記錄第一次使用kaggle訓練貓狗數據集

import os import shutil os.listdir('../input/train/train') base_dir = './cat_dog'train_dir = os.path.join(base_dir,'train') train_dir_dog = os.path.join(train_dir,'dog') train_dir_cat = os.path.join(train_dir,'cat')test_dir = os.path.join(base_dir,'test') test_dir_dog = os.path.join(test_dir,'dog') test_dir_cat = os.path.join(test_dir,'cat') os.mkdir(base_dir) os.mkdir(train_dir) os.mkdir(train_dir_dog) os.mkdir(train_dir_cat) os.mkdir(test_dir) os.mkdir(test_dir_dog) os.mkdir(test_dir_cat) fnames = ['cat.{}.jpg'.format(i) for i in range(1000)] for fname in fnames:s = os.path.join('../input/train/train/cat',fname)d = os.path.join(train_dir_cat,fname)shutil.copyfile(s,d) fnames = ['cat.{}.jpg'.format(i) for i in range(1000,1500)] for fname in fnames:s = os.path.join('../input/train/train/cat',fname)d = os.path.join(test_dir_cat,fname)shutil.copyfile(s,d) fnames = ['dog.{}.jpg'.format(i) for i in range(1000)] for fname in fnames:s = os.path.join('../input/train/train/dog',fname)d = os.path.join(train_dir_dog,fname)shutil.copyfile(s,d) fnames = ['dog.{}.jpg'.format(i) for i in range(1000,1500)] for fname in fnames:s = os.path.join('../input/train/train/dog',fname)d = os.path.join(test_dir_dog,fname)shutil.copyfile(s,d)

• 讀取圖片
• 將圖片解碼
• 預處理圖片
• 圖片歸一化

import keras from keras import layers from keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(rescale=1/255) test_datagen = ImageDataGenerator(rescale=1/255) train_generator = train_datagen.flow_from_directory(train_dir,target_size=(200,200),batch_size=20,class_mode='binary') Found 2000 images belonging to 2 classes. test_generator = train_datagen.flow_from_directory(test_dir,target_size=(200,200),batch_size=20,class_mode='binary') Found 1000 images belonging to 2 classes. model = keras.Sequential() model.add(layers.Conv2D(64,(3,3), activation='relu',input_shape=(200,200,3))) model.add(layers.Conv2D(64,(3,3), activation='relu')) model.add(layers.MaxPool2D()) model.add(layers.Dropout(0.25)) model.add(layers.Conv2D(64,(3,3), activation='relu',input_shape=(200,200,3))) model.add(layers.Conv2D(64,(3,3), activation='relu')) model.add(layers.MaxPool2D()) model.add(layers.Dropout(0.25)) model.add(layers.Conv2D(64,(3,3), activation='relu',input_shape=(200,200,3))) model.add(layers.Conv2D(64,(3,3), activation='relu')) model.add(layers.MaxPool2D()) model.add(layers.Dropout(0.25)) model.add(layers.Flatten()) model.add(layers.Dense(236,activation='relu')) model.add(layers.Dense(1,activation='sigmoid')) model.summary() _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_25 (Conv2D) (None, 198, 198, 64) 1792 _________________________________________________________________ conv2d_26 (Conv2D) (None, 196, 196, 64) 36928 _________________________________________________________________ max_pooling2d_13 (MaxPooling (None, 98, 98, 64) 0 _________________________________________________________________ dropout_13 (Dropout) (None, 98, 98, 64) 0 _________________________________________________________________ conv2d_27 (Conv2D) (None, 96, 96, 64) 36928 _________________________________________________________________ conv2d_28 (Conv2D) (None, 94, 94, 64) 36928 _________________________________________________________________ max_pooling2d_14 (MaxPooling (None, 47, 47, 64) 0 _________________________________________________________________ dropout_14 (Dropout) (None, 47, 47, 64) 0 _________________________________________________________________ conv2d_29 (Conv2D) (None, 45, 45, 64) 36928 _________________________________________________________________ conv2d_30 (Conv2D) (None, 43, 43, 64) 36928 _________________________________________________________________ max_pooling2d_15 (MaxPooling (None, 21, 21, 64) 0 _________________________________________________________________ dropout_15 (Dropout) (None, 21, 21, 64) 0 _________________________________________________________________ flatten_5 (Flatten) (None, 28224) 0 _________________________________________________________________ dense_9 (Dense) (None, 236) 6661100 _________________________________________________________________ dense_10 (Dense) (None, 1) 237 ================================================================= Total params: 6,847,769 Trainable params: 6,847,769 Non-trainable params: 0 _________________________________________________________________ model.compile(optimizer=keras.optimizers.Adam(lr=0.0001),loss='binary_crossentropy',metrics=['acc']) history = model.fit_generator(train_generator,epochs=30,steps_per_epoch=100,validation_data=test_generator,validation_steps=50) Epoch 1/30 100/100 [==============================] - 13s 131ms/step - loss: 0.6946 - acc: 0.4925 - val_loss: 0.6932 - val_acc: 0.5160 Epoch 2/30 100/100 [==============================] - 12s 116ms/step - loss: 0.6742 - acc: 0.5565 - val_loss: 0.6644 - val_acc: 0.6010 Epoch 3/30 100/100 [==============================] - 12s 116ms/step - loss: 0.6338 - acc: 0.6310 - val_loss: 0.6401 - val_acc: 0.6220 Epoch 4/30 100/100 [==============================] - 12s 116ms/step - loss: 0.6109 - acc: 0.6560 - val_loss: 0.6295 - val_acc: 0.6340 Epoch 5/30 100/100 [==============================] - 12s 116ms/step - loss: 0.5745 - acc: 0.6800 - val_loss: 0.6064 - val_acc: 0.6550 Epoch 6/30 100/100 [==============================] - 12s 116ms/step - loss: 0.5480 - acc: 0.7095 - val_loss: 0.5911 - val_acc: 0.6830 Epoch 7/30 100/100 [==============================] - 12s 116ms/step - loss: 0.5269 - acc: 0.7335 - val_loss: 0.5984 - val_acc: 0.6680 Epoch 8/30 100/100 [==============================] - 12s 116ms/step - loss: 0.5025 - acc: 0.7495 - val_loss: 0.5934 - val_acc: 0.6750 Epoch 9/30 100/100 [==============================] - 12s 116ms/step - loss: 0.4760 - acc: 0.7700 - val_loss: 0.6232 - val_acc: 0.6610 Epoch 10/30 100/100 [==============================] - 12s 116ms/step - loss: 0.4377 - acc: 0.7985 - val_loss: 0.6410 - val_acc: 0.6560 Epoch 11/30 100/100 [==============================] - 12s 117ms/step - loss: 0.4045 - acc: 0.8180 - val_loss: 0.6176 - val_acc: 0.6830 Epoch 12/30 100/100 [==============================] - 12s 116ms/step - loss: 0.3727 - acc: 0.8305 - val_loss: 0.6343 - val_acc: 0.6700 Epoch 13/30 100/100 [==============================] - 12s 116ms/step - loss: 0.3227 - acc: 0.8570 - val_loss: 0.6890 - val_acc: 0.6730 Epoch 14/30 100/100 [==============================] - 12s 117ms/step - loss: 0.2678 - acc: 0.8825 - val_loss: 0.7858 - val_acc: 0.6830 Epoch 15/30 100/100 [==============================] - 12s 116ms/step - loss: 0.2491 - acc: 0.8985 - val_loss: 0.7528 - val_acc: 0.6920 Epoch 16/30 100/100 [==============================] - 12s 116ms/step - loss: 0.1818 - acc: 0.9235 - val_loss: 0.8449 - val_acc: 0.6930 Epoch 17/30 100/100 [==============================] - 12s 117ms/step - loss: 0.1204 - acc: 0.9580 - val_loss: 0.9760 - val_acc: 0.6860 Epoch 18/30 100/100 [==============================] - 12s 118ms/step - loss: 0.0775 - acc: 0.9705 - val_loss: 1.1756 - val_acc: 0.6880 Epoch 19/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0548 - acc: 0.9865 - val_loss: 1.3155 - val_acc: 0.6920 Epoch 20/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0605 - acc: 0.9785 - val_loss: 1.6551 - val_acc: 0.6600 Epoch 21/30 100/100 [==============================] - 12s 117ms/step - loss: 0.0670 - acc: 0.9765 - val_loss: 1.2751 - val_acc: 0.6780 Epoch 22/30 100/100 [==============================] - 12s 117ms/step - loss: 0.0348 - acc: 0.9910 - val_loss: 1.4547 - val_acc: 0.6890 Epoch 23/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0511 - acc: 0.9825 - val_loss: 1.3448 - val_acc: 0.6840 Epoch 24/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0269 - acc: 0.9915 - val_loss: 1.5894 - val_acc: 0.6850 Epoch 25/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0147 - acc: 0.9980 - val_loss: 1.7083 - val_acc: 0.6590 Epoch 26/30 100/100 [==============================] - 12s 118ms/step - loss: 0.0097 - acc: 0.9980 - val_loss: 1.8089 - val_acc: 0.6830 Epoch 27/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0074 - acc: 0.9985 - val_loss: 2.1671 - val_acc: 0.6730 Epoch 28/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0068 - acc: 0.9995 - val_loss: 1.8426 - val_acc: 0.6950 Epoch 29/30 100/100 [==============================] - 12s 116ms/step - loss: 0.0024 - acc: 1.0000 - val_loss: 2.0147 - val_acc: 0.6840 Epoch 30/30 100/100 [==============================] - 12s 118ms/step - loss: 0.0463 - acc: 0.9855 - val_loss: 1.8073 - val_acc: 0.6670 import matplotlib.pyplot as plt %matplotlib inline plt.plot(history.epoch,history.history['loss'],label='loss') plt.plot(history.epoch,history.history['val_loss'],label='val_loss') plt.legend() <matplotlib.legend.Legend at 0x7f1c9763a278>

plt.plot(history .epoch,history.history['acc'],label='acc') plt.plot(history.epoch,history.history['val_acc'],label='val_acc') plt.legend() <matplotlib.legend.Legend at 0x7f1c9759cb00>

雖然有嚴重的過擬合，但值得記錄

總結

以上是生活随笔為你收集整理的kaggle（一）训练猫狗数据集的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。