上海開始施行垃圾分類啦。那么我們能不能通過平常學(xué)習(xí)的機器學(xué)習(xí)和深度學(xué)習(xí)的算法來實現(xiàn)一個簡單的垃圾分類的模型呢？
下面主要用過CNN來實現(xiàn)垃圾的分類。在本數(shù)據(jù)集中，垃圾的種類有六種（和上海的標(biāo)準(zhǔn)不一樣），分為玻璃、紙、硬紙板、塑料、金屬、一般垃圾。
該數(shù)據(jù)集包含了2527個生活垃圾圖片。數(shù)據(jù)集的創(chuàng)建者將垃圾分為了6個類別，分別是：
玻璃（glass）共501個圖片
紙（paper）共594個圖片
硬紙板（cardboard）共403個圖片
塑料（plastic）共482個圖片
金屬（metal）共410個圖片
一般垃圾（trash）共137個圖片
物品都是放在白板上在日光/室內(nèi)光源下拍攝的，壓縮后的尺寸為512 * 384。
dataset from https://github.com/garythung/trashnet/tree/master/data
Unzip data/dataset-resized.zip
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense
from tensorflow.keras.models import Sequential
import glob, os, random
base_path ='/Users/zhangwenna/Desktop/dataset-resized'
img_list = glob.glob(os.path.join(base_path, '*/*.jpg'))
print(len(img_list))
# 我們總共有2527張圖片。我們隨機展示其中的6張圖片
for i, img_path in enumerate(random.sample(img_list, 6)):
img = load_img(img_path)
img = img_to_array(img, dtype=np.uint8)
plt.subplot(2, 3, i+1)
plt.imshow(img.squeeze())
output1:
2527
image.png
#對數(shù)據(jù)進行分組
#ImageDataGenerator()是keras.preprocessing.image模塊中的圖片生成器，可以每一次給模型“喂”一個batch_size大小的樣本數(shù)據(jù)，
#同時也可以在每一個批次中對這batch_size個樣本數(shù)據(jù)進行增強，擴充數(shù)據(jù)集大小，增強模型的泛化能力。比如進行旋轉(zhuǎn)，變形，歸一化等等。
train_datagen = ImageDataGenerator(
rescale=1./225, shear_range=0.1, zoom_range=0.1,
width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True,
vertical_flip=True, validation_split=0.1)
#shear_range 剪切強度（逆時針方向的剪切變換角度)
#validation_split: 保留用于驗證的圖像的比例（嚴(yán)格在0和1之間）
test_datagen = ImageDataGenerator(
rescale=1./255, validation_split=0.1)
train_generator = train_datagen.flow_from_directory(
base_path, target_size=(300, 300), batch_size=16,
class_mode='categorical', subset='training', seed=0)
validation_generator = test_datagen.flow_from_directory(
base_path, target_size=(300, 300), batch_size=16,
class_mode='categorical', subset='validation', seed=0)
labels = (train_generator.class_indices)
labels = dict((v,k) for k,v in labels.items())
print(labels)
output2:
Found 2276 images belonging to 6 classes.
Found 251 images belonging to 6 classes.
{0: 'cardboard', 1: 'glass', 2: 'metal', 3: 'paper', 4: 'plastic', 5: 'trash'}
model = Sequential([
Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=(300, 300, 3)),
MaxPooling2D(pool_size=2),
Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'),
MaxPooling2D(pool_size=2),
Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),
MaxPooling2D(pool_size=2),
Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),
MaxPooling2D(pool_size=2),
Flatten(),
Dense(64, activation='relu'),
Dense(6, activation='softmax')
])
#(交叉熵損失函數(shù))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
model.fit_generator(train_generator, epochs=100, steps_per_epoch=2276//32,validation_data=validation_generator,
validation_steps=251//32)
#參數(shù)steps_per_epoch是通過把訓(xùn)練圖像的數(shù)量除以批次大小得出的。例如,有100張圖像且批次大小為50,則steps_per_epoch值為2
output3:
Please use Model.fit, which supports generators.
Epoch 1/100
71/71 [==============================] - 221s 3s/step - loss: 1.7205 - acc: 0.2553 - val_loss: 1.6083 - val_acc: 0.3750
Epoch 2/100
71/71 [==============================] - 121s 2s/step - loss: 1.5227 - acc: 0.3803 - val_loss: 1.4301 - val_acc: 0.4464
Epoch 3/100
71/71 [==============================] - 123s 2s/step - loss: 1.4023 - acc: 0.4428 - val_loss: 1.5464 - val_acc: 0.4286
Epoch 4/100
71/71 [==============================] - 118s 2s/step - loss: 1.3962 - acc: 0.4489 - val_loss: 1.4306 - val_acc: 0.4286
Epoch 5/100
71/71 [==============================] - 116s 2s/step - loss: 1.3639 - acc: 0.4384 - val_loss: 1.3748 - val_acc: 0.4196
Epoch 6/100
71/71 [==============================] - 118s 2s/step - loss: 1.2870 - acc: 0.4850 - val_loss: 1.2453 - val_acc: 0.5536
Epoch 7/100
71/71 [==============================] - 117s 2s/step - loss: 1.2601 - acc: 0.5133 - val_loss: 1.4683 - val_acc: 0.4464
Epoch 8/100
71/71 [==============================] - 117s 2s/step - loss: 1.2186 - acc: 0.5088 - val_loss: 1.2113 - val_acc: 0.5000
Epoch 9/100
71/71 [==============================] - 115s 2s/step - loss: 1.1850 - acc: 0.5214 - val_loss: 1.3347 - val_acc: 0.4464
Epoch 10/100
71/71 [==============================] - 122s 2s/step - loss: 1.1420 - acc: 0.5423 - val_loss: 1.2093 - val_acc: 0.5536
Epoch 11/100
71/71 [==============================] - 112s 2s/step - loss: 1.0990 - acc: 0.5678 - val_loss: 1.1321 - val_acc: 0.5089
Epoch 12/100
71/71 [==============================] - 112s 2s/step - loss: 1.0840 - acc: 0.5721 - val_loss: 1.1863 - val_acc: 0.5357
Epoch 13/100
71/71 [==============================] - 155s 2s/step - loss: 1.0766 - acc: 0.5979 - val_loss: 1.4430 - val_acc: 0.4554
Epoch 14/100
71/71 [==============================] - 169s 2s/step - loss: 0.9695 - acc: 0.6338 - val_loss: 0.9983 - val_acc: 0.6518
Epoch 15/100
71/71 [==============================] - 119s 2s/step - loss: 0.9901 - acc: 0.6294 - val_loss: 1.1473 - val_acc: 0.5625
Epoch 16/100
71/71 [==============================] - 115s 2s/step - loss: 1.0062 - acc: 0.6406 - val_loss: 1.0303 - val_acc: 0.6250
Epoch 17/100
71/71 [==============================] - 114s 2s/step - loss: 0.9439 - acc: 0.6397 - val_loss: 1.0116 - val_acc: 0.5714
Epoch 18/100
71/71 [==============================] - 116s 2s/step - loss: 0.9797 - acc: 0.6391 - val_loss: 1.1799 - val_acc: 0.5268
Epoch 19/100
71/71 [==============================] - 115s 2s/step - loss: 0.9340 - acc: 0.6459 - val_loss: 1.0967 - val_acc: 0.5804
Epoch 20/100
71/71 [==============================] - 114s 2s/step - loss: 0.8780 - acc: 0.6708 - val_loss: 1.0752 - val_acc: 0.5804
Epoch 21/100
71/71 [==============================] - 114s 2s/step - loss: 0.8546 - acc: 0.6824 - val_loss: 1.1991 - val_acc: 0.5714
Epoch 22/100
71/71 [==============================] - 115s 2s/step - loss: 0.8694 - acc: 0.6628 - val_loss: 1.2398 - val_acc: 0.5357
Epoch 23/100
71/71 [==============================] - 117s 2s/step - loss: 0.8411 - acc: 0.6901 - val_loss: 1.1025 - val_acc: 0.6786
Epoch 24/100
71/71 [==============================] - 117s 2s/step - loss: 0.8107 - acc: 0.7130 - val_loss: 1.1774 - val_acc: 0.5536
Epoch 25/100
71/71 [==============================] - 116s 2s/step - loss: 0.8752 - acc: 0.6673 - val_loss: 0.8081 - val_acc: 0.6696
Epoch 26/100
71/71 [==============================] - 111s 2s/step - loss: 0.8150 - acc: 0.7020 - val_loss: 0.9926 - val_acc: 0.6518
Epoch 27/100
71/71 [==============================] - 113s 2s/step - loss: 0.7882 - acc: 0.7104 - val_loss: 0.9890 - val_acc: 0.6339
Epoch 28/100
71/71 [==============================] - 113s 2s/step - loss: 0.7705 - acc: 0.7201 - val_loss: 1.0953 - val_acc: 0.6071
Epoch 29/100
71/71 [==============================] - 112s 2s/step - loss: 0.7430 - acc: 0.7377 - val_loss: 0.8792 - val_acc: 0.6429
Epoch 30/100
71/71 [==============================] - 112s 2s/step - loss: 0.7626 - acc: 0.7210 - val_loss: 0.8883 - val_acc: 0.6518
Epoch 31/100
71/71 [==============================] - 113s 2s/step - loss: 0.8552 - acc: 0.6815 - val_loss: 1.3025 - val_acc: 0.4732
Epoch 32/100
71/71 [==============================] - 112s 2s/step - loss: 0.7941 - acc: 0.7069 - val_loss: 0.8129 - val_acc: 0.7054
Epoch 33/100
71/71 [==============================] - 109s 2s/step - loss: 0.7429 - acc: 0.7313 - val_loss: 0.8716 - val_acc: 0.6696
Epoch 34/100
71/71 [==============================] - 112s 2s/step - loss: 0.6959 - acc: 0.7536 - val_loss: 1.0984 - val_acc: 0.6250
Epoch 35/100
71/71 [==============================] - 111s 2s/step - loss: 0.7375 - acc: 0.7394 - val_loss: 0.8002 - val_acc: 0.6786
Epoch 36/100
71/71 [==============================] - 111s 2s/step - loss: 0.7072 - acc: 0.7333 - val_loss: 0.7551 - val_acc: 0.7321
Epoch 37/100
71/71 [==============================] - 113s 2s/step - loss: 0.7440 - acc: 0.7403 - val_loss: 1.1043 - val_acc: 0.5982
Epoch 38/100
71/71 [==============================] - 111s 2s/step - loss: 0.7527 - acc: 0.7174 - val_loss: 0.8664 - val_acc: 0.6964
Epoch 39/100
71/71 [==============================] - 111s 2s/step - loss: 0.6643 - acc: 0.7473 - val_loss: 0.8213 - val_acc: 0.7232
Epoch 40/100
71/71 [==============================] - 112s 2s/step - loss: 0.7021 - acc: 0.7456 - val_loss: 0.8613 - val_acc: 0.7143
Epoch 41/100
71/71 [==============================] - 113s 2s/step - loss: 0.6386 - acc: 0.7720 - val_loss: 1.0223 - val_acc: 0.6250
Epoch 42/100
71/71 [==============================] - 111s 2s/step - loss: 0.6568 - acc: 0.7447 - val_loss: 0.9515 - val_acc: 0.6786
Epoch 43/100
71/71 [==============================] - 112s 2s/step - loss: 0.6394 - acc: 0.7852 - val_loss: 0.7870 - val_acc: 0.6786
Epoch 44/100
71/71 [==============================] - 113s 2s/step - loss: 0.6437 - acc: 0.7676 - val_loss: 0.8471 - val_acc: 0.6696
Epoch 45/100
71/71 [==============================] - 111s 2s/step - loss: 0.6337 - acc: 0.7623 - val_loss: 0.8950 - val_acc: 0.6964
Epoch 46/100
71/71 [==============================] - 111s 2s/step - loss: 0.5993 - acc: 0.7820 - val_loss: 1.0203 - val_acc: 0.5625
Epoch 47/100
71/71 [==============================] - 117s 2s/step - loss: 0.6087 - acc: 0.7799 - val_loss: 1.0065 - val_acc: 0.5982
Epoch 48/100
71/71 [==============================] - 138s 2s/step - loss: 0.6285 - acc: 0.7722 - val_loss: 0.8781 - val_acc: 0.6875
Epoch 49/100
71/71 [==============================] - 121s 2s/step - loss: 0.5612 - acc: 0.7984 - val_loss: 1.2335 - val_acc: 0.6429
Epoch 50/100
71/71 [==============================] - 116s 2s/step - loss: 0.5699 - acc: 0.7984 - val_loss: 0.9054 - val_acc: 0.6607
Epoch 51/100
71/71 [==============================] - 110s 2s/step - loss: 0.6030 - acc: 0.7909 - val_loss: 0.9821 - val_acc: 0.6429
Epoch 52/100
71/71 [==============================] - 111s 2s/step - loss: 0.6945 - acc: 0.7412 - val_loss: 0.8685 - val_acc: 0.6786
Epoch 53/100
71/71 [==============================] - 113s 2s/step - loss: 0.5679 - acc: 0.7905 - val_loss: 0.8510 - val_acc: 0.6875
Epoch 54/100
71/71 [==============================] - 113s 2s/step - loss: 0.6023 - acc: 0.7835 - val_loss: 0.8247 - val_acc: 0.6518
Epoch 55/100
71/71 [==============================] - 112s 2s/step - loss: 0.5590 - acc: 0.7896 - val_loss: 0.7802 - val_acc: 0.7321
Epoch 56/100
71/71 [==============================] - 114s 2s/step - loss: 0.5679 - acc: 0.8028 - val_loss: 0.7660 - val_acc: 0.6964
Epoch 57/100
71/71 [==============================] - 110s 2s/step - loss: 0.5839 - acc: 0.8028 - val_loss: 0.7611 - val_acc: 0.7321
Epoch 58/100
71/71 [==============================] - 111s 2s/step - loss: 0.5590 - acc: 0.7967 - val_loss: 1.0786 - val_acc: 0.6071
Epoch 59/100
71/71 [==============================] - 116s 2s/step - loss: 0.5194 - acc: 0.8275 - val_loss: 0.7342 - val_acc: 0.7321
Epoch 60/100
71/71 [==============================] - 110s 2s/step - loss: 0.4677 - acc: 0.8185 - val_loss: 0.9167 - val_acc: 0.6786
Epoch 61/100
71/71 [==============================] - 109s 2s/step - loss: 0.4906 - acc: 0.8052 - val_loss: 0.7638 - val_acc: 0.7321
Epoch 62/100
71/71 [==============================] - 112s 2s/step - loss: 0.5267 - acc: 0.8081 - val_loss: 1.2296 - val_acc: 0.5982
Epoch 63/100
71/71 [==============================] - 111s 2s/step - loss: 0.5880 - acc: 0.7909 - val_loss: 0.8299 - val_acc: 0.6964
Epoch 64/100
71/71 [==============================] - 110s 2s/step - loss: 0.5203 - acc: 0.8203 - val_loss: 0.6984 - val_acc: 0.7589
Epoch 65/100
71/71 [==============================] - 110s 2s/step - loss: 0.5617 - acc: 0.8007 - val_loss: 0.8506 - val_acc: 0.6964
Epoch 66/100
71/71 [==============================] - 112s 2s/step - loss: 0.4157 - acc: 0.8530 - val_loss: 0.9649 - val_acc: 0.6786
Epoch 67/100
71/71 [==============================] - 110s 2s/step - loss: 0.4726 - acc: 0.8363 - val_loss: 0.7467 - val_acc: 0.7411
Epoch 68/100
71/71 [==============================] - 115s 2s/step - loss: 0.4825 - acc: 0.8247 - val_loss: 0.9306 - val_acc: 0.6964
Epoch 69/100
71/71 [==============================] - 110s 2s/step - loss: 0.4757 - acc: 0.8363 - val_loss: 1.1517 - val_acc: 0.6161
Epoch 70/100
71/71 [==============================] - 111s 2s/step - loss: 0.4948 - acc: 0.8185 - val_loss: 0.8304 - val_acc: 0.7054
Epoch 71/100
71/71 [==============================] - 110s 2s/step - loss: 0.5174 - acc: 0.8096 - val_loss: 0.8679 - val_acc: 0.6518
Epoch 72/100
71/71 [==============================] - 111s 2s/step - loss: 0.4799 - acc: 0.8274 - val_loss: 0.8524 - val_acc: 0.7321
Epoch 73/100
71/71 [==============================] - 112s 2s/step - loss: 0.4212 - acc: 0.8495 - val_loss: 1.0715 - val_acc: 0.6875
Epoch 74/100
71/71 [==============================] - 111s 2s/step - loss: 0.5003 - acc: 0.8078 - val_loss: 0.8279 - val_acc: 0.7143
Epoch 75/100
71/71 [==============================] - 111s 2s/step - loss: 0.4267 - acc: 0.8425 - val_loss: 0.7447 - val_acc: 0.7500
Epoch 76/100
71/71 [==============================] - 111s 2s/step - loss: 0.4268 - acc: 0.8371 - val_loss: 0.8244 - val_acc: 0.7500
Epoch 77/100
71/71 [==============================] - 111s 2s/step - loss: 0.4720 - acc: 0.8247 - val_loss: 0.8961 - val_acc: 0.6786
Epoch 78/100
71/71 [==============================] - 112s 2s/step - loss: 0.4979 - acc: 0.8204 - val_loss: 0.8691 - val_acc: 0.6429
Epoch 79/100
71/71 [==============================] - 111s 2s/step - loss: 0.4445 - acc: 0.8461 - val_loss: 1.0964 - val_acc: 0.5982
Epoch 80/100
71/71 [==============================] - 112s 2s/step - loss: 0.4660 - acc: 0.8283 - val_loss: 0.9248 - val_acc: 0.6607
Epoch 81/100
71/71 [==============================] - 111s 2s/step - loss: 0.4824 - acc: 0.8222 - val_loss: 1.2059 - val_acc: 0.6339
Epoch 82/100
71/71 [==============================] - 110s 2s/step - loss: 0.4382 - acc: 0.8354 - val_loss: 0.8243 - val_acc: 0.6875
Epoch 83/100
71/71 [==============================] - 111s 2s/step - loss: 0.3791 - acc: 0.8603 - val_loss: 1.3547 - val_acc: 0.5804
Epoch 84/100
71/71 [==============================] - 112s 2s/step - loss: 0.4175 - acc: 0.8468 - val_loss: 1.1149 - val_acc: 0.7321
Epoch 85/100
71/71 [==============================] - 113s 2s/step - loss: 0.6471 - acc: 0.7740 - val_loss: 1.0958 - val_acc: 0.6250
Epoch 86/100
71/71 [==============================] - 113s 2s/step - loss: 0.4434 - acc: 0.8504 - val_loss: 0.8250 - val_acc: 0.6696
Epoch 87/100
71/71 [==============================] - 112s 2s/step - loss: 0.3719 - acc: 0.8559 - val_loss: 0.8524 - val_acc: 0.7589
Epoch 88/100
71/71 [==============================] - 109s 2s/step - loss: 0.3978 - acc: 0.8532 - val_loss: 0.8410 - val_acc: 0.7321
Epoch 89/100
71/71 [==============================] - 111s 2s/step - loss: 0.4387 - acc: 0.8398 - val_loss: 0.8426 - val_acc: 0.7232
Epoch 90/100
71/71 [==============================] - 110s 2s/step - loss: 0.4056 - acc: 0.8594 - val_loss: 0.8563 - val_acc: 0.7232
Epoch 91/100
71/71 [==============================] - 111s 2s/step - loss: 0.3897 - acc: 0.8592 - val_loss: 0.7448 - val_acc: 0.7321
Epoch 92/100
71/71 [==============================] - 110s 2s/step - loss: 0.3947 - acc: 0.8541 - val_loss: 0.7799 - val_acc: 0.7321
Epoch 93/100
71/71 [==============================] - 109s 2s/step - loss: 0.4416 - acc: 0.8488 - val_loss: 0.9649 - val_acc: 0.6518
Epoch 94/100
71/71 [==============================] - 116s 2s/step - loss: 0.3962 - acc: 0.8550 - val_loss: 1.2210 - val_acc: 0.6607
Epoch 95/100
71/71 [==============================] - 124s 2s/step - loss: 0.4087 - acc: 0.8577 - val_loss: 1.0710 - val_acc: 0.6607
Epoch 96/100
71/71 [==============================] - 117s 2s/step - loss: 0.3748 - acc: 0.8671 - val_loss: 0.8149 - val_acc: 0.7589
Epoch 97/100
71/71 [==============================] - 114s 2s/step - loss: 0.3882 - acc: 0.8550 - val_loss: 1.1649 - val_acc: 0.6875
Epoch 98/100
71/71 [==============================] - 115s 2s/step - loss: 0.3485 - acc: 0.8719 - val_loss: 0.9793 - val_acc: 0.6786
Epoch 99/100
71/71 [==============================] - 118s 2s/step - loss: 0.4128 - acc: 0.8477 - val_loss: 1.0489 - val_acc: 0.6964
Epoch 100/100
71/71 [==============================] - 115s 2s/step - loss: 0.3668 - acc: 0.8644 - val_loss: 1.1848 - val_acc: 0.6250
結(jié)果展示 下面我們隨機抽取validation中的16張圖片，展示圖片以及其標(biāo)簽，并且給予我們的預(yù)測。 我們發(fā)現(xiàn)預(yù)測的準(zhǔn)確度還是蠻高的，對于大部分圖片，都能識別出其類別。
test_x, test_y = validation_generator.__getitem__(1)
preds = model.predict(test_x)
plt.figure(figsize=(16, 16))
for i in range(16):
plt.subplot(4, 4, i+1)
plt.title('pred:%s / truth:%s' % (labels[np.argmax(preds[i])], labels[np.argmax(test_y[i])]))
plt.imshow(test_x[i])
output4:
image.png
