tensorflow 实现 CNN 模型垃圾分类算法

import numpy as npimport matplotlib.pyplot as pltfrom tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_imgfrom tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Densefrom 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))
# 我们总共有2527张图片。我们随机展示其中的6张图片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())

#对数据进行分组#ImageDataGenerator()是keras.preprocessing.image模块中的图片生成器，可以每一次给模型“喂”一个batch_size大小的样本数据，#同时也可以在每一个批次中对这batch_size个样本数据进行增强，扩充数据集大小，增强模型的泛化能力。比如进行旋转，变形，归一化等等。
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 剪切强度（逆时针方向的剪切变换角度)#validation_split: 保留用于验证的图像的比例（严格在0和1之间）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)

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')])#(交叉熵损失函数) 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)
#参数steps_per_epoch是通过把训练图像的数量除以批次大小得出的。例如,有100张图像且批次大小为50,则steps_per_epoch值为2

Epoch 1/10071/71 [==============================] - 221s 3s/step - loss: 1.7205 - acc: 0.2553 - val_loss: 1.6083 - val_acc: 0.3750Epoch 2/10071/71 [==============================] - 121s 2s/step - loss: 1.5227 - acc: 0.3803 - val_loss: 1.4301 - val_acc: 0.4464Epoch 3/10071/71 [==============================] - 123s 2s/step - loss: 1.4023 - acc: 0.4428 - val_loss: 1.5464 - val_acc: 0.4286Epoch 4/10071/71 [==============================] - 118s 2s/step - loss: 1.3962 - acc: 0.4489 - val_loss: 1.4306 - val_acc: 0.4286Epoch 5/10071/71 [==============================] - 116s 2s/step - loss: 1.3639 - acc: 0.4384 - val_loss: 1.3748 - val_acc: 0.4196Epoch 6/10071/71 [==============================] - 118s 2s/step - loss: 1.2870 - acc: 0.4850 - val_loss: 1.2453 - val_acc: 0.5536Epoch 7/10071/71 [==============================] - 117s 2s/step - loss: 1.2601 - acc: 0.5133 - val_loss: 1.4683 - val_acc: 0.4464Epoch 8/10071/71 [==============================] - 117s 2s/step - loss: 1.2186 - acc: 0.5088 - val_loss: 1.2113 - val_acc: 0.5000Epoch 9/10071/71 [==============================] - 115s 2s/step - loss: 1.1850 - acc: 0.5214 - val_loss: 1.3347 - val_acc: 0.4464Epoch 10/10071/71 [==============================] - 122s 2s/step - loss: 1.1420 - acc: 0.5423 - val_loss: 1.2093 - val_acc: 0.5536Epoch 11/10071/71 [==============================] - 112s 2s/step - loss: 1.0990 - acc: 0.5678 - val_loss: 1.1321 - val_acc: 0.5089Epoch 12/10071/71 [==============================] - 112s 2s/step - loss: 1.0840 - acc: 0.5721 - val_loss: 1.1863 - val_acc: 0.5357Epoch 13/10071/71 [==============================] - 155s 2s/step - loss: 1.0766 - acc: 0.5979 - val_loss: 1.4430 - val_acc: 0.4554Epoch 14/10071/71 [==============================] - 169s 2s/step - loss: 0.9695 - acc: 0.6338 - val_loss: 0.9983 - val_acc: 0.6518Epoch 15/10071/71 [==============================] - 119s 2s/step - loss: 0.9901 - acc: 0.6294 - val_loss: 1.1473 - val_acc: 0.5625Epoch 16/10071/71 [==============================] - 115s 2s/step - loss: 1.0062 - acc: 0.6406 - val_loss: 1.0303 - val_acc: 0.6250Epoch 17/10071/71 [==============================] - 114s 2s/step - loss: 0.9439 - acc: 0.6397 - val_loss: 1.0116 - val_acc: 0.5714Epoch 18/10071/71 [==============================] - 116s 2s/step - loss: 0.9797 - acc: 0.6391 - val_loss: 1.1799 - val_acc: 0.5268Epoch 19/10071/71 [==============================] - 115s 2s/step - loss: 0.9340 - acc: 0.6459 - val_loss: 1.0967 - val_acc: 0.5804Epoch 20/10071/71 [==============================] - 114s 2s/step - loss: 0.8780 - acc: 0.6708 - val_loss: 1.0752 - val_acc: 0.5804Epoch 21/10071/71 [==============================] - 114s 2s/step - loss: 0.8546 - acc: 0.6824 - val_loss: 1.1991 - val_acc: 0.5714Epoch 22/10071/71 [==============================] - 115s 2s/step - loss: 0.8694 - acc: 0.6628 - val_loss: 1.2398 - val_acc: 0.5357Epoch 23/10071/71 [==============================] - 117s 2s/step - loss: 0.8411 - acc: 0.6901 - val_loss: 1.1025 - val_acc: 0.6786Epoch 24/10071/71 [==============================] - 117s 2s/step - loss: 0.8107 - acc: 0.7130 - val_loss: 1.1774 - val_acc: 0.5536Epoch 25/10071/71 [==============================] - 116s 2s/step - loss: 0.8752 - acc: 0.6673 - val_loss: 0.8081 - val_acc: 0.6696Epoch 26/10071/71 [==============================] - 111s 2s/step - loss: 0.8150 - acc: 0.7020 - val_loss: 0.9926 - val_acc: 0.6518Epoch 27/10071/71 [==============================] - 113s 2s/step - loss: 0.7882 - acc: 0.7104 - val_loss: 0.9890 - val_acc: 0.6339Epoch 28/10071/71 [==============================] - 113s 2s/step - loss: 0.7705 - acc: 0.7201 - val_loss: 1.0953 - val_acc: 0.6071Epoch 29/10071/71 [==============================] - 112s 2s/step - loss: 0.7430 - acc: 0.7377 - val_loss: 0.8792 - val_acc: 0.6429Epoch 30/10071/71 [==============================] - 112s 2s/step - loss: 0.7626 - acc: 0.7210 - val_loss: 0.8883 - val_acc: 0.6518Epoch 31/10071/71 [==============================] - 113s 2s/step - loss: 0.8552 - acc: 0.6815 - val_loss: 1.3025 - val_acc: 0.4732Epoch 32/10071/71 [==============================] - 112s 2s/step - loss: 0.7941 - acc: 0.7069 - val_loss: 0.8129 - val_acc: 0.7054Epoch 33/10071/71 [==============================] - 109s 2s/step - loss: 0.7429 - acc: 0.7313 - val_loss: 0.8716 - val_acc: 0.6696Epoch 34/10071/71 [==============================] - 112s 2s/step - loss: 0.6959 - acc: 0.7536 - val_loss: 1.0984 - val_acc: 0.6250Epoch 35/10071/71 [==============================] - 111s 2s/step - loss: 0.7375 - acc: 0.7394 - val_loss: 0.8002 - val_acc: 0.6786Epoch 36/10071/71 [==============================] - 111s 2s/step - loss: 0.7072 - acc: 0.7333 - val_loss: 0.7551 - val_acc: 0.7321Epoch 37/10071/71 [==============================] - 113s 2s/step - loss: 0.7440 - acc: 0.7403 - val_loss: 1.1043 - val_acc: 0.5982Epoch 38/10071/71 [==============================] - 111s 2s/step - loss: 0.7527 - acc: 0.7174 - val_loss: 0.8664 - val_acc: 0.6964Epoch 39/10071/71 [==============================] - 111s 2s/step - loss: 0.6643 - acc: 0.7473 - val_loss: 0.8213 - val_acc: 0.7232Epoch 40/10071/71 [==============================] - 112s 2s/step - loss: 0.7021 - acc: 0.7456 - val_loss: 0.8613 - val_acc: 0.7143Epoch 41/10071/71 [==============================] - 113s 2s/step - loss: 0.6386 - acc: 0.7720 - val_loss: 1.0223 - val_acc: 0.6250Epoch 42/10071/71 [==============================] - 111s 2s/step - loss: 0.6568 - acc: 0.7447 - val_loss: 0.9515 - val_acc: 0.6786Epoch 43/10071/71 [==============================] - 112s 2s/step - loss: 0.6394 - acc: 0.7852 - val_loss: 0.7870 - val_acc: 0.6786Epoch 44/10071/71 [==============================] - 113s 2s/step - loss: 0.6437 - acc: 0.7676 - val_loss: 0.8471 - val_acc: 0.6696Epoch 45/10071/71 [==============================] - 111s 2s/step - loss: 0.6337 - acc: 0.7623 - val_loss: 0.8950 - val_acc: 0.6964Epoch 46/10071/71 [==============================] - 111s 2s/step - loss: 0.5993 - acc: 0.7820 - val_loss: 1.0203 - val_acc: 0.5625Epoch 47/10071/71 [==============================] - 117s 2s/step - loss: 0.6087 - acc: 0.7799 - val_loss: 1.0065 - val_acc: 0.5982Epoch 48/10071/71 [==============================] - 138s 2s/step - loss: 0.6285 - acc: 0.7722 - val_loss: 0.8781 - val_acc: 0.6875Epoch 49/10071/71 [==============================] - 121s 2s/step - loss: 0.5612 - acc: 0.7984 - val_loss: 1.2335 - val_acc: 0.6429Epoch 50/10071/71 [==============================] - 116s 2s/step - loss: 0.5699 - acc: 0.7984 - val_loss: 0.9054 - val_acc: 0.6607Epoch 51/10071/71 [==============================] - 110s 2s/step - loss: 0.6030 - acc: 0.7909 - val_loss: 0.9821 - val_acc: 0.6429Epoch 52/10071/71 [==============================] - 111s 2s/step - loss: 0.6945 - acc: 0.7412 - val_loss: 0.8685 - val_acc: 0.6786Epoch 53/10071/71 [==============================] - 113s 2s/step - loss: 0.5679 - acc: 0.7905 - val_loss: 0.8510 - val_acc: 0.6875Epoch 54/10071/71 [==============================] - 113s 2s/step - loss: 0.6023 - acc: 0.7835 - val_loss: 0.8247 - val_acc: 0.6518Epoch 55/10071/71 [==============================] - 112s 2s/step - loss: 0.5590 - acc: 0.7896 - val_loss: 0.7802 - val_acc: 0.7321Epoch 56/10071/71 [==============================] - 114s 2s/step - loss: 0.5679 - acc: 0.8028 - val_loss: 0.7660 - val_acc: 0.6964Epoch 57/10071/71 [==============================] - 110s 2s/step - loss: 0.5839 - acc: 0.8028 - val_loss: 0.7611 - val_acc: 0.7321Epoch 58/10071/71 [==============================] - 111s 2s/step - loss: 0.5590 - acc: 0.7967 - val_loss: 1.0786 - val_acc: 0.6071Epoch 59/10071/71 [==============================] - 116s 2s/step - loss: 0.5194 - acc: 0.8275 - val_loss: 0.7342 - val_acc: 0.7321Epoch 60/10071/71 [==============================] - 110s 2s/step - loss: 0.4677 - acc: 0.8185 - val_loss: 0.9167 - val_acc: 0.6786Epoch 61/10071/71 [==============================] - 109s 2s/step - loss: 0.4906 - acc: 0.8052 - val_loss: 0.7638 - val_acc: 0.7321Epoch 62/10071/71 [==============================] - 112s 2s/step - loss: 0.5267 - acc: 0.8081 - val_loss: 1.2296 - val_acc: 0.5982Epoch 63/10071/71 [==============================] - 111s 2s/step - loss: 0.5880 - acc: 0.7909 - val_loss: 0.8299 - val_acc: 0.6964Epoch 64/10071/71 [==============================] - 110s 2s/step - loss: 0.5203 - acc: 0.8203 - val_loss: 0.6984 - val_acc: 0.7589Epoch 65/10071/71 [==============================] - 110s 2s/step - loss: 0.5617 - acc: 0.8007 - val_loss: 0.8506 - val_acc: 0.6964Epoch 66/10071/71 [==============================] - 112s 2s/step - loss: 0.4157 - acc: 0.8530 - val_loss: 0.9649 - val_acc: 0.6786Epoch 67/10071/71 [==============================] - 110s 2s/step - loss: 0.4726 - acc: 0.8363 - val_loss: 0.7467 - val_acc: 0.7411Epoch 68/10071/71 [==============================] - 115s 2s/step - loss: 0.4825 - acc: 0.8247 - val_loss: 0.9306 - val_acc: 0.6964Epoch 69/10071/71 [==============================] - 110s 2s/step - loss: 0.4757 - acc: 0.8363 - val_loss: 1.1517 - val_acc: 0.6161Epoch 70/10071/71 [==============================] - 111s 2s/step - loss: 0.4948 - acc: 0.8185 - val_loss: 0.8304 - val_acc: 0.7054Epoch 71/10071/71 [==============================] - 110s 2s/step - loss: 0.5174 - acc: 0.8096 - val_loss: 0.8679 - val_acc: 0.6518Epoch 72/10071/71 [==============================] - 111s 2s/step - loss: 0.4799 - acc: 0.8274 - val_loss: 0.8524 - val_acc: 0.7321Epoch 73/10071/71 [==============================] - 112s 2s/step - loss: 0.4212 - acc: 0.8495 - val_loss: 1.0715 - val_acc: 0.6875Epoch 74/10071/71 [==============================] - 111s 2s/step - loss: 0.5003 - acc: 0.8078 - val_loss: 0.8279 - val_acc: 0.7143Epoch 75/10071/71 [==============================] - 111s 2s/step - loss: 0.4267 - acc: 0.8425 - val_loss: 0.7447 - val_acc: 0.7500Epoch 76/10071/71 [==============================] - 111s 2s/step - loss: 0.4268 - acc: 0.8371 - val_loss: 0.8244 - val_acc: 0.7500Epoch 77/10071/71 [==============================] - 111s 2s/step - loss: 0.4720 - acc: 0.8247 - val_loss: 0.8961 - val_acc: 0.6786Epoch 78/10071/71 [==============================] - 112s 2s/step - loss: 0.4979 - acc: 0.8204 - val_loss: 0.8691 - val_acc: 0.6429Epoch 79/10071/71 [==============================] - 111s 2s/step - loss: 0.4445 - acc: 0.8461 - val_loss: 1.0964 - val_acc: 0.5982Epoch 80/10071/71 [==============================] - 112s 2s/step - loss: 0.4660 - acc: 0.8283 - val_loss: 0.9248 - val_acc: 0.6607Epoch 81/10071/71 [==============================] - 111s 2s/step - loss: 0.4824 - acc: 0.8222 - val_loss: 1.2059 - val_acc: 0.6339Epoch 82/10071/71 [==============================] - 110s 2s/step - loss: 0.4382 - acc: 0.8354 - val_loss: 0.8243 - val_acc: 0.6875Epoch 83/10071/71 [==============================] - 111s 2s/step - loss: 0.3791 - acc: 0.8603 - val_loss: 1.3547 - val_acc: 0.5804Epoch 84/10071/71 [==============================] - 112s 2s/step - loss: 0.4175 - acc: 0.8468 - val_loss: 1.1149 - val_acc: 0.7321Epoch 85/10071/71 [==============================] - 113s 2s/step - loss: 0.6471 - acc: 0.7740 - val_loss: 1.0958 - val_acc: 0.6250Epoch 86/10071/71 [==============================] - 113s 2s/step - loss: 0.4434 - acc: 0.8504 - val_loss: 0.8250 - val_acc: 0.6696Epoch 87/10071/71 [==============================] - 112s 2s/step - loss: 0.3719 - acc: 0.8559 - val_loss: 0.8524 - val_acc: 0.7589Epoch 88/10071/71 [==============================] - 109s 2s/step - loss: 0.3978 - acc: 0.8532 - val_loss: 0.8410 - val_acc: 0.7321Epoch 89/10071/71 [==============================] - 111s 2s/step - loss: 0.4387 - acc: 0.8398 - val_loss: 0.8426 - val_acc: 0.7232Epoch 90/10071/71 [==============================] - 110s 2s/step - loss: 0.4056 - acc: 0.8594 - val_loss: 0.8563 - val_acc: 0.7232Epoch 91/10071/71 [==============================] - 111s 2s/step - loss: 0.3897 - acc: 0.8592 - val_loss: 0.7448 - val_acc: 0.7321Epoch 92/10071/71 [==============================] - 110s 2s/step - loss: 0.3947 - acc: 0.8541 - val_loss: 0.7799 - val_acc: 0.7321Epoch 93/10071/71 [==============================] - 109s 2s/step - loss: 0.4416 - acc: 0.8488 - val_loss: 0.9649 - val_acc: 0.6518Epoch 94/10071/71 [==============================] - 116s 2s/step - loss: 0.3962 - acc: 0.8550 - val_loss: 1.2210 - val_acc: 0.6607Epoch 95/10071/71 [==============================] - 124s 2s/step - loss: 0.4087 - acc: 0.8577 - val_loss: 1.0710 - val_acc: 0.6607Epoch 96/10071/71 [==============================] - 117s 2s/step - loss: 0.3748 - acc: 0.8671 - val_loss: 0.8149 - val_acc: 0.7589Epoch 97/10071/71 [==============================] - 114s 2s/step - loss: 0.3882 - acc: 0.8550 - val_loss: 1.1649 - val_acc: 0.6875Epoch 98/10071/71 [==============================] - 115s 2s/step - loss: 0.3485 - acc: 0.8719 - val_loss: 0.9793 - val_acc: 0.6786Epoch 99/10071/71 [==============================] - 118s 2s/step - loss: 0.4128 - acc: 0.8477 - val_loss: 1.0489 - val_acc: 0.6964Epoch 100/10071/71 [==============================] - 115s 2s/step - loss: 0.3668 - acc: 0.8644 - val_loss: 1.1848 - val_acc: 0.6250

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])