歡迎來到CNN實戰，儘管我們剛剛開始，但還是要往前看！讓我們開始吧！
資料集
連結：https://pan.baidu.com/s/1zztS32iuNynepLq7jiF6RA
提取碼：ilxh，請下載好資料，在開始
匯入庫
首先匯入我們所需要的庫
import tensorflow as tf from tensorflow.keras import layers, Sequential, optimizers import glob import random import os BATCH_SIZE = 32 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' os.environ["CUDA_VISIBLE_DEVICES"] = '2'
預處理
讓我們進行資料分類
def load_data(): global all_images, all_label, name_label_type, color_label_type, name_labels, color_labels all_images = glob.glob('./dataset/*/*') # 打亂圖片順序 random.shuffle(all_images) #以‘/’為分隔符得到所有資訊 all_labels = [item.split('/')[-1] for item in all_images] #以‘_’為分隔符得到all_labels裡面的第一個資訊，第一層資料夾 name_labels = [item.split('_')[0] for item in all_labels] #以‘_’為分隔符得到all_labels裡面的第二個資訊，第二層資料夾 color_labels = [item.split('_')[1] for item in all_labels] #將列表轉換為集合 name_label_type = set(item for item in name_labels) #將列表轉換為集合 color_label_type = set(item for item in color_labels) #將集合轉換為字典，並提取標號 name_lable_index = dict((name, index) for index, name in enumerate(name_label_type)) #將集合轉換為字典，並提取標號 color_lable_index = dict((color, index) for index, color in enumerate(color_label_type)) #輸出為{name||index} print(name_lable_index) #輸出個數，為3 print(len(name_lable_index)) #輸出為{name//index} print(color_lable_index) #輸出個數，為1812 print(len(color_lable_index)) #有沒有都行 name_labels = [name_lable_index[name] for name in name_labels] color_labels = [color_lable_index[color] for color in color_labels]
準備工作
def load_preprocess_images(path): image = tf.io.read_file(path) # 將表示一張影像的三維矩陣重新按照jpeg格式編碼並存入檔案中。開啟這張影像就可以得到和原始影像一樣的影像 #轉換為tensor image = tf.image.decode_jpeg(image, channels=3) # 調整影像大小 image = tf.image.resize(image, [224, 224]) #轉換為浮點型別 image = tf.cast(image, tf.float32) #轉換到0~1之間 image = image / 255.0 #例如 0.4✖2 - 1 = -0.2 # 0.6 ✖ 2 - 1 = 0.2 image = 2 * image - 1 return image
進行預處理
[
train_dataset = multi_pred_dataset.take(train_num).repeat().shuffle(train_num).batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)
]
這裡的repeat()不要簡單地想為重複，而是每次進行batch都要重新掃描資料
def to_dataset(): global train_dataset, valid_dataset, train_num #用於資料集的構建與預處理 AUTOTUNE = tf.data.experimental.AUTOTUNE # 圖片路徑列表直接轉資料集 path_dataset = tf.data.Dataset.from_tensor_slices(all_images) # 把路徑資料轉成圖片tensor資料 image_dataset = path_dataset.map(load_preprocess_images, num_parallel_calls=AUTOTUNE) #進行特徵切片，就是把每一個color_labels和name_labels分為一組 label_dataset = tf.data.Dataset.from_tensor_slices((color_labels, name_labels)) #將圖片資料集和標籤資料集壓縮打包一下 multi_pred_dataset = tf.data.Dataset.zip((image_dataset, label_dataset)) #計算一下個數 all_images_num = len(all_images) #設定80%為訓練集 train_num = int(all_images_num * 0.8) #提取80%的訓練集 train_dataset = multi_pred_dataset.take(train_num).repeat().shuffle(train_num).batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE) #提取剩下的20%資料集當作測試集 valid_dataset = multi_pred_dataset.skip(train_num).batch(BATCH_SIZE)
建立模型
def train(): #以順序連線型設定卷積層 model = Sequential([ layers.Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)), layers.BatchNormalization(), layers.Conv2D(64, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), layers.Conv2D(128, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Conv2D(128, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), layers.Conv2D(256, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Conv2D(256, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), layers.Conv2D(512, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Conv2D(512, (3, 3), activation='relu'), layers.BatchNormalization(), layers.MaxPooling2D(), ]) inputs = tf.keras.Input(shape=(224, 224, 3)) x = model(inputs) #壓縮為單畫素的多張圖片 x = layers.GlobalAveragePooling2D()(x) #全連線層 x1 = layers.Dense(1024, activation='relu')(x) #反向隨機失活 x1 = layers.Dropout(0.5)(x1) #以softmax進行輸出 out_color = layers.Dense(len(color_label_type), activation='softmax', name='out_color')(x1) x2 = layers.Dense(1024, activation='relu')(x) x2 = layers.Dropout(0.7)(x2) out_name = layers.Dense(len(name_label_type), activation='softmax', name='out_name')(x2) model = tf.keras.Model(inputs=inputs, outputs=[out_color, out_name]) model.summary() #進行編譯 model.compile( optimizer=optimizers.Adam(learning_rate=0.0001), loss={ 'out_color': 'sparse_categorical_crossentropy', 'out_name': 'sparse_categorical_crossentropy', }, metrics=['acc'] ) #fit函式返回一個History的物件 history = model.fit( train_dataset, epochs=15, steps_per_epoch=train_num // BATCH_SIZE, validation_split=0.1, validation_data=valid_dataset, validation_steps=1 ) model.save('multi_pred.h5') return history
測試一下吧
if __name__ == '__main__': load_data() to_dataset() history = train()
{'dataset\\black': 0, 'dataset\\red': 1, 'dataset\\blue': 2}
3
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'shirt\\00000146.jpg': 1767, 'dress\\00000077.jpg': 1768, 'shirt\\00000143.jpg': 1769, 'dress\\00000335.JPG': 1770, 'shirt\\00000161.jpg': 1771, 'shoes\\00000150.jpg': 1772, 'dress\\00000257.gif': 1773, 'shoes\\00000285.jpg': 1774, 'jeans\\00000291.png': 1775, 'dress\\00000361.jpg': 1776, 'shoes\\00000255.jpg': 1777, 'jeans\\00000165.png': 1778, 'shirt\\00000312.jpg': 1779, 'dress\\00000268.jpg': 1780, 'dress\\00000153.jpg': 1781, 'shirt\\00000291.jpg': 1782, 'shoes\\00000371.jpg': 1783, 'jeans\\00000325.jpg': 1784, 'dress\\00000028.jpg': 1785, 'jeans\\00000310.jpg': 1786, 'shoes\\00000069.jpg': 1787, 'shirt\\00000178.jpg': 1788, 'jeans\\00000359.jpg': 1789, 'shoes\\00000152.jpg': 1790, 'dress\\00000349.jpg': 1791, 'dress\\00000142.jpg': 1792, 'shoes\\00000008.jpg': 1793, 'shoes\\00000329.JPG': 1794, 'jeans\\00000068.jpg': 1795, 'shirt\\00000168.jpg': 1796, 'dress\\00000280.jpg': 1797, 'shirt\\00000230.jpg': 1798, 'shirt\\00000370.jpeg': 1799, 'jeans\\00000000.jpg': 1800, 'shirt\\00000287.png': 1801, 'shirt\\00000200.jpg': 1802, 'jeans\\00000322.jpeg': 1803, 'dress\\00000051.jpg': 1804, 'dress\\00000209.jpg': 1805, 'shoes\\00000180.jpg': 1806, 'dress\\00000194.jpg': 1807, 'dress\\00000288.jpg': 1808, 'dress\\00000063.jpg': 1809, 'shirt\\00000350.jpg': 1810, 'shoes\\00000012.jpg': 1811}
1812
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 224, 224, 3 0 []
)]
sequential_1 (Sequential) (None, 10, 10, 512) 4693056 ['input_2[0][0]']
global_average_pooling2d_1 (Gl (None, 512) 0 ['sequential_1[0][0]']
obalAveragePooling2D)
dense_2 (Dense) (None, 1024) 525312 ['global_average_pooling2d_1[0][0
]']
dense_3 (Dense) (None, 1024) 525312 ['global_average_pooling2d_1[0][0
]']
dropout_2 (Dropout) (None, 1024) 0 ['dense_2[0][0]']
dropout_3 (Dropout) (None, 1024) 0 ['dense_3[0][0]']
out_color (Dense) (None, 1812) 1857300 ['dropout_2[0][0]']
out_name (Dense) (None, 3) 3075 ['dropout_3[0][0]']
==================================================================================================
Total params: 7,604,055
Trainable params: 7,600,215
Non-trainable params: 3,840
__________________________________________________________________________________________________
Epoch 1/15
63/63 [==============================] - 551s 9s/step - loss: 7.7720 - out_color_loss: 7.6270 - out_name_loss: 0.1449 - out_color_acc: 4.9603e-04 - out_name_acc: 0.9489 - val_loss: 8.8415 - val_out_color_loss: 7.6047 - val_out_name_loss: 1.2368 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.3438
Epoch 2/15
63/63 [==============================] - 544s 9s/step - loss: 7.4915 - out_color_loss: 7.4127 - out_name_loss: 0.0788 - out_color_acc: 0.0035 - out_name_acc: 0.9742 - val_loss: 9.4525 - val_out_color_loss: 7.8520 - val_out_name_loss: 1.6005 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.3438
Epoch 3/15
63/63 [==============================] - 550s 9s/step - loss: 7.1851 - out_color_loss: 7.0988 - out_name_loss: 0.0863 - out_color_acc: 0.0050 - out_name_acc: 0.9712 - val_loss: 9.8440 - val_out_color_loss: 8.2164 - val_out_name_loss: 1.6276 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.4375
Epoch 4/15
63/63 [==============================] - 559s 9s/step - loss: 6.7626 - out_color_loss: 6.6860 - out_name_loss: 0.0766 - out_color_acc: 0.0079 - out_name_acc: 0.9727 - val_loss: 10.4185 - val_out_color_loss: 8.6171 - val_out_name_loss: 1.8014 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.6562
Epoch 5/15
63/63 [==============================] - 545s 9s/step - loss: 6.3178 - out_color_loss: 6.2775 - out_name_loss: 0.0403 - out_color_acc: 0.0184 - out_name_acc: 0.9871 - val_loss: 10.2609 - val_out_color_loss: 9.2650 - val_out_name_loss: 0.9959 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.6562
Epoch 6/15
63/63 [==============================] - 542s 9s/step - loss: 5.9863 - out_color_loss: 5.9314 - out_name_loss: 0.0549 - out_color_acc: 0.0243 - out_name_acc: 0.9816 - val_loss: 10.6269 - val_out_color_loss: 9.6827 - val_out_name_loss: 0.9443 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.6562
Epoch 7/15
63/63 [==============================] - 543s 9s/step - loss: 5.6085 - out_color_loss: 5.5616 - out_name_loss: 0.0469 - out_color_acc: 0.0367 - out_name_acc: 0.9836 - val_loss: 10.2469 - val_out_color_loss: 9.7589 - val_out_name_loss: 0.4880 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.7812
Epoch 8/15
63/63 [==============================] - 552s 9s/step - loss: 5.2854 - out_color_loss: 5.2536 - out_name_loss: 0.0318 - out_color_acc: 0.0352 - out_name_acc: 0.9871 - val_loss: 11.0552 - val_out_color_loss: 10.1455 - val_out_name_loss: 0.9096 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.7188
Epoch 9/15
63/63 [==============================] - 553s 9s/step - loss: 5.0149 - out_color_loss: 4.9681 - out_name_loss: 0.0468 - out_color_acc: 0.0501 - out_name_acc: 0.9836 - val_loss: 10.6914 - val_out_color_loss: 10.4317 - val_out_name_loss: 0.2597 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9062
Epoch 10/15
63/63 [==============================] - 543s 9s/step - loss: 4.8085 - out_color_loss: 4.7618 - out_name_loss: 0.0467 - out_color_acc: 0.0635 - out_name_acc: 0.9856 - val_loss: 11.1986 - val_out_color_loss: 11.1860 - val_out_name_loss: 0.0126 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 1.0000
Epoch 11/15
63/63 [==============================] - 539s 9s/step - loss: 4.5344 - out_color_loss: 4.4990 - out_name_loss: 0.0355 - out_color_acc: 0.0843 - out_name_acc: 0.9851 - val_loss: 11.4026 - val_out_color_loss: 11.2238 - val_out_name_loss: 0.1788 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9375
Epoch 12/15
63/63 [==============================] - 541s 9s/step - loss: 4.3190 - out_color_loss: 4.2725 - out_name_loss: 0.0465 - out_color_acc: 0.1086 - out_name_acc: 0.9836 - val_loss: 12.0516 - val_out_color_loss: 11.9839 - val_out_name_loss: 0.0677 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9688
Epoch 13/15
63/63 [==============================] - 537s 9s/step - loss: 4.1122 - out_color_loss: 4.0877 - out_name_loss: 0.0245 - out_color_acc: 0.1314 - out_name_acc: 0.9916 - val_loss: 12.1688 - val_out_color_loss: 11.9726 - val_out_name_loss: 0.1962 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9375
Epoch 14/15
63/63 [==============================] - 537s 9s/step - loss: 3.9003 - out_color_loss: 3.8640 - out_name_loss: 0.0363 - out_color_acc: 0.1567 - out_name_acc: 0.9876 - val_loss: 12.8494 - val_out_color_loss: 12.7712 - val_out_name_loss: 0.0782 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9688
Epoch 15/15
63/63 [==============================] - 534s 8s/step - loss: 3.7464 - out_color_loss: 3.7241 - out_name_loss: 0.0223 - out_color_acc: 0.1716 - out_name_acc: 0.9906 - val_loss: 13.4787 - val_out_color_loss: 13.3881 - val_out_name_loss: 0.0906 - val_out_color_acc: 0.0000e+00 - val_out_name_acc: 0.9688
繪圖
匯入我們需要的庫
import pandas as pd import matplotlib.pyplot as plt
history物件是.fit（）操作的輸出，並提供記憶體中所有損失和度量值的記錄。它儲存為字典，您可以在history中檢索。history：
history.history
{'loss': [7.805914878845215,
7.481752395629883,
7.16594934463501,
6.768990516662598,
6.301596164703369,
5.92037296295166,
5.5305562019348145,
5.187618732452393,
4.939748764038086,
4.631637096405029,
4.405154705047607,
4.145672798156738,
4.006145000457764,
3.867635488510132,
3.6568844318389893],
'out_color_loss': [7.609991550445557,
7.401507377624512,
7.084959983825684,
6.6868791580200195,
6.238056182861328,
5.863605499267578,
5.48148250579834,
5.145328044891357,
4.8934125900268555,
4.5913496017456055,
4.366715431213379,
4.108206272125244,
3.958033561706543,
3.838737964630127,
3.623124599456787],
'out_name_loss': [0.19592536985874176,
0.08024449646472931,
0.08099038153886795,
0.08211344480514526,
0.06354033946990967,
0.056765757501125336,
0.049073684960603714,
0.04229027032852173,
0.046335890889167786,
0.0402863435447216,
0.038440391421318054,
0.03746694326400757,
0.04811092093586922,
0.02889903075993061,
0.0337597094476223],
'out_color_acc': [0.0019841270986944437,
0.0024801588151603937,
0.0059523810632526875,
0.013888888992369175,
0.010912698693573475,
0.0252976194024086,
0.0367063507437706,
0.0481150783598423,
0.0605158731341362,
0.0927579328417778,
0.0992063507437706,
0.1225198432803154,
0.1383928507566452,
0.1527777761220932,
0.1870039701461792],
'out_name_acc': [0.935019850730896,
0.9751983880996704,
0.9722222089767456,
0.976190447807312,
0.9791666865348816,
0.9826388955116272,
0.983134925365448,
0.9866071343421936,
0.9836309552192688,
0.9841269850730896,
0.9821428656578064,
0.9885912537574768,
0.985615074634552,
0.9910714030265808,
0.9885912537574768],
'val_loss': [8.54788875579834,
8.789600372314453,
8.75654125213623,
8.980286598205566,
9.147688865661621,
9.493474960327148,
9.888298034667969,
9.873955726623535,
10.239419937133789,
10.923574447631836,
11.744060516357422,
11.921720504760742,
12.956429481506348,
13.046647071838379,
13.293322563171387],
'val_out_color_loss': [7.518805503845215,
7.714929580688477,
7.906545639038086,
8.21251106262207,
8.608160018920898,
9.020652770996094,
9.407577514648438,
9.865028381347656,
10.233110427856445,
10.912347793579102,
11.735987663269043,
11.911794662475586,
12.955001831054688,
12.997121810913086,
13.278850555419922],
'val_out_name_loss': [1.029083013534546,
1.0746711492538452,
0.8499959111213684,
0.7677759528160095,
0.5395288467407227,
0.4728219211101532,
0.4807209372520447,
0.00892757810652256,
0.0063096145167946815,
0.011226941831409931,
0.00807263981550932,
0.009926014579832554,
0.0014275459107011557,
0.049525510519742966,
0.014471798203885555],
'val_out_color_acc': [0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0],
'val_out_name_acc': [0.65625,
0.53125,
0.53125,
0.875,
0.84375,
0.875,
0.875,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.96875,
1.0]}
現在，使用history.history視覺化時間損失：
df_loss_acc = pd.DataFrame(history.history) df_loss= df_loss_acc[['out_color_loss','val_out_color_loss']] df_loss.rename(columns={'out_color_loss':'train','val_out_color_loss':'validation'},inplace=True) df_acc= df_loss_acc[['out_color_acc','val_out_color_acc']] df_acc.rename(columns={'out_color_acc':'train','val_out_color_acc':'validation'},inplace=True) df_loss= df_loss_acc[['out_name_loss','val_out_name_loss']] df_loss.rename(columns={'out_name_loss':'train','val_out_name_loss':'validation'},inplace=True) df_acc= df_loss_acc[['out_name_acc','val_out_name_acc']] df_acc.rename(columns={'out_name_acc':'train','val_name_color_acc':'validation'},inplace=True) df_loss.plot(title='color_Model loss',figsize=(12,8)).set(xlabel='Epoch',ylabel='Loss') df_acc.plot(title='color_Model Accuracy',figsize=(12,8)).set(xlabel='Epoch',ylabel='Accuracy') df_loss.plot(title='name_Model loss',figsize=(12,8)).set(xlabel='Epoch',ylabel='Loss') df_acc.plot(title='name_Model Accuracy',figsize=(12,8)).set(xlabel='Epoch',ylabel='Accuracy') plt.show()
我也很無語的，哈哈val_out_color_acc竟然是0，說明模型的複雜度還是不夠高，好了今天的實戰就到這裡了，能力有限，希望高人指導
在圖形出來後，就更加的不理解？？？？？？