深度学习第三天-卷积神经网络（CNN）：乳腺癌识别
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>- 参考文章地址： >- 本文为[🔗365天深度学习训练营](https://mp.weixin.qq.com/s/k-vYaC8l7uxX51WoypLkTw) 中的学习记录博客
>- 参考文章地址： [🔗深度学习100例-卷积神经网络（CNN）天气识别 | 第5天]()
1.把数据导入linux系统
scp -r D:\87177\5day xx@12233334:../sdxx/k/5day/26
2.解压缩rar文件
unrar x 第26天.rar
3.jupter notebook开始运行
1）设置环境
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")
import matplotlib.pyplot as plt
import os,PIL,pathlib
import numpy as np
import pandas as pd
import warnings
from tensorflow import keras
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
2）导入数据
import pathlib
data_dir = "./26-data"
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*')))
print("",image_count)
得到结果： 13403
参数
batch_size = 16
img_height = 50
img_width = 50
关于image_dataset_from_directory()的详细介绍可以参考文章：**
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=12,
image_size=(img_height, img_width),
batch_size=batch_size)
得到结果：
Found 13403 files belonging to 2 classes.
Using 10723 files for training.
运行：
"""
关于image_dataset_from_directory()的详细介绍可以参考文章：
"""
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=12,
image_size=(img_height, img_width),
batch_size=batch_size)
得到结果：
Found 13403 files belonging to 2 classes.
Using 2680 files for validation.
运行：
class_names = train_ds.class_names
print(class_names)
得到结果：
['0', '1']
3）检查数据
运行：
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
得到结果：
(16, 50, 50, 3)
(16,)
3）配置数据集
shuffle() ： 打乱数据，关于此函数的详细介绍可以参考：https://zhuanlan.zhihu.com/p/42417456
prefetch() ： 预取数据，加速运行，其详细介绍可以参考我前两篇文章，里面都有讲解。
cache() ： 将数据集缓存到内存当中，加速运行
运行：
AUTOTUNE = tf.data.AUTOTUNE
def train_preprocessing(image,label):
return (image/255.0,label)
train_ds = (
train_ds.cache()
.shuffle(1000)
.map(train_preprocessing) # 这里可以设置预处理函数
# .batch(batch_size) # 在image_dataset_from_directory处已经设置了batch_size
.prefetch(buffer_size=AUTOTUNE)
)
val_ds = (
val_ds.cache()
.shuffle(1000)
.map(train_preprocessing) # 这里可以设置预处理函数
# .batch(batch_size) # 在image_dataset_from_directory处已经设置了batch_size
.prefetch(buffer_size=AUTOTUNE)
)
4）数据可视化
plt.figure(figsize=(10, 8)) # 图形的宽为10高为5
plt.suptitle("数据展示")
class_names = ["乳腺癌细胞","正常细胞"]
for images, labels in train_ds.take(1):
for i in range(15):
plt.subplot(4, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
# 显示图片
plt.imshow(images[i])
# 显示标签
plt.xlabel(class_names[labels[i]-1])
plt.show()
4.构建模型
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=16,kernel_size=(3,3),padding="same",activation="relu",input_shape=[img_width, img_height, 3]),
tf.keras.layers.Conv2D(filters=16,kernel_size=(3,3),padding="same",activation="relu"),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Conv2D(filters=16,kernel_size=(3,3),padding="same",activation="relu"),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Conv2D(filters=16,kernel_size=(3,3),padding="same",activation="relu"),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(2, activation="softmax")
])
model.summary()
得到结果：
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 50, 50, 16) 448
_________________________________________________________________
conv2d_1 (Conv2D) (None, 50, 50, 16) 2320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 25, 25, 16) 0
_________________________________________________________________
dropout (Dropout) (None, 25, 25, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 25, 25, 16) 2320
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 12, 12, 16) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 12, 12, 16) 2320
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 16) 0
_________________________________________________________________
flatten (Flatten) (None, 576) 0
_________________________________________________________________
dense (Dense) (None, 2) 1154
=================================================================
Total params: 8,562
Trainable params: 8,562
Non-trainable params: 0
5.编译
model.compile(optimizer="adam",
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
6.训练模型
from tensorflow.keras.callbacks import ModelCheckpoint, Callback, EarlyStopping, ReduceLROnPlateau, LearningRateScheduler
NO_EPOCHS = 100
PATIENCE = 5
VERBOSE = 1
# 设置动态学习率
annealer = LearningRateScheduler(lambda x: 1e-3 * 0.99 ** (x+NO_EPOCHS))
# 设置早停
earlystopper = EarlyStopping(monitor='loss', patience=PATIENCE, verbose=VERBOSE)
#
checkpointer = ModelCheckpoint('best_model.h5',
monitor='val_accuracy',
verbose=VERBOSE,
save_best_only=True,
save_weights_only=True)
train_model = model.fit(train_ds,
epochs=NO_EPOCHS,
verbose=1,
validation_data=val_ds,
callbacks=[earlystopper, checkpointer, annealer])
得到结果：
Epoch 1/100
671/671 [==============================] - 37s 52ms/step - loss: 0.5644 - accuracy: 0.7007 - val_loss: 0.5268 - val_accuracy: 0.7228
Epoch 00001: val_accuracy improved from -inf to 0.72276, saving model to best_model.h5
Epoch 2/100
671/671 [==============================] - 8s 12ms/step - loss: 0.4430 - accuracy: 0.8062 - val_loss: 0.4252 - val_accuracy: 0.8317
Epoch 00002: val_accuracy improved from 0.72276 to 0.83172, saving model to best_model.h5
Epoch 3/100
671/671 [==============================] - 7s 11ms/step - loss: 0.4225 - accuracy: 0.8152 - val_loss: 0.4436 - val_accuracy: 0.8194
Epoch 00003: val_accuracy did not improve from 0.83172
Epoch 4/100
671/671 [==============================] - 7s 11ms/step - loss: 0.4000 - accuracy: 0.8242 - val_loss: 0.3964 - val_accuracy: 0.8358
Epoch 00004: val_accuracy improved from 0.83172 to 0.83582, saving model to best_model.h5
Epoch 5/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3826 - accuracy: 0.8289 - val_loss: 0.3652 - val_accuracy: 0.8403
Epoch 00005: val_accuracy improved from 0.83582 to 0.84030, saving model to best_model.h5
Epoch 6/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3751 - accuracy: 0.8320 - val_loss: 0.4237 - val_accuracy: 0.8209
Epoch 00006: val_accuracy did not improve from 0.84030
Epoch 7/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3658 - accuracy: 0.8345 - val_loss: 0.3552 - val_accuracy: 0.8608
Epoch 00007: val_accuracy improved from 0.84030 to 0.86082, saving model to best_model.h5
Epoch 8/100
671/671 [==============================] - 8s 11ms/step - loss: 0.3589 - accuracy: 0.8398 - val_loss: 0.3421 - val_accuracy: 0.8459
Epoch 00008: val_accuracy did not improve from 0.86082
Epoch 9/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3533 - accuracy: 0.8430 - val_loss: 0.3504 - val_accuracy: 0.8444
Epoch 00009: val_accuracy did not improve from 0.86082
Epoch 10/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3489 - accuracy: 0.8454 - val_loss: 0.3508 - val_accuracy: 0.8493
Epoch 00010: val_accuracy did not improve from 0.86082
Epoch 11/100
671/671 [==============================] - 8s 11ms/step - loss: 0.3446 - accuracy: 0.8463 - val_loss: 0.3326 - val_accuracy: 0.8616
Epoch 00011: val_accuracy improved from 0.86082 to 0.86157, saving model to best_model.h5
Epoch 12/100
671/671 [==============================] - 8s 11ms/step - loss: 0.3467 - accuracy: 0.8491 - val_loss: 0.3850 - val_accuracy: 0.8455
Epoch 00012: val_accuracy did not improve from 0.86157
Epoch 13/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3337 - accuracy: 0.8524 - val_loss: 0.3387 - val_accuracy: 0.8623
Epoch 00013: val_accuracy improved from 0.86157 to 0.86231, saving model to best_model.h5
Epoch 14/100
671/671 [==============================] - 8s 11ms/step - loss: 0.3308 - accuracy: 0.8561 - val_loss: 0.3292 - val_accuracy: 0.8713
Epoch 00014: val_accuracy improved from 0.86231 to 0.87127, saving model to best_model.h5
Epoch 15/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3230 - accuracy: 0.8580 - val_loss: 0.3403 - val_accuracy: 0.8500
Epoch 00015: val_accuracy did not improve from 0.87127
Epoch 16/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3234 - accuracy: 0.8574 - val_loss: 0.3169 - val_accuracy: 0.8840
Epoch 00016: val_accuracy improved from 0.87127 to 0.88396, saving model to best_model.h5
Epoch 17/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3168 - accuracy: 0.8651 - val_loss: 0.3056 - val_accuracy: 0.8787
Epoch 00017: val_accuracy did not improve from 0.88396
Epoch 18/100
671/671 [==============================] - 7s 11ms/step - loss: 0.3090 - accuracy: 0.8673 - val_loss: 0.2904 - val_accuracy: 0.8914
Epoch 00018: val_accuracy improved from 0.88396 to 0.89142, saving model to best_model.h5
Epoch 19/100
671/671 [==============================] - 8s 11ms/step - loss: 0.3090 - accuracy: 0.8650 - val_loss: 0.3056 - val_accuracy: 0.8828
Epoch 00019: val_accuracy did not improve from 0.89142
Epoch 20/100
671/671 [==============================] - 8s 11ms/step - loss: 0.3008 - accuracy: 0.8736 - val_loss: 0.3003 - val_accuracy: 0.8813
Epoch 00020: val_accuracy did not improve from 0.89142
Epoch 21/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2987 - accuracy: 0.8715 - val_loss: 0.3085 - val_accuracy: 0.8840
Epoch 00021: val_accuracy did not improve from 0.89142
Epoch 22/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2965 - accuracy: 0.8723 - val_loss: 0.3309 - val_accuracy: 0.8694
Epoch 00022: val_accuracy did not improve from 0.89142
Epoch 23/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2937 - accuracy: 0.8753 - val_loss: 0.3135 - val_accuracy: 0.8619
Epoch 00023: val_accuracy did not improve from 0.89142
Epoch 24/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2872 - accuracy: 0.8781 - val_loss: 0.3174 - val_accuracy: 0.8664
Epoch 00024: val_accuracy did not improve from 0.89142
Epoch 25/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2827 - accuracy: 0.8802 - val_loss: 0.3107 - val_accuracy: 0.8698
Epoch 00025: val_accuracy did not improve from 0.89142
Epoch 26/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2803 - accuracy: 0.8815 - val_loss: 0.2883 - val_accuracy: 0.8858
Epoch 00026: val_accuracy did not improve from 0.89142
Epoch 27/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2802 - accuracy: 0.8811 - val_loss: 0.3010 - val_accuracy: 0.8746
Epoch 00027: val_accuracy did not improve from 0.89142
Epoch 28/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2787 - accuracy: 0.8832 - val_loss: 0.3022 - val_accuracy: 0.8832
Epoch 00028: val_accuracy did not improve from 0.89142
Epoch 29/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2740 - accuracy: 0.8846 - val_loss: 0.2763 - val_accuracy: 0.8851
Epoch 00029: val_accuracy did not improve from 0.89142
Epoch 30/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2771 - accuracy: 0.8815 - val_loss: 0.2766 - val_accuracy: 0.8951
Epoch 00030: val_accuracy improved from 0.89142 to 0.89515, saving model to best_model.h5
Epoch 31/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2739 - accuracy: 0.8851 - val_loss: 0.2764 - val_accuracy: 0.8914
Epoch 00031: val_accuracy did not improve from 0.89515
Epoch 32/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2676 - accuracy: 0.8858 - val_loss: 0.2646 - val_accuracy: 0.8940
Epoch 00032: val_accuracy did not improve from 0.89515
Epoch 33/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2682 - accuracy: 0.8871 - val_loss: 0.2759 - val_accuracy: 0.8922
Epoch 00033: val_accuracy did not improve from 0.89515
Epoch 34/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2639 - accuracy: 0.8901 - val_loss: 0.3046 - val_accuracy: 0.8757
Epoch 00034: val_accuracy did not improve from 0.89515
Epoch 35/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2645 - accuracy: 0.8896 - val_loss: 0.3199 - val_accuracy: 0.8716
Epoch 00035: val_accuracy did not improve from 0.89515
Epoch 36/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2603 - accuracy: 0.8891 - val_loss: 0.3165 - val_accuracy: 0.8679
Epoch 00036: val_accuracy did not improve from 0.89515
Epoch 37/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2638 - accuracy: 0.8891 - val_loss: 0.3043 - val_accuracy: 0.8791
Epoch 00037: val_accuracy did not improve from 0.89515
Epoch 38/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2596 - accuracy: 0.8933 - val_loss: 0.2878 - val_accuracy: 0.8821
Epoch 00038: val_accuracy did not improve from 0.89515
Epoch 39/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2555 - accuracy: 0.8936 - val_loss: 0.2620 - val_accuracy: 0.8914
Epoch 00039: val_accuracy did not improve from 0.89515
Epoch 40/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2586 - accuracy: 0.8912 - val_loss: 0.2927 - val_accuracy: 0.8791
Epoch 00040: val_accuracy did not improve from 0.89515
Epoch 41/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2558 - accuracy: 0.8912 - val_loss: 0.2908 - val_accuracy: 0.8843
Epoch 00041: val_accuracy did not improve from 0.89515
Epoch 42/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2493 - accuracy: 0.8950 - val_loss: 0.2861 - val_accuracy: 0.8933
Epoch 00042: val_accuracy did not improve from 0.89515
Epoch 43/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2503 - accuracy: 0.8933 - val_loss: 0.2855 - val_accuracy: 0.8869
Epoch 00043: val_accuracy did not improve from 0.89515
Epoch 44/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2483 - accuracy: 0.8942 - val_loss: 0.2745 - val_accuracy: 0.8959
Epoch 00044: val_accuracy improved from 0.89515 to 0.89590, saving model to best_model.h5
Epoch 45/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2497 - accuracy: 0.8967 - val_loss: 0.2590 - val_accuracy: 0.8951
Epoch 00045: val_accuracy did not improve from 0.89590
Epoch 46/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2460 - accuracy: 0.8976 - val_loss: 0.2656 - val_accuracy: 0.8944
Epoch 00046: val_accuracy did not improve from 0.89590
Epoch 47/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2440 - accuracy: 0.8993 - val_loss: 0.2612 - val_accuracy: 0.8955
Epoch 00047: val_accuracy did not improve from 0.89590
Epoch 48/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2409 - accuracy: 0.8979 - val_loss: 0.2798 - val_accuracy: 0.8922
Epoch 00048: val_accuracy did not improve from 0.89590
Epoch 49/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2438 - accuracy: 0.8989 - val_loss: 0.2524 - val_accuracy: 0.8963
Epoch 00049: val_accuracy improved from 0.89590 to 0.89627, saving model to best_model.h5
Epoch 50/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2441 - accuracy: 0.8995 - val_loss: 0.2616 - val_accuracy: 0.8966
Epoch 00050: val_accuracy improved from 0.89627 to 0.89664, saving model to best_model.h5
Epoch 51/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2390 - accuracy: 0.9010 - val_loss: 0.2683 - val_accuracy: 0.8940
Epoch 00051: val_accuracy did not improve from 0.89664
Epoch 52/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2390 - accuracy: 0.9004 - val_loss: 0.2462 - val_accuracy: 0.9007
Epoch 00052: val_accuracy improved from 0.89664 to 0.90075, saving model to best_model.h5
Epoch 53/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2386 - accuracy: 0.8999 - val_loss: 0.3076 - val_accuracy: 0.8769
Epoch 00053: val_accuracy did not improve from 0.90075
Epoch 54/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2363 - accuracy: 0.9024 - val_loss: 0.2433 - val_accuracy: 0.9060
Epoch 00054: val_accuracy improved from 0.90075 to 0.90597, saving model to best_model.h5
Epoch 55/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2314 - accuracy: 0.9050 - val_loss: 0.2610 - val_accuracy: 0.8989
Epoch 00055: val_accuracy did not improve from 0.90597
Epoch 56/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2355 - accuracy: 0.9003 - val_loss: 0.2585 - val_accuracy: 0.8974
Epoch 00056: val_accuracy did not improve from 0.90597
Epoch 57/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2370 - accuracy: 0.9023 - val_loss: 0.2430 - val_accuracy: 0.9022
Epoch 00057: val_accuracy did not improve from 0.90597
Epoch 58/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2298 - accuracy: 0.9063 - val_loss: 0.2604 - val_accuracy: 0.8951
Epoch 00058: val_accuracy did not improve from 0.90597
Epoch 59/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2272 - accuracy: 0.9030 - val_loss: 0.2420 - val_accuracy: 0.9037
Epoch 00059: val_accuracy did not improve from 0.90597
Epoch 60/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2254 - accuracy: 0.9072 - val_loss: 0.2538 - val_accuracy: 0.8959
Epoch 00060: val_accuracy did not improve from 0.90597
Epoch 61/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2300 - accuracy: 0.9058 - val_loss: 0.2689 - val_accuracy: 0.8910
Epoch 00061: val_accuracy did not improve from 0.90597
Epoch 62/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2289 - accuracy: 0.9044 - val_loss: 0.2585 - val_accuracy: 0.8963
Epoch 00062: val_accuracy did not improve from 0.90597
Epoch 63/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2265 - accuracy: 0.9046 - val_loss: 0.2879 - val_accuracy: 0.8832
Epoch 00063: val_accuracy did not improve from 0.90597
Epoch 64/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2304 - accuracy: 0.9039 - val_loss: 0.2587 - val_accuracy: 0.8948
Epoch 00064: val_accuracy did not improve from 0.90597
Epoch 65/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2247 - accuracy: 0.9057 - val_loss: 0.2619 - val_accuracy: 0.8970
Epoch 00065: val_accuracy did not improve from 0.90597
Epoch 66/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2251 - accuracy: 0.9076 - val_loss: 0.2847 - val_accuracy: 0.8851
Epoch 00066: val_accuracy did not improve from 0.90597
Epoch 67/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2239 - accuracy: 0.9091 - val_loss: 0.2899 - val_accuracy: 0.8832
Epoch 00067: val_accuracy did not improve from 0.90597
Epoch 68/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2229 - accuracy: 0.9077 - val_loss: 0.2987 - val_accuracy: 0.8772
Epoch 00068: val_accuracy did not improve from 0.90597
Epoch 69/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2244 - accuracy: 0.9064 - val_loss: 0.2493 - val_accuracy: 0.8981
Epoch 00069: val_accuracy did not improve from 0.90597
Epoch 70/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2209 - accuracy: 0.9077 - val_loss: 0.2572 - val_accuracy: 0.9007
Epoch 00070: val_accuracy did not improve from 0.90597
Epoch 71/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2242 - accuracy: 0.9078 - val_loss: 0.2657 - val_accuracy: 0.8970
Epoch 00071: val_accuracy did not improve from 0.90597
Epoch 72/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2220 - accuracy: 0.9071 - val_loss: 0.2421 - val_accuracy: 0.9075
Epoch 00072: val_accuracy improved from 0.90597 to 0.90746, saving model to best_model.h5
Epoch 73/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2178 - accuracy: 0.9093 - val_loss: 0.2348 - val_accuracy: 0.9067
Epoch 00073: val_accuracy did not improve from 0.90746
Epoch 74/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2178 - accuracy: 0.9106 - val_loss: 0.2572 - val_accuracy: 0.9037
Epoch 00074: val_accuracy did not improve from 0.90746
Epoch 75/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2197 - accuracy: 0.9115 - val_loss: 0.2632 - val_accuracy: 0.8955
Epoch 00075: val_accuracy did not improve from 0.90746
Epoch 76/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2154 - accuracy: 0.9105 - val_loss: 0.2601 - val_accuracy: 0.8959
Epoch 00076: val_accuracy did not improve from 0.90746
Epoch 77/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2186 - accuracy: 0.9080 - val_loss: 0.2433 - val_accuracy: 0.9034
Epoch 00077: val_accuracy did not improve from 0.90746
Epoch 78/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2147 - accuracy: 0.9127 - val_loss: 0.2809 - val_accuracy: 0.8862
Epoch 00078: val_accuracy did not improve from 0.90746
Epoch 79/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2118 - accuracy: 0.9122 - val_loss: 0.2361 - val_accuracy: 0.9041
Epoch 00079: val_accuracy did not improve from 0.90746
Epoch 80/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2122 - accuracy: 0.9105 - val_loss: 0.2469 - val_accuracy: 0.8966
Epoch 00080: val_accuracy did not improve from 0.90746
Epoch 81/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2121 - accuracy: 0.9116 - val_loss: 0.2430 - val_accuracy: 0.9026
Epoch 00081: val_accuracy did not improve from 0.90746
Epoch 82/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2135 - accuracy: 0.9132 - val_loss: 0.2306 - val_accuracy: 0.9082
Epoch 00082: val_accuracy improved from 0.90746 to 0.90821, saving model to best_model.h5
Epoch 83/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2106 - accuracy: 0.9117 - val_loss: 0.2476 - val_accuracy: 0.9082
Epoch 00083: val_accuracy did not improve from 0.90821
Epoch 84/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2083 - accuracy: 0.9156 - val_loss: 0.2607 - val_accuracy: 0.8970
Epoch 00084: val_accuracy did not improve from 0.90821
Epoch 85/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2070 - accuracy: 0.9136 - val_loss: 0.2582 - val_accuracy: 0.8974
Epoch 00085: val_accuracy did not improve from 0.90821
Epoch 86/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2118 - accuracy: 0.9120 - val_loss: 0.3005 - val_accuracy: 0.8735
Epoch 00086: val_accuracy did not improve from 0.90821
Epoch 87/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2082 - accuracy: 0.9126 - val_loss: 0.2911 - val_accuracy: 0.8802
Epoch 00087: val_accuracy did not improve from 0.90821
Epoch 88/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2078 - accuracy: 0.9155 - val_loss: 0.2466 - val_accuracy: 0.9034
Epoch 00088: val_accuracy did not improve from 0.90821
Epoch 89/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2114 - accuracy: 0.9132 - val_loss: 0.2587 - val_accuracy: 0.8989
Epoch 00089: val_accuracy did not improve from 0.90821
Epoch 90/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2057 - accuracy: 0.9152 - val_loss: 0.2813 - val_accuracy: 0.8922
Epoch 00090: val_accuracy did not improve from 0.90821
Epoch 91/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2079 - accuracy: 0.9147 - val_loss: 0.2526 - val_accuracy: 0.9015
Epoch 00091: val_accuracy did not improve from 0.90821
Epoch 92/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2102 - accuracy: 0.9135 - val_loss: 0.2576 - val_accuracy: 0.9026
Epoch 00092: val_accuracy did not improve from 0.90821
Epoch 93/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2083 - accuracy: 0.9162 - val_loss: 0.2506 - val_accuracy: 0.8974
Epoch 00093: val_accuracy did not improve from 0.90821
Epoch 94/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2064 - accuracy: 0.9155 - val_loss: 0.2705 - val_accuracy: 0.8944
Epoch 00094: val_accuracy did not improve from 0.90821
Epoch 95/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2025 - accuracy: 0.9183 - val_loss: 0.2589 - val_accuracy: 0.8981
Epoch 00095: val_accuracy did not improve from 0.90821
Epoch 96/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2015 - accuracy: 0.9181 - val_loss: 0.2549 - val_accuracy: 0.8970
Epoch 00096: val_accuracy did not improve from 0.90821
Epoch 97/100
671/671 [==============================] - 8s 11ms/step - loss: 0.2010 - accuracy: 0.9179 - val_loss: 0.2401 - val_accuracy: 0.9011
Epoch 00097: val_accuracy did not improve from 0.90821
Epoch 98/100
671/671 [==============================] - 7s 11ms/step - loss: 0.1981 - accuracy: 0.9199 - val_loss: 0.2531 - val_accuracy: 0.9007
Epoch 00098: val_accuracy did not improve from 0.90821
Epoch 99/100
671/671 [==============================] - 7s 11ms/step - loss: 0.2016 - accuracy: 0.9163 - val_loss: 0.2463 - val_accuracy: 0.8985
Epoch 00099: val_accuracy did not improve from 0.90821
Epoch 100/100
671/671 [==============================] - 7s 11ms/step - loss: 0.1972 - accuracy: 0.9191 - val_loss: 0.2718 - val_accuracy: 0.8884
Epoch 00100: val_accuracy did not improve from 0.90821
6.评估模型
1）
accuracy于loss图
acc = train_model.history['accuracy']
val_acc = train_model.history['val_accuracy']
loss = train_model.history['loss']
val_loss = train_model.history['val_loss']
epochs_range = range(len(acc))
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()
2.混淆矩阵
此次报错：No module named ‘seaborn’，所以得安装一下：
conda install -c conda-forge seaborn
from sklearn.metrics import confusion_matrix
import seaborn as sns
import pandas as pd
# 定义一个绘制混淆矩阵图的函数
def plot_cm(labels, predictions):
# 生成混淆矩阵
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))
sns.heatmap(conf_df, annot=True, fmt="d", cmap="BuPu")
plt.title('混淆矩阵',fontsize=15)
plt.ylabel('真实值',fontsize=14)
plt.xlabel('预测值',fontsize=14)
val_pre = []
val_label = []
for images, labels in val_ds:#这里可以取部分验证数据（.take(1)）生成混淆矩阵
for image, label in zip(images, labels):
# 需要给图片增加一个维度
img_array = tf.expand_dims(image, 0)
# 使用模型预测图片中的人物
prediction = model.predict(img_array)
val_pre.append(class_names[np.argmax(prediction)])
val_label.append(class_names[label])
plot_cm(val_label, val_pre)
3.各项指标评估
from sklearn import metrics
def test_accuracy_report(model):
print(metrics.classification_report(val_label, val_pre, target_names=class_names))
score = model.evaluate(val_ds, verbose=0)
print('Loss function: %s, accuracy:' % score[0], score[1])
test_accuracy_report(model)
precision recall f1-score support
乳腺癌细胞 0.86 0.92 0.89 1339
正常细胞 0.92 0.86 0.88 1341
accuracy 0.89 2680
macro avg 0.89 0.89 0.89 2680
weighted avg 0.89 0.89 0.89 2680
Loss function: 0.27176907658576965, accuracy: 0.8884328603744507
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