深度学习之tensorflow2实战：多输出模型

欢迎来到CNN实战，尽管我们刚刚开始，但还是要往前看！让我们开始吧！

数据集

链接：https://pan.baidu.com/s/1zztS32iuNynepLq7jiF6RA
提取码：ilxh，请下载好数据，在开始

导入库

首先导入我们所需要的库

import tensorflow as tffrom tensorflow.keras import layers, Sequential, optimizersimport globimport randomimport osBATCH_SIZE = 32os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'os.environ["CUDA_VISIBLE_DEVICES"] = '2'

预处理

让我们进行数据分类

def load_data():    global all_images, all_label, name_label_type, color_label_type, name_labels, color_labels    all_images = glob.glob('./dataset/*/*')    # 打乱图片顺序    random.shuffle(all_images)    #以‘/’为分隔符得到所有信息    all_labels = [item.split('/')[-1] for item in all_images]    #以‘_’为分隔符得到all_labels里面的第一个信息，第一层文件夹    name_labels = [item.split('_')[0] for item in all_labels]    #以‘_’为分隔符得到all_labels里面的第二个信息，第二层文件夹    color_labels = [item.split('_')[1] for item in all_labels]    #将列表转换为集合    name_label_type = set(item for item in name_labels)    #将列表转换为集合    color_label_type = set(item for item in color_labels)    #将集合转换为字典，并提取标号    name_lable_index = dict((name, index) for index, name in enumerate(name_label_type))    #将集合转换为字典，并提取标号    color_lable_index = dict((color, index) for index, color in enumerate(color_label_type))    #输出为{name||index}    print(name_lable_index)    #输出个数，为3    print(len(name_lable_index))    #输出为{name//index}    print(color_lable_index)    #输出个数，为1812    print(len(color_lable_index))    #有没有都行    name_labels = [name_lable_index[name] for name in name_labels]    color_labels = [color_lable_index[color] for color in color_labels]

准备工作

def load_preprocess_images(path):    image = tf.io.read_file(path)    # 将表示一张图像的三维矩阵重新按照jpeg格式编码并存入文件中。打开这张图像就可以得到和原始图像一样的图像    #转换为tensor    image = tf.image.decode_jpeg(image, channels=3)    # 调整图像大小    image = tf.image.resize(image, [224, 224])    #转换为浮点类型    image = tf.cast(image, tf.float32)    #转换到0~1之间    image = image / 255.0    #例如 0.4✖2 - 1 = -0.2    # 0.6 ✖ 2 - 1 = 0.2    image = 2 * image - 1    return image

进行预处理

[

train_dataset = multi_pred_dataset.take(train_num).repeat().shuffle(train_num).batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)

]

这里的repeat()不要简单地想为重复，而是每次进行batch都要重新扫描数据

def to_dataset():    global train_dataset, valid_dataset, train_num    #用于数据集的构建与预处理    AUTOTUNE = tf.data.experimental.AUTOTUNE    # 图片路径列表直接转数据集    path_dataset = tf.data.Dataset.from_tensor_slices(all_images)    # 把路径数据转成图片tensor数据    image_dataset = path_dataset.map(load_preprocess_images, num_parallel_calls=AUTOTUNE)    #进行特征切片，就是把每一个color_labels和name_labels分为一组    label_dataset = tf.data.Dataset.from_tensor_slices((color_labels, name_labels))    #将图片数据集和标签数据集压缩打包一下    multi_pred_dataset = tf.data.Dataset.zip((image_dataset, label_dataset))    #计算一下个数    all_images_num = len(all_images)    #设置80%为训练集    train_num = int(all_images_num * 0.8)    #提取80%的训练集    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%数据集当作测试集    valid_dataset = multi_pred_dataset.skip(train_num).batch(BATCH_SIZE)

建立模型

def train():    #以顺序连接型设置卷积层    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)    #压缩为单像素的多张图片    x = layers.GlobalAveragePooling2D()(x)    #全连接层    x1 = layers.Dense(1024, activation='relu')(x)    #反向随机失活    x1 = layers.Dropout(0.5)(x1)    #以softmax进行输出    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()        #进行编译    model.compile(        optimizer=optimizers.Adam(learning_rate=0.0001),        loss={            'out_color': 'sparse_categorical_crossentropy',            'out_name': 'sparse_categorical_crossentropy',        },        metrics=['acc']    )    #fit函数返回一个History的对象    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

测试一下吧

if __name__ == '__main__':    load_data()    to_dataset()    history = train()

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'shirt\\00000134.jpg': 1765, 'shirt\\00000300.jpg': 1766, '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: 7,604,055Trainable params: 7,600,215Non-trainable params: 3,840__________________________________________________________________________________________________Epoch 1/1563/63 [==============================] - 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: 0.3438Epoch 2/1563/63 [==============================] - 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: 0.3438Epoch 3/1563/63 [==============================] - 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: 0.4375Epoch 4/1563/63 [==============================] - 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: 0.6562Epoch 5/1563/63 [==============================] - 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: 0.6562Epoch 6/1563/63 [==============================] - 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: 0.6562Epoch 7/1563/63 [==============================] - 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: 0.7812Epoch 8/1563/63 [==============================] - 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: 0.7188Epoch 9/1563/63 [==============================] - 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: 0.9062Epoch 10/1563/63 [==============================] - 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: 1.0000Epoch 11/1563/63 [==============================] - 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: 0.9375Epoch 12/1563/63 [==============================] - 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: 0.9688Epoch 13/1563/63 [==============================] - 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: 0.9375Epoch 14/1563/63 [==============================] - 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: 0.9688Epoch 15/1563/63 [==============================] - 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

绘图

导入我们需要的库

import pandas as pdimport matplotlib.pyplot as plt

history对象是.fit（）操作的输出，并提供内存中所有损失和度量值的记录。它存储为字典，您可以在history中检索。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]}

现在，使用history.history可视化时间损失：

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()