tensorflow 2系列04 从0构建图像分类分类模型

tensorflow 2系列04 从0构建一个图像分类模型之花卉分类

本期文章是一个系列课程，本文是这个系列的第3篇复习笔记

(1)Build and train neural network models using TensorFlow 2.x

(2)Image classification

(3)Natural language processing(NLP)

(4)Time series, sequences and predictions

本次图像分类是从0构建一个生产可用的模型，主要包含以下步骤

数据处理

构建模型

检验模型效果

过拟合调优

数据处理

数据下载

import tensorflow as tf

import numpy as np

import os

import PIL

import matplotlib.pyplot as plt

import pathlib

dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"

data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)

data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.jpg')))

print(image_count)

3670

roses = list(data_dir.glob('roses/*'))

img=plt.imread(roses[0])

img.shape

plt.imshow(img)

PIL.Image.open(str(roses[0]))

PIL.Image.open(str(roses[1]))

tulips = list(data_dir.glob('tulips/*'))

PIL.Image.open(str(tulips[0]))

PIL.Image.open(str(tulips[1]))

创建数据集

batch_size = 32

img_height = 180

img_width = 180

# tf.keras.preprocessing.image.ImageDataGenerator

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)

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)

class_names = train_ds.class_names

print(class_names)

Found 3670 files belonging to 5 classes.

Using 2936 files for training.

Found 3670 files belonging to 5 classes.

Using 734 files for validation.

['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']

可视化数据

plt.figure(figsize=(10, 10))

for images, labels in train_ds.take(1):

for i in range(9):

ax = plt.subplot(3, 3, i + 1)

plt.imshow(images[i].numpy().astype("uint8"))

plt.title(class_names[labels[i]])

AUTOTUNE =1000

train_ds=train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)

val_ds=val_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)

normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))

image_batch, labels_batch = next(iter(normalized_ds))

first_image = image_batch[0]

# Notice the pixels values are now in `[0,1]`.

print(np.min(first_image), np.max(first_image))

0.0 1.0

定义模型

num_classes = 5

model = tf.keras.Sequential([

tf.keras.layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),

tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Flatten(),

tf.keras.layers.Dense(128, activation='relu'),

tf.keras.layers.Dense(num_classes,activation="softmax")

])

model.compile(optimizer='adam',

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),

metrics=['accuracy'])

model.summary()

Model: "sequential"

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

rescaling_1 (Rescaling) (None, 180, 180, 3) 0

_________________________________________________________________

conv2d (Conv2D) (None, 180, 180, 16) 448

_________________________________________________________________

max_pooling2d (MaxPooling2D) (None, 90, 90, 16) 0

_________________________________________________________________

conv2d_1 (Conv2D) (None, 90, 90, 32) 4640

_________________________________________________________________

max_pooling2d_1 (MaxPooling2 (None, 45, 45, 32) 0

_________________________________________________________________

conv2d_2 (Conv2D) (None, 45, 45, 64) 18496

_________________________________________________________________

max_pooling2d_2 (MaxPooling2 (None, 22, 22, 64) 0

_________________________________________________________________

flatten (Flatten) (None, 30976) 0

_________________________________________________________________

dense (Dense) (None, 128) 3965056

_________________________________________________________________

dense_1 (Dense) (None, 5) 645

=================================================================

Total params: 3,989,285

Trainable params: 3,989,285

Non-trainable params: 0

_________________________________________________________________

epochs=5

history = model.fit(

train_ds,

validation_data=val_ds,

epochs=epochs

)

Epoch 1/5

92/92 [==============================] - 51s 550ms/step - loss: 1.3677 - accuracy: 0.4166 - val_loss: 1.0836 - val_accuracy: 0.5681

Epoch 2/5

92/92 [==============================] - 50s 542ms/step - loss: 0.9736 - accuracy: 0.6100 - val_loss: 0.9855 - val_accuracy: 0.6063

Epoch 3/5

92/92 [==============================] - 50s 544ms/step - loss: 0.7864 - accuracy: 0.6904 - val_loss: 0.8481 - val_accuracy: 0.6608

Epoch 4/5

92/92 [==============================] - 50s 546ms/step - loss: 0.5951 - accuracy: 0.7766 - val_loss: 0.8688 - val_accuracy: 0.6703

Epoch 5/5

92/92 [==============================] - 50s 545ms/step - loss: 0.4205 - accuracy: 0.8498 - val_loss: 0.9487 - val_accuracy: 0.6894

Visualize training results

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=(8, 8))

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()

过拟合调优

数据增强

data_augmentation = tf.keras.Sequential(

[

tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal",

input_shape=(img_height,

img_width,

3)),

tf.keras.layers.experimental.preprocessing.RandomRotation(0.1),

tf.keras.layers.experimental.preprocessing.RandomZoom(0.1),

]

)

plt.figure(figsize=(10, 10))

for images, _ in train_ds.take(1):

for i in range(9):

augmented_images = data_augmentation(images)

ax = plt.subplot(3, 3, i + 1)

plt.imshow(augmented_images[0].numpy().astype("uint8"))

plt.axis("off")

dropout

model = tf.keras.Sequential([

data_augmentation,

tf.keras.layers.experimental.preprocessing.Rescaling(1./255),

tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.Flatten(),

tf.keras.layers.Dense(128, activation='relu'),

tf.keras.layers.Dense(num_classes,activation="softmax")

])

model.compile(optimizer='adam',

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),

metrics=['accuracy'])

model.summary()

Model: "sequential_2"

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

sequential_1 (Sequential) (None, 180, 180, 3) 0

_________________________________________________________________

rescaling_2 (Rescaling) (None, 180, 180, 3) 0

_________________________________________________________________

conv2d_3 (Conv2D) (None, 180, 180, 16) 448

_________________________________________________________________

max_pooling2d_3 (MaxPooling2 (None, 90, 90, 16) 0

_________________________________________________________________

conv2d_4 (Conv2D) (None, 90, 90, 32) 4640

_________________________________________________________________

max_pooling2d_4 (MaxPooling2 (None, 45, 45, 32) 0

_________________________________________________________________

conv2d_5 (Conv2D) (None, 45, 45, 64) 18496

_________________________________________________________________

max_pooling2d_5 (MaxPooling2 (None, 22, 22, 64) 0

_________________________________________________________________

dropout (Dropout) (None, 22, 22, 64) 0

_________________________________________________________________

flatten_1 (Flatten) (None, 30976) 0

_________________________________________________________________

dense_2 (Dense) (None, 128) 3965056

_________________________________________________________________

dense_3 (Dense) (None, 5) 645

=================================================================

Total params: 3,989,285

Trainable params: 3,989,285

Non-trainable params: 0

_________________________________________________________________

epochs = 5

history = model.fit(

train_ds,

validation_data=val_ds,

epochs=epochs

)

Epoch 1/5

92/92 [==============================] - 57s 616ms/step - loss: 1.2607 - accuracy: 0.4813 - val_loss: 1.1557 - val_accuracy: 0.5572

Epoch 2/5

92/92 [==============================] - 56s 613ms/step - loss: 1.0268 - accuracy: 0.5926 - val_loss: 1.0223 - val_accuracy: 0.5777

Epoch 3/5

92/92 [==============================] - 56s 612ms/step - loss: 0.9231 - accuracy: 0.6349 - val_loss: 0.9180 - val_accuracy: 0.6431

Epoch 4/5

92/92 [==============================] - 63s 680ms/step - loss: 0.8650 - accuracy: 0.6649 - val_loss: 0.8474 - val_accuracy: 0.6744

Epoch 5/5

92/92 [==============================] - 59s 646ms/step - loss: 0.8098 - accuracy: 0.6907 - val_loss: 0.9028 - val_accuracy: 0.6417

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=(8, 8))

plt.subplot(1, 2, 1)

plt.plot(epochs_range, acc, label='Training Accuracy')

plt.plot(epochs_range, val_acc, label='Validatiplt.subplot(1, 2, 2)on 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()

sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"

sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)

img = tf.keras.preprocessing.image.load_img(

sunflower_path, target_size=(img_height, img_width)

)

plt.imshow(img)

img_array = tf.keras.preprocessing.image.img_to_array(img)

print(img_array.shape)

img_array = tf.expand_dims(img_array, 0) # Create a batch

print(img_array.shape)

predictions = model.predict(img_array)

print(predictions.shape)

print(class_names[np.argmax(predictions)])

score = tf.nn.softmax(predictions[0])

print(score)

print(np.max(score))

print(

"This image most likely belongs to {} with a {:.2f} percent confidence."

.format(class_names[np.argmax(score)], 100 * np.max(score))

)

(180, 180, 3)

(1, 180, 180, 3)

(1, 5)

sunflowers

tf.Tensor([0.1501881 0.15026027 0.15038382 0.39373598 0.15543188], shape=(5,), dtype=float32)

0.39373598

This image most likely belongs to sunflowers with a 39.37 percent confidence.

总结

数据相关工具类

tf.kears.util.get_file(fname,origin,untar)从网络上下载压缩包并自动解压，返回路径

pathlib.Path()加载路径

图像显示PIL.Image.Open() plt.imgshow()

tf.keras.preprocessing.image_dataset_from_directory从目录构造数据集是一个feature,label的元组数组主要参数　data_dir,数据目录 validation_split=0.2,验证集占比 subset="validation",验证集名称 seed=123,划分的随机种子 image_size=(img_height, img_width),设置target image size batch_size=batch_size 数据批处理大小返回的是一个dataset对象主键包含的属性 class_names cache()方法缓存数据集，不用频繁读取磁盘 shuffle()方法设置随机混洗多少次 prefetch()预取多少条做缓冲，加快训练速度上面三个方法可以链式调用 cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)

图片可视化 PIL.Image.Open打开一个图片或者用plt来显示图片plt.imgshow()

数据可视化首先定义一个画布plt.figure(figsize=(10, 10)) plt.subplot()来指定行列第几行，定义图形的行列 plt.plot(num,arr,label)num是数组arr的长度，arr要显示的数组，label是表示的图例

显示数组的值

特征处理相关

特征处理有几种方式，一种是直接对数据集进行处理，还有一种是端到端的处理方式，就是将数据处理放进模型中，模型直接接收原始数据，前面几层直接是处理特征的层个人觉得看情况调用，第一种情况提前处理，有利于节省后面模型训练的时间，第二种是端到端的，比较方便开箱即用．

生产阶段推荐第一种验证可以用第二种，或者机器条件好的情况，可以用第二种第二种方式代码如下

data_augmentation = tf.keras.Sequential(

[

tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal",

input_shape=(img_height,

img_width,

3)),#图像翻转

tf.keras.layers.experimental.preprocessing.RandomRotation(0.1),#随机旋转

tf.keras.layers.experimental.preprocessing.RandomZoom(0.1),#随机放大缩小

tf.keras.layers.experimental.preprocessing.Rescaling(1./255) #缩放到0,1之间

]

)

这个data_augmentation可以当函数使用，也可以作为模型输入的第一层比如下面

model = tf.keras.Sequential([

data_augmentation,

tf.keras.layers.experimental.preprocessing.Rescaling(1./255),

tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),

tf.keras.layers.MaxPooling2D(),

tf.keras.layers.Dropout(0.2),

tf.keras.layers.Flatten(),

tf.keras.layers.Dense(128, activation='relu'),

tf.keras.layers.Dense(num_classes,activation="softmax")

])

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tensorflow 2系列04 从0构建图像分类分类模型

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