TensorFlow2 學習——CNN圖像分類

TensorFlow2 學習——CNN圖像分類

1. 導包

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. 圖像分類 fashion_mnist

• 數據處理

  # 原始數據
  (X_train_all, y_train_all),(X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
  
  # 訓練集、驗證集拆分
  X_train, X_valid, y_train, y_valid = train_test_split(X_train_all, y_train_all, test_size=0.25)
  
  # 數據標準化，你也可以用除以255的方式實現歸一化
  # 注意最後reshape中的1，代表圖像只有一個channel，即當前圖像是灰度圖
  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)
  

• 構建CNN模型

  model = tf.keras.models.Sequential()
  # 多個卷積層
  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))
  # 將前面卷積層得出的多維數據轉爲一維
  # 7和前面的kernel_size、padding、MaxPool2D有關
  # Conv2D: 28*28 -> 28*28 (因爲padding="same")
  # MaxPool2D: 28*28 -> 14*14
  # Conv2D: 14*14 -> 14*14 (因爲padding="same")
  # MaxPool2D: 14*14 -> 7*7
  model.add(tf.keras.layers.Reshape(target_shape=(7 * 7 * 64,)))
  # 傳入全連接層
  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"])
  

• 模型訓練

  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
  

• 作圖

  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)
  

• 測試集評估準確率

  model.evaluate(X_test_scaled, y_test)
  

  [0.269884311157465, 0.9071]
  

• 可以看到使用CNN後，圖像分類的準確率明顯提升了。之前的模型是0.8747，現在是0.9071。

3. 圖像分類 Dogs vs. Cats

3.1 原始數據

• 原始數據下載

  • Kaggle: https://www.kaggle.com/c/dogs-vs-cats/
  • 百度網盤: https://pan.baidu.com/s/13hw4LK8ihR6-6-8mpjLKDA 提取碼 dmp4

• 讀取一張圖片，並展示

  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加載圖片

• 由於原始圖片過多，我們不能將所有圖片一次加載入內存。Tensorflow爲我們提供了便利的Dataset API，可以從硬盤中一批一批的加載數據，以用於訓練。
• 處理本地圖片路徑與標籤

  # 訓練數據的路徑
  train_dir = "C:/Users/Skey/Downloads/datasets/cat_vs_dog/train/"
  
  train_filenames = [] # 所有圖片的文件名
  train_labels = [] # 所有圖片的標籤
  for filename in os.listdir(train_dir):
      train_filenames.append(train_dir + filename)
      if (filename.startswith("cat")):
          train_labels.append(0) # 將cat標記爲0
      else:
          train_labels.append(1) # 將dog標記爲1
  
  # 數據隨機拆分
  X_train, X_valid, y_train, y_valid = train_test_split(train_filenames, train_labels, test_size=0.2)
  

• 定義一個解碼圖片的方法

  def _decode_and_resize(filename, label):
      image_string = tf.io.read_file(filename)            # 讀取圖片
      image_decoded = tf.image.decode_jpeg(image_string)  # 解碼
      image_resized = tf.image.resize(image_decoded, [256, 256]) / 255.0 # 重置size，並歸一化
      return image_resized, label
  

• 定義 Dataset，用於加載圖片數據

  # 訓練集
  train_dataset = tf.data.Dataset.from_tensor_slices((train_filenames, train_labels))
  train_dataset = train_dataset.map(
      map_func=_decode_and_resize, # 調用前面定義的方法，解析filename，轉爲特徵和標籤
      num_parallel_calls=tf.data.experimental.AUTOTUNE)
  train_dataset = train_dataset.shuffle(buffer_size=128) # 設置緩衝區大小
  train_dataset = train_dataset.batch(32) # 每批數據的量
  train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE) # 啓動預加載圖片，也就是說CPU會提前從磁盤加載數據，不用等上一次訓練完後再加載
  
  # 驗證集
  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 構建CNN模型，並訓練

• 構建模型與編譯

  model = tf.keras.Sequential([
  	# 卷積，32個filter(卷積核)，每個大小爲3*3，步長爲1
      tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(256, 256, 3)),
      # 池化，默認大小2*2，步長爲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]
  )
  

• 模型總覽

  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
  

• 開始訓練

  model.fit(train_dataset, epochs=10, validation_data=valid_dataset)
  

  • 由於數據量大，此處訓練時間較久
  • 需要注意的是此處打印的step，每個step指的是一個batch（例如32個樣本一個batch）

• 模型評估

  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))