文章目录

TensorFlow2 学习——CNN图像分类

1. 导包
2. 图像分类 fashion_mnist
3. 图像分类 Dogs vs. Cats

3.1 原始数据
3.2 利用Dataset加载图片
3.3 构建CNN模型，并训练

TensorFlow2 学习——CNN图像分类

1. 导包

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

2. 图像分类 fashion_mnist

数据处理

# 原始数据
(X_train_all, y_train_all),(X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()

# 训练集、验证集拆分
X_train, X_valid, y_train, y_valid = train_test_split(X_train_all, y_train_all, test_size=0.25)

# 数据标准化，你也可以用除以255的方式实现归一化
# 注意最后reshape中的1，代表图像只有一个channel，即当前图像是灰度图
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train.reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)
X_valid_scaled = scaler.transform(X_valid.reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)
X_test_scaled = scaler.transform(X_test.reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)

构建CNN模型

model = tf.keras.models.Sequential()
# 多个卷积层
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=[5, 5], padding="same", activation="relu", input_shape=(28, 28, 1)))
model.add(tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2))
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=[5, 5], padding="same", activation="relu"))
model.add(tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2))
# 将前面卷积层得出的多维数据转为一维
# 7和前面的kernel_size、padding、MaxPool2D有关
# Conv2D: 28*28 -> 28*28 (因为padding="same")
# MaxPool2D: 28*28 -> 14*14
# Conv2D: 14*14 -> 14*14 (因为padding="same")
# MaxPool2D: 14*14 -> 7*7
model.add(tf.keras.layers.Reshape(target_shape=(7 * 7 * 64,)))
# 传入全连接层
model.add(tf.keras.layers.Dense(1024, activation="relu"))
model.add(tf.keras.layers.Dense(10, activation="softmax"))

# compile
model.compile(loss = "sparse_categorical_crossentropy",
             optimizer = "sgd",
             metrics = ["accuracy"])

模型训练

callbacks = [
    tf.keras.callbacks.EarlyStopping(min_delta=1e-3, patience=5)
]

history = model.fit(X_train_scaled, y_train, epochs=15, 
                    validation_data=(X_valid_scaled, y_valid),
                    callbacks = callbacks)

Train on 50000 samples, validate on 10000 samples
Epoch 1/15
50000/50000 [==============================] - 17s 343us/sample - loss: 0.5707 - accuracy: 0.7965 - val_loss: 0.4631 - val_accuracy: 0.8323
Epoch 2/15
50000/50000 [==============================] - 13s 259us/sample - loss: 0.3728 - accuracy: 0.8669 - val_loss: 0.3573 - val_accuracy: 0.8738
...
Epoch 13/15
50000/50000 [==============================] - 12s 244us/sample - loss: 0.1625 - accuracy: 0.9407 - val_loss: 0.2489 - val_accuracy: 0.9112
Epoch 14/15
50000/50000 [==============================] - 12s 240us/sample - loss: 0.1522 - accuracy: 0.9451 - val_loss: 0.2584 - val_accuracy: 0.9104
Epoch 15/15
50000/50000 [==============================] - 12s 237us/sample - loss: 0.1424 - accuracy: 0.9500 - val_loss: 0.2521 - val_accuracy: 0.9114

作图

def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8, 5))
    plt.grid(True)
    #plt.gca().set_ylim(0, 1)
    plt.show()
    
plot_learning_curves(history)

测试集评估准确率

model.evaluate(X_test_scaled, y_test)

[0.269884311157465, 0.9071]

可以看到使用CNN后，图像分类的准确率明显提升了。之前的模型是0.8747，现在是0.9071。

3. 图像分类 Dogs vs. Cats

3.1 原始数据

原始数据下载
- Kaggle: https://www.kaggle.com/c/dogs-vs-cats/
- 百度网盘: https://pan.baidu.com/s/13hw4LK8ihR6-6-8mpjLKDA 提取码 dmp4

读取一张图片，并展示

image_string = tf.io.read_file("C:/Users/Skey/Downloads/datasets/cat_vs_dog/train/cat.28.jpg")
image_decoded = tf.image.decode_jpeg(image_string)
plt.imshow(image_decoded)

cat图像

3.2 利用Dataset加载图片

由于原始图片过多，我们不能将所有图片一次加载入内存。Tensorflow为我们提供了便利的Dataset API，可以从硬盘中一批一批的加载数据，以用于训练。

处理本地图片路径与标签

# 训练数据的路径
train_dir = "C:/Users/Skey/Downloads/datasets/cat_vs_dog/train/"

train_filenames = [] # 所有图片的文件名
train_labels = [] # 所有图片的标签
for filename in os.listdir(train_dir):
    train_filenames.append(train_dir + filename)
    if (filename.startswith("cat")):
        train_labels.append(0) # 将cat标记为0
    else:
        train_labels.append(1) # 将dog标记为1

# 数据随机拆分
X_train, X_valid, y_train, y_valid = train_test_split(train_filenames, train_labels, test_size=0.2)

定义一个解码图片的方法

def _decode_and_resize(filename, label):
    image_string = tf.io.read_file(filename)            # 读取图片
    image_decoded = tf.image.decode_jpeg(image_string)  # 解码
    image_resized = tf.image.resize(image_decoded, [256, 256]) / 255.0 # 重置size，并归一化
    return image_resized, label

定义 Dataset，用于加载图片数据

# 训练集
train_dataset = tf.data.Dataset.from_tensor_slices((train_filenames, train_labels))
train_dataset = train_dataset.map(
    map_func=_decode_and_resize, # 调用前面定义的方法，解析filename，转为特征和标签
    num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_dataset = train_dataset.shuffle(buffer_size=128) # 设置缓冲区大小
train_dataset = train_dataset.batch(32) # 每批数据的量
train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE) # 启动预加载图片，也就是说CPU会提前从磁盘加载数据，不用等上一次训练完后再加载

# 验证集
valid_dataset = tf.data.Dataset.from_tensor_slices((valid_filenames, valid_labels))
valid_dataset = valid_dataset.map(
    map_func=_decode_and_resize,
    num_parallel_calls=tf.data.experimental.AUTOTUNE)
valid_dataset = valid_dataset.batch(32)

3.3 构建CNN模型，并训练

构建模型与编译

model = tf.keras.Sequential([
	# 卷积，32个filter(卷积核)，每个大小为3*3，步长为1
    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(256, 256, 3)),
    # 池化，默认大小2*2，步长为2
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Conv2D(32, 5, activation='relu'),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(2, activation='softmax')
])

model.compile(
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
    loss=tf.keras.losses.sparse_categorical_crossentropy,
    metrics=[tf.keras.metrics.sparse_categorical_accuracy]
)

模型总览

model.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_2 (Conv2D)            (None, 254, 254, 32)      896       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 127, 127, 32)      0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 123, 123, 32)      25632     
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 61, 61, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 119072)            0         
_________________________________________________________________
dense_2 (Dense)              (None, 64)                7620672   
_________________________________________________________________
dense_3 (Dense)              (None, 2)                 130       
=================================================================
Total params: 7,647,330
Trainable params: 7,647,330
Non-trainable params: 0

开始训练
```
model.fit(train_dataset, epochs=10, validation_data=valid_dataset)
```
- 由于数据量大，此处训练时间较久
- 需要注意的是此处打印的step，每个step指的是一个batch（例如32个样本一个batch）

模型评估

test_dataset = tf.data.Dataset.from_tensor_slices((valid_filenames, valid_labels))
test_dataset = test_dataset.map(_decode_and_resize)
test_dataset = test_dataset.batch(32)

print(model.metrics_names)
print(model.evaluate(test_dataset))

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TensorFlow2 学习——CNN图像分类

文章目录

TensorFlow2 学习——CNN图像分类

1. 导包

2. 图像分类 fashion_mnist

3. 图像分类 Dogs vs. Cats

3.1 原始数据

3.2 利用Dataset加载图片

3.3 构建CNN模型，并训练

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