m0_52062055 2022-08-31 16:21 采纳率: 30%
浏览 497
已结题

python使用model.compile方法的时候,遇到AttributeError: 'NoneType' object has no attribute 'compile'这个问题

在跑《deep learning for computer vision with python》第二本第11章的googlenet程序时候,遇到使用model.compile方法出现bug。'NoneType' object has no attribute 'compile'
import matplotlib

matplotlib.use("Agg")

from sklearn.preprocessing import LabelBinarizer
from pyimagesearch.nn.conv import MiniGoogLeNet
from pyimagesearch.callbacks import TrainingMonitor
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import LearningRateScheduler
from keras.optimizers import SGD
from keras.datasets import cifar10
import numpy as np
import argparse
import os

NUM_EPOCHS = 70
INIT_LR = 5e-3


def poly_decay(epoch):
    maxEpochs = NUM_EPOCHS
    baseLR = INIT_LR
    power = 1.0

    alpha = baseLR * (1 - (epoch / float(maxEpochs))) ** power

    return alpha


ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required=True,
                help="path to output model")
ap.add_argument("-o", "--output", required=True,
                help="path to output directory (logs, plots, etc.)")
args = vars(ap.parse_args())

print("[INFO] loading CIFAR-10 data...")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float")
testX = testX.astype("float")

mean = np.mean(trainX, axis=0)
trainX -= mean
testX -= mean

lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)

aug = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1,
                         horizontal_flip=True,
                         fill_mode="nearest")

figPath = os.path.sep.join([args["output"], "{}.png".format(os.getpid())])
jsonPath = os.path.sep.join([args["output"], "{}.json".format(os.getpid())])
callbacks = [TrainingMonitor(figPath, jsonPath=jsonPath),
             LearningRateScheduler(poly_decay)]

print("[INFO] compiling model...")
opt = SGD(lr=INIT_LR, momentum=0.9)
model = MiniGoogLeNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=opt,
              metrics=["accuracy"])

print("[INFO] training network...")
model.fit_generator(aug.flow(trainX, trainY, batch_size=64),
                    validation_data=(testX, testY), steps_per_epoch=len(trainX) // 64,
                    epochs=NUM_EPOCHS, callbacks=callbacks, verbose=1)
print("[INFO] serializing network...")
model.save(args["model"])

在terminal端输入 python googlenet_cifar10.py 指令的时候 出现'NoneType' object has no attribute 'compile'的bug
  • 写回答

5条回答 默认 最新

  • 这次真没糖 2022-08-31 22:12
    关注

    MiniGoogLeNet的代码有点问题,参考下面这个。

    
    # import the necessary packages
    from tensorflow.keras.layers import BatchNormalization
    from tensorflow.keras.layers import Conv2D
    from tensorflow.keras.layers import AveragePooling2D
    from tensorflow.keras.layers import MaxPooling2D
    from tensorflow.keras.layers import Activation
    from tensorflow.keras.layers import Dropout
    from tensorflow.keras.layers import Dense
    from tensorflow.keras.layers import Flatten
    from tensorflow.keras.layers import Input
    from tensorflow.keras.models import Model
    from tensorflow.keras.layers import concatenate
    from tensorflow.keras import backend as K
     
    class MiniGoogLeNet:
        @staticmethod
        def conv_module(x, K, kX, kY, stride, chanDim, padding="same"):
            # define a CONV => BN => RELU pattern
            x = Conv2D(K, (kX, kY), strides=stride, padding=padding)(x)
            x = BatchNormalization(axis=chanDim)(x)
            x = Activation("relu")(x)
            # return the block
            return x
     
        @staticmethod
        def inception_module(x, numK1x1, numK3x3, chanDim):
            # define two CONV modules, then concatenate across the
            # channel dimension
            conv_1x1 = MiniGoogLeNet.conv_module(x, numK1x1, 1, 1, (1, 1), chanDim)
            conv_3x3 = MiniGoogLeNet.conv_module(x, numK3x3, 3, 3, (1, 1), chanDim)
            x = concatenate([conv_1x1, conv_3x3], axis=chanDim)
            # return the block
            return x
     
        @staticmethod
        def downsample_module(x, K, chanDim):
            # define the CONV module and POOL, then concatenate
            # across the channel dimensions
            conv_3x3 = MiniGoogLeNet.conv_module(x, K, 3, 3, (2, 2), chanDim, padding="valid")
            pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
            x = concatenate([conv_3x3, pool], axis=chanDim)
            # return the block
            return x
     
        @staticmethod
        def build(width, height, depth, classes):
            # initialize the input shape to be "channels last" and the
            # channels dimension itself
            inputShape = (height, width, depth)
            chanDim = -1
     
            # if we are using "channels first", update the input shape
            # and channels dimension
            if K.image_data_format() == "channels_first":
                inputShape = (depth, height, width)
                chanDim = 1
            # define the model input and first CONV module
            inputs = Input(shape=inputShape)
            x = MiniGoogLeNet.conv_module(inputs, 96, 3, 3, (1, 1), chanDim)
            # two Inception modules followed by a downsample module
            x = MiniGoogLeNet.inception_module(x, 32, 32, chanDim)
            x = MiniGoogLeNet.inception_module(x, 32, 48, chanDim)
            x = MiniGoogLeNet.downsample_module(x, 80, chanDim)
     
            # four Inception modules followed by a downsample module
            x = MiniGoogLeNet.inception_module(x, 112, 48, chanDim)
            x = MiniGoogLeNet.inception_module(x, 96, 64, chanDim)
            x = MiniGoogLeNet.inception_module(x, 80, 80, chanDim)
            x = MiniGoogLeNet.inception_module(x, 48, 96, chanDim)
            x = MiniGoogLeNet.downsample_module(x, 96, chanDim)
     
            # two Inception modules followed by global POOL and dropout
            x = MiniGoogLeNet.inception_module(x, 176, 160, chanDim)
            x = MiniGoogLeNet.inception_module(x, 176, 160, chanDim)
            x = AveragePooling2D((7, 7))(x)
            x = Dropout(0.5)(x)
     
            # softmax classifier
            x = Flatten()(x)
            x = Dense(classes)(x)
            x = Activation("softmax")(x)
     
            # create the model
            model = Model(inputs, x, name="googlenet")
     
            # return the constructed network architecture
            return model
    
    本回答被题主选为最佳回答 , 对您是否有帮助呢?
    评论
查看更多回答(4条)

报告相同问题?

问题事件

  • 系统已结题 9月9日
  • 已采纳回答 9月1日
  • 赞助了问题酬金10元 8月31日
  • 创建了问题 8月31日

悬赏问题

  • ¥15 如何用Thoony写ESP32温湿度检测无源蜂鸣器报警代码?
  • ¥20 部件内部的CT图像数据集
  • ¥15 Visual studio调用动态库
  • ¥15 双目摄像头标定后的校准文件
  • ¥15 powerbi举证增加度量值后出现对应关系错乱
  • ¥30 频率分析法分析绘制奈奎斯特图、波特图
  • ¥15 弹出来一万个系统找不到指定的文件框框,怎么解决
  • ¥15 ADS生成的微带线为什么是蓝色空心的
  • ¥15 求一下解题思路,完全不懂
  • ¥15 tensorflow