TensorFlow2 学习——CNN图像分类

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
import os

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)
  

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)
  
  # 转换为tensorflow的constant
  train_filenames = tf.constant(X_train)
  valid_filenames = tf.constant(X_valid)
  train_labels = tf.constant(y_train)
  valid_labels = tf.constant(y_valid)
  

• 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))