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TensorFlow识别4种天气状态（CNN，模型ACC：93.78%）
活动地址：CSDN21天学习挑战赛
1.数据处理
数据链接：传送门（提取码：hqij ）
设置GPU环境
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")- 1
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导入数据
import matplotlib.pyplot as plt import os,PIL # 设置随机种子尽可能使结果可以重现 import numpy as np np.random.seed(1) # 设置随机种子尽可能使结果可以重现 import tensorflow as tf tf.random.set_seed(1) from tensorflow import keras from tensorflow.keras import layers,models import pathlib- 1
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设置数据地址
data_dir = "E:\demo_study\jupyter\Jupyter_notebook\Weather-recognition-based-on-CNN\weather_photos" data_dir = pathlib.Path(data_dir)- 1
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查看数据
数据集一共分为
cloudy、rain、shine、sunrise四类，分别存放于weather_photos文件夹中以各自名字命名的子文件夹中查看图片总数：
image_count = len(list(data_dir.glob('*/*.jpg'))) print("图片总数为：",image_count)- 1
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输出：
图片总数为： 1125- 1
查看第一张图片：
roses = list(data_dir.glob('sunrise/*.jpg')) PIL.Image.open(str(roses[0]))- 1
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输出：
1.1.数据预处理
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中设置数据参数：
batch_size = 64 img_height = 180 img_width = 180- 1
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加载训练集数据：
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)- 1
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输出：
Found 1125 files belonging to 4 classes. Using 900 files for training.- 1
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加载验证集数据：
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)- 1
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输出：
Found 1125 files belonging to 4 classes. Using 225 files for validation.- 1
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通过
class_names输出数据集的标签，标签按字母顺序对应于目录名称：class_names = train_ds.class_names print(class_names)- 1
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输出：
['cloudy', 'rain', 'shine', 'sunrise']- 1
查看
train_ds的数据类型：train_ds- 1
输出：
<PrefetchDataset element_spec=(TensorSpec(shape=(None, 180, 180, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>- 1
1.2.数据可视化
# 每次画的图不一样 plt.figure(figsize=(12, 10)) for images, labels in train_ds.take(1): for i in range(30): ax = plt.subplot(5, 6, i + 1) plt.imshow(images[i].numpy().astype("uint8")) plt.title(class_names[labels[i]]) plt.savefig('pic1.jpg', dpi=600) #指定分辨率保存 plt.axis("off")- 1
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输出：
查看图片形状：
for image_batch, labels_batch in train_ds: print(image_batch.shape) print(labels_batch.shape) break- 1
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Image_batch是形状的张量（32,180,180,3）。这是一批形状180x180x3的32张图片（最后一维指的是彩色通道RGB）。Label_batch是形状（32，）的张量，这些标签对应32张图片
输出：
(64, 180, 180, 3) (64,)- 1
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1.3.配置数据集
shuffle()： 打乱数据，详细可参考：数据集shuffle方法中buffer_size的理解prefetch()：预取数据，加速运行，详细可参考：Better performance with the tf.data APIcache()：将数据集缓存到内存当中，加速运行推荐一篇博客：【学习笔记】使用tf.data对预处理过程优化
prefetch()功能详细介绍：它使得训练步骤的预处理和模型执行部分重叠起来，原来是：
prefetch()之后是：
当然，不这么处理也可以的AUTOTUNE = tf.data.AUTOTUNE train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE) val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)- 1
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2.构建CNN网络
卷积神经网络（CNN）的输入是张量 (Tensor) 形式的：
(image_height, image_width, color_channels)，包含了图像高度、宽度及颜色信息（color_channels为(R,G,B)，分别对应 RGB 的三个颜色通道）首先通过
layers.experimental.preprocessing.Rescaling对图像进行处理（官方解释：rescales and offsets the values of a batch of image (e.g. go from inputs in the[0, 255]range to inputs in the[0, 1]range）函数详细介绍可看：tf.keras.layers.Rescaling
设置
scale=1./255，可将[0, 255]范围内的输入重新缩放为[0, 1]范围内
参数input_shape指明图像的大小格式2.1.池化层
池化层介绍：
在CNN中通常会在相邻的卷积层之间加入一个池化层，池化层可以有效的缩小参数矩阵的尺寸，从而减少最后连接层的中的参数数量
常见的2类池化层可看下图（借鉴自知乎：传送门）：
池化层的作用：
- 缩小特征图一个显而易见的好处是减少参数量，降维、去除冗余信息、对特征进行压缩、简化网络复杂度、减小计算量、减小内存消耗
- 池化最重要的一个作用是扩大神经元，也就是特征图中一个像素的感受野大小。 在浅层卷积中，特征图还很大，一个像素能接收到的实际图像面积很小。虽然逐层的卷积一定程度上能够沟通相邻的神经元，但作用有限。而池化，通过简单粗暴地合并相邻的若干个神经元，使缩小后的特征图上的神经元能够获得来自原图像更大范围的信息，从而提取出更高阶的特征
- 平均池化和最大池化分别代表了合并相邻神经元信息的两种策略。平均池化能够更好地保留所有神经元的信息，能够体现出对应区域的平均响应情况；而最大池化则能够保留细节，保留对应区域的最大响应，防止强烈的响应被周围的神经元弱化。
这里用的是平均池化层（官方可看：tf.keras.layers.AveragePooling2D），其模板为：
tf.keras.layers.AveragePooling2D( pool_size=(2, 2), strides=None, padding='valid', data_format=None, **kwargs )- 1
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在这里我们默认填充形状为
valid（有效），此时不添加填充，结果输出的形状是： ⌊ i n p u t _ s h a p e − p o o l _ s i z e s t r i d e s ⌋ + 1 ( i n p u t _ s h a p e ≥ p o o l _ s i z e ) \lfloor \dfrac{input\_shape-pool\_size}{strides}\rfloor +1( input\_shape\geq pool\_size) ⌊stridesinput_shape−pool_size⌋+1(input_shape≥pool_size)如果填充形状为
same（有效），此时将添加填充，结果输出的形状是：（特别地，如果跨度为1，则输出形状与输入形状相同） ⌊ i n p u t _ s h a p e − 1 s t r i d e s ⌋ + 1 \lfloor \dfrac{input\_shape-1}{strides}\rfloor +1 ⌊stridesinput_shape−1⌋+12.2.卷积层
输入图片矩阵 I I I大小： w × w w\times w w×w
卷积核 K K K大小： k × k k\times k k×k
步长 S S S大小： s s s
填充 P P P大小： p p p卷积输出大小的计算公式为： o = ( w − k + 2 p ) s + 1 o=\dfrac{\left( w-k+2p\right) }{s}+1 o=s(w−k+2p)+1
可以用
tf.keras.layers.Conv2D()（官方可看：tf.keras.layers.Conv2D）构造卷积核，代码结构为：tf.keras.layers.Conv2D( filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), groups=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs )- 1
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这里网络构造了3个3x3的卷积核，使用
relu激活函数num_classes = 4 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.Conv2D(64, (3, 3), activation='relu'), # 卷积层3，卷积核3*3 layers.Dropout(0.3), # 防止过拟合，提高模型泛化能力 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(num_classes) # 输出层，输出预期结果 ]) model.summary() # 打印网络结构- 1
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输出如下：
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= rescaling (Rescaling) (None, 180, 180, 3) 0 conv2d (Conv2D) (None, 178, 178, 16) 448 average_pooling2d (AverageP (None, 89, 89, 16) 0 ooling2D) conv2d_1 (Conv2D) (None, 87, 87, 32) 4640 average_pooling2d_1 (Averag (None, 43, 43, 32) 0 ePooling2D) conv2d_2 (Conv2D) (None, 41, 41, 64) 18496 dropout (Dropout) (None, 41, 41, 64) 0 flatten (Flatten) (None, 107584) 0 dense (Dense) (None, 128) 13770880 dense_1 (Dense) (None, 4) 516 ================================================================= Total params: 13,794,980 Trainable params: 13,794,980 Non-trainable params: 0 _________________________________________________________________- 1
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2.3.编译设置
- 损失函数（loss）：用于衡量模型在训练期间的准确率，这里用
sparse_categorical_crossentropy，原理与categorical_crossentropy（多类交叉熵损失 ）一样，不过真实值采用的整数编码（例如第0个类用数字0表示，第3个类用数字3表示，官方可看：tf.keras.losses.SparseCategoricalCrossentropy） - 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，这里是
Adam（官方可看：tf.keras.optimizers.Adam） - 评价函数（metrics）：用于监控训练和测试步骤，本次使用
accuracy，即被正确分类的图像的比率（官方可看：tf.keras.metrics.Accuracy）
# 设置优化器 opt = tf.keras.optimizers.Adam(learning_rate=0.001) model.compile(optimizer=opt, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])- 1
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2.4.模型训练
epochs = 30 history = model.fit( train_ds, validation_data=val_ds, epochs=epochs )- 1
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这里 s t e p step step（ i t e r a t i o n iteration iteration）的个数为： s t e p = ⌈ e x a m p l e N u m s ∗ e p o c h b a t c h s i z e ⌉ = ⌈ ( 1125 − 225 ) ∗ 1 64 ⌉ = ⌈ 14.0625 ⌉ = 15 step=\lceil \dfrac{exampleNums∗epoch }{batch size}\rceil=\lceil \dfrac{(1125-225)∗1}{64}\rceil=\lceil 14.0625\rceil=15 step=⌈batchsizeexampleNums∗epoch⌉=⌈64(1125−225)∗1⌉=⌈14.0625⌉=15
输出：
Epoch 1/30 15/15 [==============================] - 12s 293ms/step - loss: 1.3749 - accuracy: 0.5144 - val_loss: 0.6937 - val_accuracy: 0.6533 Epoch 2/30 15/15 [==============================] - 2s 144ms/step - loss: 0.6076 - accuracy: 0.7800 - val_loss: 0.4826 - val_accuracy: 0.7956 Epoch 3/30 15/15 [==============================] - 2s 144ms/step - loss: 0.3909 - accuracy: 0.8589 - val_loss: 0.4557 - val_accuracy: 0.7911 Epoch 4/30 15/15 [==============================] - 2s 145ms/step - loss: 0.2943 - accuracy: 0.8878 - val_loss: 0.4268 - val_accuracy: 0.8356 Epoch 5/30 15/15 [==============================] - 2s 144ms/step - loss: 0.2307 - accuracy: 0.9056 - val_loss: 0.4260 - val_accuracy: 0.8400 Epoch 6/30 15/15 [==============================] - 2s 144ms/step - loss: 0.2000 - accuracy: 0.9267 - val_loss: 0.3143 - val_accuracy: 0.8711 Epoch 7/30 15/15 [==============================] - 2s 144ms/step - loss: 0.1496 - accuracy: 0.9367 - val_loss: 0.3277 - val_accuracy: 0.8844 Epoch 8/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0894 - accuracy: 0.9678 - val_loss: 0.2851 - val_accuracy: 0.9200 Epoch 9/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0638 - accuracy: 0.9800 - val_loss: 0.4995 - val_accuracy: 0.8578 Epoch 10/30 15/15 [==============================] - 2s 145ms/step - loss: 0.1132 - accuracy: 0.9622 - val_loss: 0.4961 - val_accuracy: 0.8356 Epoch 11/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0576 - accuracy: 0.9800 - val_loss: 0.3318 - val_accuracy: 0.8844 Epoch 12/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0417 - accuracy: 0.9878 - val_loss: 0.5433 - val_accuracy: 0.8756 Epoch 13/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0301 - accuracy: 0.9944 - val_loss: 0.3797 - val_accuracy: 0.8978 Epoch 14/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0401 - accuracy: 0.9833 - val_loss: 0.3982 - val_accuracy: 0.8489 Epoch 15/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0260 - accuracy: 0.9922 - val_loss: 0.4777 - val_accuracy: 0.8844 Epoch 16/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0139 - accuracy: 0.9978 - val_loss: 0.3858 - val_accuracy: 0.8978 Epoch 17/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0067 - accuracy: 0.9989 - val_loss: 0.3942 - val_accuracy: 0.9156 Epoch 18/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0059 - accuracy: 0.9989 - val_loss: 0.4101 - val_accuracy: 0.8844 Epoch 19/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.5176 - val_accuracy: 0.8889 Epoch 20/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0068 - accuracy: 0.9989 - val_loss: 0.3836 - val_accuracy: 0.9156 Epoch 21/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0116 - accuracy: 0.9989 - val_loss: 0.4635 - val_accuracy: 0.8800 Epoch 22/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0062 - accuracy: 0.9989 - val_loss: 0.4315 - val_accuracy: 0.9022 Epoch 23/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.5728 - val_accuracy: 0.8933 Epoch 24/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0090 - accuracy: 0.9989 - val_loss: 0.5049 - val_accuracy: 0.8889 Epoch 25/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0210 - accuracy: 0.9911 - val_loss: 0.5712 - val_accuracy: 0.8756 Epoch 26/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0354 - accuracy: 0.9878 - val_loss: 0.6332 - val_accuracy: 0.8889 Epoch 27/30 15/15 [==============================] - 2s 146ms/step - loss: 0.0781 - accuracy: 0.9789 - val_loss: 0.5726 - val_accuracy: 0.8578 Epoch 28/30 15/15 [==============================] - 2s 144ms/step - loss: 0.0713 - accuracy: 0.9767 - val_loss: 0.5084 - val_accuracy: 0.8889 Epoch 29/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0439 - accuracy: 0.9822 - val_loss: 0.5302 - val_accuracy: 0.8711 Epoch 30/30 15/15 [==============================] - 2s 145ms/step - loss: 0.0343 - accuracy: 0.9878 - val_loss: 0.4488 - val_accuracy: 0.8933- 1
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保存模型：
model.save('model')- 1
输出日志：
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: model\assets INFO:tensorflow:Assets written to: model\assets- 1
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3.模型评估（acc：92.00%）
model = tf.keras.models.load_model('model') 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, 5)) plt.subplot(1, 2, 1) plt.plot(epochs_range, acc, label='Training Accuracy') plt.plot(epochs_range, val_acc, label='Validation Accuracy') plt.legend() 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() plt.title('Training and Validation Loss') plt.savefig('pic2.jpg', dpi=600) #指定分辨率保存 plt.show()- 1
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输出：
4.模型优化（acc：93.78%）
优化思路：
Training loss不断下降，Validation loss趋于不变，说明网络过拟合，我们尝试将网络结构设置的更复杂一些，学习率调小一些4.1.优化网络
num_classes = 4 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)), layers.Conv2D(64, (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.Conv2D(128, (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(256, activation='relu'), # 全连接层，特征进一步提取 layers.Dense(128, activation='relu'), # 全连接层，特征进一步提取 layers.Dense(num_classes) # 输出层，输出预期结果 ]) model.summary() # 打印网络结构- 1
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输出：
Model: "sequential_7" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= rescaling_7 (Rescaling) (None, 180, 180, 3) 0 conv2d_25 (Conv2D) (None, 178, 178, 16) 448 average_pooling2d_18 (Avera (None, 89, 89, 16) 0 gePooling2D) conv2d_26 (Conv2D) (None, 87, 87, 32) 4640 average_pooling2d_19 (Avera (None, 43, 43, 32) 0 gePooling2D) conv2d_27 (Conv2D) (None, 41, 41, 64) 18496 average_pooling2d_20 (Avera (None, 20, 20, 64) 0 gePooling2D) conv2d_28 (Conv2D) (None, 18, 18, 128) 73856 dropout_8 (Dropout) (None, 18, 18, 128) 0 flatten_7 (Flatten) (None, 41472) 0 dense_16 (Dense) (None, 256) 10617088 dense_17 (Dense) (None, 128) 32896 dense_18 (Dense) (None, 4) 516 ================================================================= Total params: 10,747,940 Trainable params: 10,747,940 Non-trainable params: 0 _________________________________________________________________- 1
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4.2.优化学习率
# 设置优化器 opt = tf.keras.optimizers.Adam(learning_rate=0.0008) model.compile(optimizer=opt, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])- 1
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训练模型：
epochs = 50 history = model.fit( train_ds, validation_data=val_ds, epochs=epochs )- 1
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输出：
Epoch 1/50 15/15 [==============================] - 2s 131ms/step - loss: 1.0564 - accuracy: 0.4989 - val_loss: 0.7628 - val_accuracy: 0.5689 Epoch 2/50 15/15 [==============================] - 2s 123ms/step - loss: 0.7389 - accuracy: 0.6589 - val_loss: 0.8257 - val_accuracy: 0.6711 Epoch 3/50 15/15 [==============================] - 2s 126ms/step - loss: 0.5849 - accuracy: 0.7667 - val_loss: 0.5710 - val_accuracy: 0.7422 Epoch 4/50 15/15 [==============================] - 2s 125ms/step - loss: 0.4292 - accuracy: 0.8389 - val_loss: 0.6052 - val_accuracy: 0.7689 Epoch 5/50 15/15 [==============================] - 2s 140ms/step - loss: 0.3802 - accuracy: 0.8511 - val_loss: 0.7504 - val_accuracy: 0.7556 Epoch 6/50 15/15 [==============================] - 2s 123ms/step - loss: 0.3367 - accuracy: 0.8667 - val_loss: 0.4836 - val_accuracy: 0.8311 Epoch 7/50 15/15 [==============================] - 2s 124ms/step - loss: 0.2773 - accuracy: 0.8889 - val_loss: 0.3823 - val_accuracy: 0.8622 Epoch 8/50 15/15 [==============================] - 2s 125ms/step - loss: 0.2457 - accuracy: 0.9067 - val_loss: 0.3668 - val_accuracy: 0.8622 Epoch 9/50 15/15 [==============================] - 2s 126ms/step - loss: 0.2333 - accuracy: 0.9144 - val_loss: 0.4030 - val_accuracy: 0.8489 Epoch 10/50 15/15 [==============================] - 2s 124ms/step - loss: 0.2526 - accuracy: 0.9089 - val_loss: 0.6440 - val_accuracy: 0.8044 Epoch 11/50 15/15 [==============================] - 2s 123ms/step - loss: 0.2331 - accuracy: 0.9122 - val_loss: 0.4930 - val_accuracy: 0.8444 Epoch 12/50 15/15 [==============================] - 2s 126ms/step - loss: 0.1934 - accuracy: 0.9311 - val_loss: 0.3481 - val_accuracy: 0.8844 Epoch 13/50 15/15 [==============================] - 2s 124ms/step - loss: 0.1471 - accuracy: 0.9367 - val_loss: 0.3174 - val_accuracy: 0.9022 Epoch 14/50 15/15 [==============================] - 2s 122ms/step - loss: 0.1141 - accuracy: 0.9656 - val_loss: 0.4393 - val_accuracy: 0.8578 Epoch 15/50 15/15 [==============================] - 2s 125ms/step - loss: 0.0878 - accuracy: 0.9700 - val_loss: 0.4360 - val_accuracy: 0.8978 Epoch 16/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0718 - accuracy: 0.9744 - val_loss: 0.3478 - val_accuracy: 0.8978 Epoch 17/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0561 - accuracy: 0.9844 - val_loss: 0.3561 - val_accuracy: 0.9200 Epoch 18/50 15/15 [==============================] - 2s 124ms/step - loss: 0.1152 - accuracy: 0.9544 - val_loss: 0.3755 - val_accuracy: 0.9111 Epoch 19/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0642 - accuracy: 0.9778 - val_loss: 0.3634 - val_accuracy: 0.8978 Epoch 20/50 15/15 [==============================] - 2s 124ms/step - loss: 0.0347 - accuracy: 0.9922 - val_loss: 0.3544 - val_accuracy: 0.8978 Epoch 21/50 15/15 [==============================] - 2s 132ms/step - loss: 0.0432 - accuracy: 0.9844 - val_loss: 0.7549 - val_accuracy: 0.8222 Epoch 22/50 15/15 [==============================] - 2s 124ms/step - loss: 0.0641 - accuracy: 0.9811 - val_loss: 0.4202 - val_accuracy: 0.8933 Epoch 23/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0295 - accuracy: 0.9900 - val_loss: 0.4618 - val_accuracy: 0.9200 Epoch 24/50 15/15 [==============================] - 2s 124ms/step - loss: 0.0131 - accuracy: 0.9978 - val_loss: 0.4210 - val_accuracy: 0.9067 Epoch 25/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0172 - accuracy: 0.9944 - val_loss: 0.4878 - val_accuracy: 0.8978 Epoch 26/50 15/15 [==============================] - 2s 125ms/step - loss: 0.0086 - accuracy: 0.9978 - val_loss: 0.4908 - val_accuracy: 0.9111 Epoch 27/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0096 - accuracy: 0.9967 - val_loss: 0.5744 - val_accuracy: 0.8978 Epoch 28/50 15/15 [==============================] - 2s 122ms/step - loss: 0.0058 - accuracy: 0.9989 - val_loss: 0.5868 - val_accuracy: 0.8889 Epoch 29/50 15/15 [==============================] - 2s 126ms/step - loss: 0.0196 - accuracy: 0.9911 - val_loss: 0.5722 - val_accuracy: 0.8578 Epoch 30/50 15/15 [==============================] - 2s 126ms/step - loss: 0.0108 - accuracy: 0.9967 - val_loss: 0.5498 - val_accuracy: 0.9022 Epoch 31/50 15/15 [==============================] - 2s 125ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.5006 - val_accuracy: 0.9111 Epoch 32/50 15/15 [==============================] - 2s 124ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.5010 - val_accuracy: 0.9067 Epoch 33/50 15/15 [==============================] - 2s 130ms/step - loss: 0.0067 - accuracy: 0.9967 - val_loss: 0.5418 - val_accuracy: 0.9156 Epoch 34/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0092 - accuracy: 0.9967 - val_loss: 0.6289 - val_accuracy: 0.9067 Epoch 35/50 15/15 [==============================] - 2s 122ms/step - loss: 0.0152 - accuracy: 0.9933 - val_loss: 0.5908 - val_accuracy: 0.8978 Epoch 36/50 15/15 [==============================] - 2s 123ms/step - loss: 0.0359 - accuracy: 0.9911 - val_loss: 0.5404 - val_accuracy: 0.8756 Epoch 37/50 15/15 [==============================] - 2s 124ms/step - loss: 0.0492 - accuracy: 0.9811 - val_loss: 0.5009 - val_accuracy: 0.9022 Epoch 38/50 15/15 [==============================] - 2s 125ms/step - loss: 0.0395 - accuracy: 0.9844 - val_loss: 0.4718 - val_accuracy: 0.9244 Epoch 39/50 15/15 [==============================] - 2s 125ms/step - loss: 0.0357 - accuracy: 0.9956 - val_loss: 0.6038 - val_accuracy: 0.8844 Epoch 40/50 15/15 [==============================] - 2s 124ms/step - loss: 0.0309 - accuracy: 0.9889 - val_loss: 0.4189 - val_accuracy: 0.9200 Epoch 41/50 15/15 [==============================] - 2s 126ms/step - loss: 0.0093 - accuracy: 0.9989 - val_loss: 0.5180 - val_accuracy: 0.9067 Epoch 42/50 15/15 [==============================] - 2s 125ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.4415 - val_accuracy: 0.9244 Epoch 43/50 15/15 [==============================] - 2s 124ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4622 - val_accuracy: 0.9378 Epoch 44/50 15/15 [==============================] - 2s 125ms/step - loss: 5.7711e-04 - accuracy: 1.0000 - val_loss: 0.4805 - val_accuracy: 0.9333 Epoch 45/50 15/15 [==============================] - 2s 125ms/step - loss: 4.1283e-04 - accuracy: 1.0000 - val_loss: 0.4820 - val_accuracy: 0.9244 Epoch 46/50 15/15 [==============================] - 2s 126ms/step - loss: 3.2792e-04 - accuracy: 1.0000 - val_loss: 0.4859 - val_accuracy: 0.9333 Epoch 47/50 15/15 [==============================] - 2s 127ms/step - loss: 2.7573e-04 - accuracy: 1.0000 - val_loss: 0.4932 - val_accuracy: 0.9289 Epoch 48/50 15/15 [==============================] - 2s 123ms/step - loss: 2.7769e-04 - accuracy: 1.0000 - val_loss: 0.4877 - val_accuracy: 0.9333 Epoch 49/50 15/15 [==============================] - 2s 124ms/step - loss: 2.6387e-04 - accuracy: 1.0000 - val_loss: 0.5107 - val_accuracy: 0.9289 Epoch 50/50 15/15 [==============================] - 2s 122ms/step - loss: 2.1140e-04 - accuracy: 1.0000 - val_loss: 0.4979 - val_accuracy: 0.9378- 1
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可以发现
val_accuracy的最大值在训练的第50次，为93.78%训练过程可视化图：
-
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第六章 图论 16 AcWing 1558. 加油站
- 原文地址：https://blog.csdn.net/qq_45550375/article/details/126325300
