肺炎分类在 TPU 上
作者: Amy MiHyun Jang
创建日期: 2020/07/28
最后修改: 2024/02/12
描述: 在 TPU 上进行医学图像分类。
介绍 + 设置
本教程将解释如何构建一个 X 射线图像分类模型， 以预测 X 射线扫描是否显示肺炎的存在。
import re
import os
import random
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect()
print("设备:", tpu.master())
strategy = tf.distribute.TPUStrategy(tpu)
except:
strategy = tf.distribute.get_strategy()
print("副本数量:", strategy.num_replicas_in_sync)
Device: grpc://10.0.27.122:8470
INFO:tensorflow:正在初始化TPU系统: grpc://10.0.27.122:8470
INFO:tensorflow:正在初始化TPU系统: grpc://10.0.27.122:8470
INFO:tensorflow:清除急切缓存
INFO:tensorflow:清除急切缓存
INFO:tensorflow:完成TPU系统初始化。
INFO:tensorflow:完成TPU系统初始化。
WARNING:absl:[`tf.distribute.TPUStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/TPUStrategy)已弃用，请使用非实验性符号[`tf.distribute.TPUStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/TPUStrategy)。
INFO:tensorflow:发现TPU系统:
INFO:tensorflow:发现TPU系统:
INFO:tensorflow:*** TPU核心数量: 8
INFO:tensorflow:*** TPU核心数量: 8
INFO:tensorflow:*** TPU工作节点数量: 1
INFO:tensorflow:*** TPU工作节点数量: 1
INFO:tensorflow:*** 每个工作节点的TPU核心数量: 8
INFO:tensorflow:*** 每个工作节点的TPU核心数量: 8
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** 可用设备: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
副本数量: 8
---
## 加载数据
我们使用的胸部X光数据来自
[*Cell*](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5)，将数据划分为训练和测试文件。
首先加载训练TFRecords。
让我们计算一下有多少健康/正常的胸部X光片，以及有多少肺炎的胸部X光片：
请注意，肺炎分类的图像数量远多于正常。
这表明我们的数据存在不平衡问题。我们将在后面的笔记本中纠正这一不平衡。
我们希望将每个文件名映射到相应的（图像，标签）对。以下方法将帮助我们做到这一点。
由于我们只有两个标签，我们将对标签进行编码，`1`或`True`表示肺炎，`0`或`False`表示正常。
让我们将数据分成训练集和验证集。
让我们可视化一下（图像，标签）对的形状。
加载并格式化测试数据。
---
## 可视化数据集
首先，让我们使用缓冲预取，以便在不让I/O成为阻塞的情况下从磁盘获取数据。
请注意，大型图像数据集不应缓存到内存中。我们之所以这样做，是因为数据集不是很大，我们希望在TPU上进行训练。
调用训练数据的下一批次迭代。
定义显示批次中图像的方法。
由于该方法接受NumPy数组作为参数，因此在批次上调用numpy函数以返回以NumPy数组形式的张量。
---
## 构建CNN
为了使我们的模型更加模块化和易于理解，让我们定义一些块。由于我们正在构建卷积神经网络，因此我们将创建一个卷积块和一个全连接层块。
这个CNN的架构受到了这篇
[文章](https://towardsdatascience.com/deep-learning-for-detecting-pneumonia-from-x-ray-images-fc9a3d9fdba8)的启发。
以下方法将为我们定义构建模型的函数。
图像的原始值范围在[0, 255]之间。CNN在处理较小的数字时效果更佳，因此我们将对输入进行缩放。
Dropout层很重要，因为它们减少了模型过拟合的可能性。我们希望以一个带有一个节点的`Dense`层结束模型，因为这将是用于判断X光片是否显示肺炎的二元输出。
---
## 修正数据不平衡
我们在这个例子中早些时候看到数据不平衡，肺炎分类的图像数量多于正常图像。我们将通过使用类权重来纠正这一点：
类`0`（正常）的权重远高于类`1`（肺炎）的权重。由于正常图像较少，因此每个正常图像的权重会更高，以平衡数据，因为CNN在训练数据平衡时效果最佳。
---
## 训练模型
### 定义回调函数
检查点回调保存模型的最佳权重，因此下次我们想使用模型时，不必花时间训练它。早停回调在模型开始停滞时停止训练过程，甚至在模型开始过拟合时停止。
我们还希望调整我们的学习率。学习率过高将导致模型发散。学习率过低会导致模型速度过慢。我们在下面实现了指数学习率调度方法。
### 适配模型
对于我们的指标，我们希望包括精确度和召回率，因为它们将为我们提供关于模型效果的更全面的信息。准确率告诉我们标签的正确比例。由于我们的数据不平衡，准确率可能给出关于良好模型的偏差感觉（即，一个总是预测肺炎的模型将准确率为74%，但并不是一个好的模型）。
精确度是真阳性（TP）与真阳性和假阳性的总和之比。
(FP). 它显示标记为正的样本中实际正确的比例。
召回率是 TP 与 TP 和假阴性 (FN) 之和的比值。它显示实际正样本中正确的比例。
由于图像只有两种可能的标签，因此我们将使用二元交叉熵损失。当我们拟合模型时，请记得指定类权重，这些权重我们之前定义过。因为我们使用的是 TPU，所以训练会很快 - 少于 2 分钟。
---
## 可视化模型性能
让我们绘制模型在训练集和验证集上的准确率和损失。请注意，当前笔记本未指定随机种子。在您的笔记本中，可能会有轻微的差异。
我们看到模型的准确率约为95%。
---
## 预测和评估结果
让我们在测试数据上评估模型！
我们看到在测试数据上的准确率低于验证集的准确率。这可能表明模型过拟合。
我们的召回率高于精确率，这表明几乎所有的肺炎图像都被正确识别，但一些正常图像被错误识别。我们应该努力提高我们的精确率。
</div>
我们需要一个Google Cloud链接来加载数据，以便使用TPU。
下面，我们定义将在本示例中使用的关键配置参数。
要在TPU上运行，此示例必须在选择了TPU运行时的Colab中。
```python
AUTOTUNE = tf.data.AUTOTUNE
BATCH_SIZE = 25 * strategy.num_replicas_in_sync
IMAGE_SIZE = [180, 180]
CLASS_NAMES = ["正常", "肺炎"]
train_images = tf.data.TFRecordDataset(
"gs://download.tensorflow.org/data/ChestXRay2017/train/images.tfrec"
)
train_paths = tf.data.TFRecordDataset(
"gs://download.tensorflow.org/data/ChestXRay2017/train/paths.tfrec"
)
ds = tf.data.Dataset.zip((train_images, train_paths))
COUNT_NORMAL = len(
[
filename
for filename in train_paths
if "正常" in filename.numpy().decode("utf-8")
]
)
print("训练集中的正常图片数量: " + str(COUNT_NORMAL))
COUNT_PNEUMONIA = len(
[
filename
for filename in train_paths
if "肺炎" in filename.numpy().decode("utf-8")
]
)
print("训练集中的肺炎图片数量: " + str(COUNT_PNEUMONIA))
训练集中的正常图片数量: 1349
训练集中的肺炎图片数量: 3883
def get_label(file_path):
# 将路径转换为路径组件列表
parts = tf.strings.split(file_path, "/")
# 倒数第二个是分类目录
if parts[-2] == "肺炎":
return 1
else:
return 0
def decode_img(img):
# 将压缩字符串转换为3D uint8张量
img = tf.image.decode_jpeg(img, channels=3)
# 将图像调整为所需大小。
return tf.image.resize(img, IMAGE_SIZE)
def process_path(image, path):
label = get_label(path)
# 将原始数据从文件加载为字符串
img = decode_img(image)
return img, label
ds = ds.map(process_path, num_parallel_calls=AUTOTUNE)
ds = ds.shuffle(10000)
train_ds = ds.take(4200)
val_ds = ds.skip(4200)
for image, label in train_ds.take(1):
print("图像形状: ", image.numpy().shape)
print("标签: ", label.numpy())
图像形状: (180, 180, 3)
标签: False
test_images = tf.data.TFRecordDataset(
"gs://download.tensorflow.org/data/ChestXRay2017/test/images.tfrec"
)
test_paths = tf.data.TFRecordDataset(
"gs://download.tensorflow.org/data/ChestXRay2017/test/paths.tfrec"
)
test_ds = tf.data.Dataset.zip((test_images, test_paths))
test_ds = test_ds.map(process_path, num_parallel_calls=AUTOTUNE)
test_ds = test_ds.batch(BATCH_SIZE)
def prepare_for_training(ds, cache=True):
# 这是一个小数据集，只需要加载一次，并将其保留在内存中。
# 对于无法装入内存的数据集，使用`.cache(filename)`来缓存预处理工作。
if cache:
if isinstance(cache, str):
ds = ds.cache(cache)
else:
ds = ds.cache()
ds = ds.batch(BATCH_SIZE)
# `prefetch`允许数据集在模型训练的同时在后台获取批次。
ds = ds.prefetch(buffer_size=AUTOTUNE)
return ds
train_ds = prepare_for_training(train_ds)
val_ds = prepare_for_training(val_ds)
image_batch, label_batch = next(iter(train_ds))
def show_batch(image_batch, label_batch):
plt.figure(figsize=(10, 10))
for n in range(25):
ax = plt.subplot(5, 5, n + 1)
plt.imshow(image_batch[n] / 255)
if label_batch[n]:
plt.title("肺炎")
else:
plt.title("正常")
plt.axis("off")
show_batch(image_batch.numpy(), label_batch.numpy())
import os
os.environ['KERAS_BACKEND'] = 'tensorflow'
import keras
from keras import layers
def conv_block(filters, inputs):
x = layers.SeparableConv2D(filters, 3, activation="relu", padding="same")(inputs)
x = layers.SeparableConv2D(filters, 3, activation="relu", padding="same")(x)
x = layers.BatchNormalization()(x)
outputs = layers.MaxPool2D()(x)
return outputs
def dense_block(units, dropout_rate, inputs):
x = layers.Dense(units, activation="relu")(inputs)
x = layers.BatchNormalization()(x)
outputs = layers.Dropout(dropout_rate)(x)
return outputs
def build_model():
inputs = keras.Input(shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3))
x = layers.Rescaling(1.0 / 255)(inputs)
x = layers.Conv2D(16, 3, activation="relu", padding="same")(x)
x = layers.Conv2D(16, 3, activation="relu", padding="same")(x)
x = layers.MaxPool2D()(x)
x = conv_block(32, x)
x = conv_block(64, x)
x = conv_block(128, x)
x = layers.Dropout(0.2)(x)
x = conv_block(256, x)
x = layers.Dropout(0.2)(x)
x = layers.Flatten()(x)
x = dense_block(512, 0.7, x)
x = dense_block(128, 0.5, x)
x = dense_block(64, 0.3, x)
outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
initial_bias = np.log([COUNT_PNEUMONIA / COUNT_NORMAL])
print("初始偏差: {:.5f}".format(initial_bias[0]))
TRAIN_IMG_COUNT = COUNT_NORMAL + COUNT_PNEUMONIA
weight_for_0 = (1 / COUNT_NORMAL) * (TRAIN_IMG_COUNT) / 2.0
weight_for_1 = (1 / COUNT_PNEUMONIA) * (TRAIN_IMG_COUNT) / 2.0
class_weight = {0: weight_for_0, 1: weight_for_1}
print("类0的权重: {:.2f}".format(weight_for_0))
print("类1的权重: {:.2f}".format(weight_for_1))
初始偏差: 1.05724
类0的权重: 1.94
类1的权重: 0.67
checkpoint_cb = keras.callbacks.ModelCheckpoint("xray_model.keras", save_best_only=True)
early_stopping_cb = keras.callbacks.EarlyStopping(
patience=10, restore_best_weights=True
)
initial_learning_rate = 0.015
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True
)
with strategy.scope():
model = build_model()
METRICS = [
keras.metrics.BinaryAccuracy(),
keras.metrics.Precision(name="precision"),
keras.metrics.Recall(name="recall"),
]
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=lr_schedule),
loss="binary_crossentropy",
metrics=METRICS,
)
history = model.fit(
train_ds,
epochs=100,
validation_data=val_ds,
class_weight=class_weight,
callbacks=[checkpoint_cb, early_stopping_cb],
)
Epoch 1/100
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Iterator.get_next_as_optional()` instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Iterator.get_next_as_optional()` instead.
21/21 [==============================] - 12s 568ms/step - loss: 0.5857 - binary_accuracy: 0.6960 - precision: 0.8887 - recall: 0.6733 - val_loss: 34.0149 - val_binary_accuracy: 0.7180 - val_precision: 0.7180 - val_recall: 1.0000
Epoch 2/100
21/21 [==============================] - 3s 128ms/step - loss: 0.2916 - binary_accuracy: 0.8755 - precision: 0.9540 - recall: 0.8738 - val_loss: 97.5194 - val_binary_accuracy: 0.7180 - val_precision: 0.7180 - val_recall: 1.0000
Epoch 3/100
21/21 [==============================] - 4s 167ms/step - loss: 0.2384 - binary_accuracy: 0.9002 - precision: 0.9663 - recall: 0.8964 - val_loss: 27.7902 - val_binary_accuracy: 0.7180 - val_precision: 0.7180 - val_recall: 1.0000
Epoch 4/100
21/21 [==============================] - 4s 173ms/step - loss: 0.2046 - binary_accuracy: 0.9145 - precision: 0.9725 - recall: 0.9102 - val_loss: 10.8302 - val_binary_accuracy: 0.7180 - val_precision: 0.7180 - val_recall: 1.0000
Epoch 5/100
21/21 [==============================] - 4s 174ms/step - loss: 0.1841 - binary_accuracy: 0.9279 - precision: 0.9733 - recall: 0.9279 - val_loss: 3.5860 - val_binary_accuracy: 0.7103 - val_precision: 0.7162 - val_recall: 0.9879
Epoch 6/100
21/21 [==============================] - 4s 185ms/step - loss: 0.1600 - binary_accuracy: 0.9362 - precision: 0.9791 - recall: 0.9337 - val_loss: 0.3014 - val_binary_accuracy: 0.8895 - val_precision: 0.8973 - val_recall: 0.9555
Epoch 7/100
21/21 [==============================] - 3s 130ms/step - loss: 0.1567 - binary_accuracy: 0.9393 - precision: 0.9798 - recall: 0.9372 - val_loss: 0.6763 - val_binary_accuracy: 0.7810 - val_precision: 0.7760 - val_recall: 0.9771
Epoch 8/100
21/21 [==============================] - 3s 131ms/step - loss: 0.1532 - binary_accuracy: 0.9421 - precision: 0.9825 - recall: 0.9385 - val_loss: 0.3169 - val_binary_accuracy: 0.8895 - val_precision: 0.8684 - val_recall: 0.9973
Epoch 9/100
21/21 [==============================] - 4s 184ms/step - loss: 0.1457 - binary_accuracy: 0.9431 - precision: 0.9822 - recall: 0.9401 - val_loss: 0.2064 - val_binary_accuracy: 0.9273 - val_precision: 0.9840 - val_recall: 0.9136
Epoch 10/100
21/21 [==============================] - 3s 132ms/step - loss: 0.1201 - binary_accuracy: 0.9521 - precision: 0.9869 - recall: 0.9479 - val_loss: 0.4364 - val_binary_accuracy: 0.8605 - val_precision: 0.8443 - val_recall: 0.9879
Epoch 11/100
21/21 [==============================] - 3s 127ms/step - loss: 0.1200 - binary_accuracy: 0.9510 - precision: 0.9863 - recall: 0.9469 - val_loss: 0.5197 - val_binary_accuracy: 0.8508 - val_precision: 1.0000 - val_recall: 0.7922
Epoch 12/100
21/21 [==============================] - 4s 186ms/step - loss: 0.1077 - binary_accuracy: 0.9581 - precision: 0.9870 - recall: 0.9559 - val_loss: 0.1349 - val_binary_accuracy: 0.9486 - val_precision: 0.9587 - val_recall: 0.9703
Epoch 13/100
21/21 [==============================] - 4s 173ms/step - loss: 0.0918 - binary_accuracy: 0.9650 - precision: 0.9914 - recall: 0.9611 - val_loss: 0.0926 - val_binary_accuracy: 0.9700 - val_precision: 0.9837 - val_recall: 0.9744
Epoch 14/100
21/21 [==============================] - 3s 130ms/step - loss: 0.0996 - binary_accuracy: 0.9612 - precision: 0.9913 - recall: 0.9559 - val_loss: 0.1811 - val_binary_accuracy: 0.9419 - val_precision: 0.9956 - val_recall: 0.9231
Epoch 15/100
21/21 [==============================] - 3s 129ms/step - loss: 0.0898 - binary_accuracy: 0.9643 - precision: 0.9901 - recall: 0.9614 - val_loss: 0.1525 - val_binary_accuracy: 0.9486 - val_precision: 0.9986 - val_recall: 0.9298
Epoch 16/100
21/21 [==============================] - 3s 128ms/step - loss: 0.0941 - binary_accuracy: 0.9621 - precision: 0.9904 - recall: 0.9582 - val_loss: 0.5101 - val_binary_accuracy: 0.8527 - val_precision: 1.0000 - val_recall: 0.7949
Epoch 17/100
21/21 [==============================] - 3s 125ms/step - loss: 0.0798 - binary_accuracy: 0.9636 - precision: 0.9897 - recall: 0.9607 - val_loss: 0.1239 - val_binary_accuracy: 0.9622 - val_precision: 0.9875 - val_recall: 0.9595
Epoch 18/100
21/21 [==============================] - 3s 126ms/step - loss: 0.0821 - binary_accuracy: 0.9657 - precision: 0.9911 - recall: 0.9623 - val_loss: 0.1597 - val_binary_accuracy: 0.9322 - val_precision: 0.9956 - val_recall: 0.9096
Epoch 19/100
21/21 [==============================] - 3s 143ms/step - loss: 0.0800 - binary_accuracy: 0.9657 - precision: 0.9917 - recall: 0.9617 - val_loss: 0.2538 - val_binary_accuracy: 0.9109 - val_precision: 1.0000 - val_recall: 0.8758
Epoch 20/100
21/21 [==============================] - 3s 127ms/step - loss: 0.0605 - binary_accuracy: 0.9738 - precision: 0.9950 - recall: 0.9694 - val_loss: 0.6594 - val_binary_accuracy: 0.8566 - val_precision: 1.0000 - val_recall: 0.8003
Epoch 21/100
21/21 [==============================] - 4s 167ms/step - loss: 0.0726 - binary_accuracy: 0.9733 - precision: 0.9937 - recall: 0.9701 - val_loss: 0.0593 - val_binary_accuracy: 0.9816 - val_precision: 0.9945 - val_recall: 0.9798
Epoch 22/100
21/21 [==============================] - 3s 126ms/step - loss: 0.0577 - binary_accuracy: 0.9783 - precision: 0.9951 - recall: 0.9755 - val_loss: 0.1087 - val_binary_accuracy: 0.9729 - val_precision: 0.9931 - val_recall: 0.9690
Epoch 23/100
21/21 [==============================] - 3s 125ms/step - loss: 0.0652 - binary_accuracy: 0.9729 - precision: 0.9924 - recall: 0.9707 - val_loss: 1.8465 - val_binary_accuracy: 0.7180 - val_precision: 0.7180 - val_recall: 1.0000
Epoch 24/100
21/21 [==============================] - 3s 124ms/step - loss: 0.0538 - binary_accuracy: 0.9783 - precision: 0.9951 - recall: 0.9755 - val_loss: 1.5769 - val_binary_accuracy: 0.7180 - val_precision: 0.7180 - val_recall: 1.0000
Epoch 25/100
21/21 [==============================] - 4s 167ms/step - loss: 0.0549 - binary_accuracy: 0.9776 - precision: 0.9954 - recall: 0.9743 - val_loss: 0.0590 - val_binary_accuracy: 0.9777 - val_precision: 0.9904 - val_recall: 0.9784
Epoch 26/100
21/21 [==============================] - 3s 131ms/step - loss: 0.0677 - binary_accuracy: 0.9719 - precision: 0.9924 - recall: 0.9694 - val_loss: 2.6008 - val_binary_accuracy: 0.6928 - val_precision: 0.9977 - val_recall: 0.5735
Epoch 27/100
21/21 [==============================] - 3s 127ms/step - loss: 0.0469 - binary_accuracy: 0.9833 - precision: 0.9971 - recall: 0.9804 - val_loss: 1.0184 - val_binary_accuracy: 0.8605 - val_precision: 0.9983 - val_recall: 0.8070
Epoch 28/100
21/21 [==============================] - 3s 126ms/step - loss: 0.0501 - binary_accuracy: 0.9790 - precision: 0.9961 - recall: 0.9755 - val_loss: 0.3737 - val_binary_accuracy: 0.9089 - val_precision: 0.9954 - val_recall: 0.8772
Epoch 29/100
21/21 [==============================] - 3s 128ms/step - loss: 0.0548 - binary_accuracy: 0.9798 - precision: 0.9941 - recall: 0.9784 - val_loss: 1.2928 - val_binary_accuracy: 0.7907 - val_precision: 1.0000 - val_recall: 0.7085
Epoch 30/100
21/21 [==============================] - 3s 129ms/step - loss: 0.0370 - binary_accuracy: 0.9860 - precision: 0.9980 - recall: 0.9829 - val_loss: 0.1370 - val_binary_accuracy: 0.9612 - val_precision: 0.9972 - val_recall: 0.9487
Epoch 31/100
21/21 [==============================] - 3s 125ms/step - loss: 0.0585 - binary_accuracy: 0.9819 - precision: 0.9951 - recall: 0.9804 - val_loss: 1.1955 - val_binary_accuracy: 0.6870 - val_precision: 0.9976 - val_recall: 0.5655
Epoch 32/100
21/21 [==============================] - 3s 140ms/step - loss: 0.0813 - binary_accuracy: 0.9695 - precision: 0.9934 - recall: 0.9652 - val_loss: 1.0394 - val_binary_accuracy: 0.8576 - val_precision: 0.9853 - val_recall: 0.8138
Epoch 33/100
21/21 [==============================] - 3s 128ms/step - loss: 0.1111 - binary_accuracy: 0.9555 - precision: 0.9870 - recall: 0.9524 - val_loss: 4.9438 - val_binary_accuracy: 0.5911 - val_precision: 1.0000 - val_recall: 0.4305
Epoch 34/100
21/21 [==============================] - 3s 130ms/step - loss: 0.0680 - binary_accuracy: 0.9726 - precision: 0.9921 - recall: 0.9707 - val_loss: 2.8822 - val_binary_accuracy: 0.7267 - val_precision: 0.9978 - val_recall: 0.6208
Epoch 35/100
21/21 [==============================] - 4s 187ms/step - loss: 0.0784 - binary_accuracy: 0.9712 - precision: 0.9892 - recall: 0.9717 - val_loss: 0.3940 - val_binary_accuracy: 0.9390 - val_precision: 0.9942 - val_recall: 0.9204
fig, ax = plt.subplots(1, 4, figsize=(20, 3))
ax = ax.ravel()
for i, met in enumerate(["precision", "recall", "binary_accuracy", "loss"]):
ax[i].plot(history.history[met])
ax[i].plot(history.history["val_" + met])
ax[i].set_title("Model {}".format(met))
ax[i].set_xlabel("epochs")
ax[i].set_ylabel(met)
ax[i].legend(["train", "val"])
model.evaluate(test_ds, return_dict=True)
4/4 [==============================] - 3s 708ms/step - loss: 0.9718 - binary_accuracy: 0.7901 - precision: 0.7524 - recall: 0.9897
{'binary_accuracy': 0.7900640964508057,
'loss': 0.9717951416969299,
'precision': 0.752436637878418,
'recall': 0.9897436499595642}
for image, label in test_ds.take(1):
plt.imshow(image[0] / 255.0)
plt.title(CLASS_NAMES[label[0].numpy()])
prediction = model.predict(test_ds.take(1))[0]
scores = [1 - prediction, prediction]
for score, name in zip(scores, CLASS_NAMES):
print("该图像是 %.2f 百分比 %s" % ((100 * score), name))
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
This is separate from the ipykernel package so we can avoid doing imports until
该图像是 47.19 百分比 NORMAL
该图像是 52.81 百分比 PNEUMONIA