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在 TPU 上进行肺炎分类
作者： Amy MiHyun Jang
创建日期 2020/07/28
最后修改日期 2024/02/12
描述： 在 TPU 上进行医学图像分类。
简介 + 设置
本教程将讲解如何构建一个 X 射线图像分类模型，以预测 X 射线扫描是否显示存在肺炎。
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("Device:", tpu.master())
strategy = tf.distribute.TPUStrategy(tpu)
except:
strategy = tf.distribute.get_strategy()
print("Number of replicas:", strategy.num_replicas_in_sync)
Device: grpc://10.0.27.122:8470
INFO:tensorflow:Initializing the TPU system: grpc://10.0.27.122:8470
INFO:tensorflow:Initializing the TPU system: grpc://10.0.27.122:8470
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Finished initializing TPU system.
WARNING:absl:[`tf.distribute.TPUStrategy`](https://tensorflowcn.cn/api_docs/python/tf/distribute/TPUStrategy) is deprecated, please use the non experimental symbol [`tf.distribute.TPUStrategy`](https://tensorflowcn.cn/api_docs/python/tf/distribute/TPUStrategy) instead.
INFO:tensorflow:Found TPU system:
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
Number of replicas: 8
我们需要一个指向我们数据的 Google Cloud 链接，以便使用 TPU 加载数据。下面，我们定义了本示例中将使用的关键配置参数。要在 TPU 上运行，本示例必须在 Colab 上，并且已选择 TPU 运行时。
AUTOTUNE = tf.data.AUTOTUNE
BATCH_SIZE = 25 * strategy.num_replicas_in_sync
IMAGE_SIZE = [180, 180]
CLASS_NAMES = ["NORMAL", "PNEUMONIA"]
加载数据
我们使用的来自 Cell 的胸部 X 射线数据将数据分为训练文件和测试文件。让我们首先加载训练 TFRecord。
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))
让我们统计一下我们有多少张健康/正常的胸部 X 射线图像，以及有多少张肺炎胸部 X 射线图像。
COUNT_NORMAL = len(
[
filename
for filename in train_paths
if "NORMAL" in filename.numpy().decode("utf-8")
]
)
print("Normal images count in training set: " + str(COUNT_NORMAL))
COUNT_PNEUMONIA = len(
[
filename
for filename in train_paths
if "PNEUMONIA" in filename.numpy().decode("utf-8")
]
)
print("Pneumonia images count in training set: " + str(COUNT_PNEUMONIA))
Normal images count in training set: 1349
Pneumonia images count in training set: 3883
请注意，被分类为肺炎的图像远多于正常的图像。这表明我们的数据存在不平衡。我们稍后将在笔记本中纠正这种不平衡。
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，使得 `1` 或 `True` 表示肺炎，`0` 或 `False` 表示正常。
def get_label(file_path):
# convert the path to a list of path components
parts = tf.strings.split(file_path, "/")
# The second to last is the class-directory
if parts[-2] == "PNEUMONIA":
return 1
else:
return 0
def decode_img(img):
# convert the compressed string to a 3D uint8 tensor
img = tf.image.decode_jpeg(img, channels=3)
# resize the image to the desired size.
return tf.image.resize(img, IMAGE_SIZE)
def process_path(image, path):
label = get_label(path)
# load the raw data from the file as a string
img = decode_img(image)
return img, label
ds = ds.map(process_path, num_parallel_calls=AUTOTUNE)
让我们将数据分割成训练数据集和验证数据集。
ds = ds.shuffle(10000)
train_ds = ds.take(4200)
val_ds = ds.skip(4200)
让我们可视化一个（图像，标签）对的形状。
for image, label in train_ds.take(1):
print("Image shape: ", image.numpy().shape)
print("Label: ", label.numpy())
Image shape: (180, 180, 3)
Label: False
同样加载并格式化测试数据。
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)
可视化数据集
首先，让我们使用缓冲预取，这样我们就可以从磁盘中获取数据，而不会让 I/O 成为阻塞操作。
请注意，大型图像数据集不应缓存在内存中。我们在这里这样做是因为数据集不是很大，并且我们想在 TPU 上进行训练。
def prepare_for_training(ds, cache=True):
# This is a small dataset, only load it once, and keep it in memory.
# use `.cache(filename)` to cache preprocessing work for datasets that don't
# fit in memory.
if cache:
if isinstance(cache, str):
ds = ds.cache(cache)
else:
ds = ds.cache()
ds = ds.batch(BATCH_SIZE)
# `prefetch` lets the dataset fetch batches in the background while the model
# is training.
ds = ds.prefetch(buffer_size=AUTOTUNE)
return ds
调用训练数据的下一批次迭代。
train_ds = prepare_for_training(train_ds)
val_ds = prepare_for_training(val_ds)
image_batch, label_batch = next(iter(train_ds))
定义显示批次中图像的方法。
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("PNEUMONIA")
else:
plt.title("NORMAL")
plt.axis("off")
由于该方法接受 NumPy 数组作为其参数，请在批次上调用 numpy 函数以返回 NumPy 数组形式的张量。
show_batch(image_batch.numpy(), label_batch.numpy())
构建 CNN
为了使我们的模型更具模块化且更易于理解，我们来定义一些模块。由于我们正在构建一个卷积神经网络，我们将创建一个卷积模块和一个密集层模块。
这个 CNN 的架构灵感来自于这篇文章。
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
以下方法将为我们定义构建模型的函数。
图像的原始值范围是 [0, 255]。CNN 在处理较小数值时效果更好，因此我们将为输入缩小这个范围。
Dropout 层很重要，因为它们降低了模型过拟合的可能性。我们希望模型的末端是一个带有一个节点的 `Dense` 层，因为这将是决定 X 射线是否显示肺炎存在的二进制输出。
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
纠正数据不平衡
我们在本示例的前面部分看到，数据是不平衡的，被分类为肺炎的图像多于正常的图像。我们将通过使用类别加权来纠正这一点。
initial_bias = np.log([COUNT_PNEUMONIA / COUNT_NORMAL])
print("Initial bias: {:.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("Weight for class 0: {:.2f}".format(weight_for_0))
print("Weight for class 1: {:.2f}".format(weight_for_1))
Initial bias: 1.05724
Weight for class 0: 1.94
Weight for class 1: 0.67
类别 `0`（正常）的权重远高于类别 `1`（肺炎）的权重。因为正常的图像较少，所以每张正常图像将被赋予更高的权重以平衡数据，因为 CNN 在训练数据平衡时效果最好。
训练模型
定义回调函数
检查点回调函数会保存模型的最佳权重，这样下次我们想使用模型时，就不必花费时间进行训练。早停回调函数在模型开始停滞不前，甚至更糟的是，当模型开始过拟合时，会停止训练过程。
checkpoint_cb = keras.callbacks.ModelCheckpoint("xray_model.keras", save_best_only=True)
early_stopping_cb = keras.callbacks.EarlyStopping(
patience=10, restore_best_weights=True
)
我们还希望调整我们的学习率。学习率过高会导致模型发散。学习率过小会导致模型速度太慢。我们下面实现了指数学习率调度方法。
initial_learning_rate = 0.015
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True
)
拟合模型
对于我们的评估指标，我们希望包含精确率和召回率，因为它们将为我们提供关于模型好坏的更全面的信息。准确率告诉我们标签正确的比例。由于我们的数据不平衡，准确率可能会对一个好模型产生一种有偏差的印象（例如，一个总是预测为“肺炎”的模型准确率将是 74%，但它不是一个好模型）。
精确率是真正例 (TP) 数量除以 TP 和假正例 (FP) 的总和。它显示了被标记为正例的样本中实际正确的比例。
召回率是 TP 数量除以 TP 和假负例 (FN) 的总和。它显示了实际正例中被正确识别的比例。
由于图像只有两种可能的标签，我们将使用二元交叉熵损失函数。当我们拟合模型时，请记住指定我们之前定义的类别权重。因为我们使用的是 TPU，训练将很快——不到 2 分钟。
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
可视化模型性能
让我们绘制训练集和验证集的模型准确率和损失。请注意，本笔记本没有指定随机种子。对于您的笔记本，可能会有轻微的差异。
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"])
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！
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}
我们看到，在测试数据上的准确率低于验证集的准确率。这可能表明存在过拟合。
我们的召回率大于精确率，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地识别了。我们应该致力于提高我们的精确率。
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("This image is %.2f percent %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
This image is 47.19 percent NORMAL
This image is 52.81 percent PNEUMONIA
