsize=12)
plt.ylabel('True positives [%]',fontsize=12)
plt.legend(loc="lower right")
plt.grid(True)
# 调用函数
plot_roc(to_categorical(val_label), to_categorical(val_pre))
