3

我注意到一个问题，在评估（）期间，我没有看到基于 fit（）中的结果的预期结果。我在网上发现了许多讨论，其中人们有类似的问题。例如，这个未解决的问题讨论了 dropout 层和批标准化作为可能的原因，但也有些人注意到可能存在与 dropout 和批标准化分开的问题。对于初学者来说，甚至很难知道到底是什么问题。

我使用的网络架构确实包含批量标准化，但我不确定这是否是问题所在。

这个演示的数据可以在这里下载。

该脚本清楚地展示了我遇到的问题：

import random
import os
import matplotlib.image as mpimg
import cv2
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
HEIGHT_WIDTH = 299
BATCH_SIZE = 10
VERBOSE = 2

SANITY_SWITCH = False

print('starting script')

net = tf.keras.applications.InceptionResNetV2(
    include_top=True,
    weights=None,  # 'imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=2,  # 1000,
    classifier_activation='softmax'
)

print_output = True
def utility_metric(y_true, y_pred):
    global print_output
    if print_output:
        print(f'y_true:{y_true.numpy()}')
        print(f'y_pred:{y_pred.numpy()}')
        print_output = False
    return 0


net.compile(
    optimizer='ADAM',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy', utility_metric]
)

net.run_eagerly = True

class_map = {'dog': 0, 'cat': 1}

def preprocess(file):
    imdata = mpimg.imread(file)
    imdata = cv2.resize(imdata, dsize=(HEIGHT_WIDTH, HEIGHT_WIDTH), interpolation=cv2.INTER_LINEAR)
    imdata.shape = (HEIGHT_WIDTH, HEIGHT_WIDTH, 3)
    imdata /= 127.5
    imdata -= 1.
    return imdata, class_map[os.path.basename(os.path.dirname(file))]

train_data = [f'data/Training/cat/{x}' for x in os.listdir('data/Training/cat')] + [f'data/Training/dog/{x}' for x in os.listdir('data/Training/dog')]
test_data = [f'data/Testing/cat/{x}' for x in os.listdir('data/Testing/cat')] + [f'data/Testing/dog/{x}' for x in os.listdir('data/Testing/dog')]

random.shuffle(train_data)
random.shuffle(test_data)

if SANITY_SWITCH:
    tmp_data = train_data
    train_data = test_data
    test_data = tmp_data


def get_gen(data):
    def gen():
        pairs = []
        i = 0
        for im_file in data:
            i += 1
            if i <= BATCH_SIZE:
                pairs += [preprocess(im_file)]
            if i == BATCH_SIZE:
                yield (
                    [pair[0] for pair in pairs],
                    [pair[1] for pair in pairs]
                )
                pairs.clear()
                i = 0
    return gen

def get_ds(data):
    return tf.data.Dataset.from_generator(
        get_gen(data),
        (tf.float32, tf.int64),
        output_shapes=(
            tf.TensorShape((BATCH_SIZE, HEIGHT_WIDTH, HEIGHT_WIDTH, 3)),
            tf.TensorShape(([BATCH_SIZE]))
        )
    )
print('starting training')
net.fit(
    get_ds(train_data),
    epochs=5,
    verbose=VERBOSE,
    use_multiprocessing=True,
    workers=16,
    batch_size=BATCH_SIZE,
    shuffle=False
)
print('starting testing')
print_output = True
net.evaluate(
    get_ds(test_data),
    verbose=VERBOSE,
    batch_size=BATCH_SIZE,
    use_multiprocessing=True,
    workers=16,
)
print('script complete')

完整的输出在这里：

starting script
2020-12-22 15:29:33.896474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-12-22 15:29:34.184215: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:04:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.186083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 1 with properties: 
pciBusID: 0000:05:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.188086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 2 with properties: 
pciBusID: 0000:08:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.190088: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 3 with properties: 
pciBusID: 0000:09:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.192124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 4 with properties: 
pciBusID: 0000:84:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.194144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 5 with properties: 
pciBusID: 0000:85:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.196095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 6 with properties: 
pciBusID: 0000:88:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.197451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 7 with properties: 
pciBusID: 0000:89:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:34.208178: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-12-22 15:29:34.301110: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-12-22 15:29:34.348641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-12-22 15:29:34.370185: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-12-22 15:29:34.459524: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-12-22 15:29:34.471473: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-12-22 15:29:34.599447: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-12-22 15:29:34.634806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0, 1, 2, 3, 4, 5, 6, 7
2020-12-22 15:29:34.635371: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2020-12-22 15:29:34.680254: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000105000 Hz
2020-12-22 15:29:34.687348: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x561e331d4820 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-12-22 15:29:34.687415: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-12-22 15:29:35.617673: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:04:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.619368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 1 with properties: 
pciBusID: 0000:05:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.621161: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 2 with properties: 
pciBusID: 0000:08:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.622953: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 3 with properties: 
pciBusID: 0000:09:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.624745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 4 with properties: 
pciBusID: 0000:84:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.626508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 5 with properties: 
pciBusID: 0000:85:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.628264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 6 with properties: 
pciBusID: 0000:88:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.629460: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 7 with properties: 
pciBusID: 0000:89:00.0 name: Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2020-12-22 15:29:35.629581: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-12-22 15:29:35.629633: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-12-22 15:29:35.629685: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-12-22 15:29:35.629733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-12-22 15:29:35.629788: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-12-22 15:29:35.629837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-12-22 15:29:35.629886: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-12-22 15:29:35.657298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0, 1, 2, 3, 4, 5, 6, 7
2020-12-22 15:29:35.659638: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-12-22 15:29:35.678371: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-12-22 15:29:35.678447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 1 2 3 4 5 6 7 
2020-12-22 15:29:35.678500: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N Y Y Y N N N N 
2020-12-22 15:29:35.678538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 1:   Y N Y Y N N N N 
2020-12-22 15:29:35.678569: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 2:   Y Y N Y N N N N 
2020-12-22 15:29:35.678597: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 3:   Y Y Y N N N N N 
2020-12-22 15:29:35.678624: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 4:   N N N N N Y Y Y 
2020-12-22 15:29:35.678652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 5:   N N N N Y N Y Y 
2020-12-22 15:29:35.678678: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 6:   N N N N Y Y N Y 
2020-12-22 15:29:35.678705: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 7:   N N N N Y Y Y N 
2020-12-22 15:29:35.703703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10689 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:04:00.0, compute capability: 3.7)
2020-12-22 15:29:35.711407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 8534 MB memory) -> physical GPU (device: 1, name: Tesla K80, pci bus id: 0000:05:00.0, compute capability: 3.7)
2020-12-22 15:29:35.716593: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 10689 MB memory) -> physical GPU (device: 2, name: Tesla K80, pci bus id: 0000:08:00.0, compute capability: 3.7)
2020-12-22 15:29:35.721879: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 10689 MB memory) -> physical GPU (device: 3, name: Tesla K80, pci bus id: 0000:09:00.0, compute capability: 3.7)
2020-12-22 15:29:35.726952: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:4 with 10689 MB memory) -> physical GPU (device: 4, name: Tesla K80, pci bus id: 0000:84:00.0, compute capability: 3.7)
2020-12-22 15:29:35.732126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:5 with 10689 MB memory) -> physical GPU (device: 5, name: Tesla K80, pci bus id: 0000:85:00.0, compute capability: 3.7)
2020-12-22 15:29:35.736838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:6 with 10689 MB memory) -> physical GPU (device: 6, name: Tesla K80, pci bus id: 0000:88:00.0, compute capability: 3.7)
2020-12-22 15:29:35.740357: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:7 with 108 MB memory) -> physical GPU (device: 7, name: Tesla K80, pci bus id: 0000:89:00.0, compute capability: 3.7)
2020-12-22 15:29:35.746472: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x561e387dea00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-12-22 15:29:35.746517: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7
2020-12-22 15:29:35.746537: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): Tesla K80, Compute Capability 3.7
2020-12-22 15:29:35.746577: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (2): Tesla K80, Compute Capability 3.7
2020-12-22 15:29:35.746594: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (3): Tesla K80, Compute Capability 3.7
2020-12-22 15:29:35.746614: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (4): Tesla K80, Compute Capability 3.7
2020-12-22 15:29:35.746645: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (5): Tesla K80, Compute Capability 3.7
2020-12-22 15:29:35.746664: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (6): Tesla K80, Compute Capability 3.7
2020-12-22 15:29:35.746694: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (7): Tesla K80, Compute Capability 3.7
starting training
Epoch 1/5
2020-12-22 15:29:48.307104: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-12-22 15:29:51.694232: W tensorflow/stream_executor/gpu/asm_compiler.cc:81] Running ptxas --version returned 256
2020-12-22 15:29:51.796020: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] Internal: ptxas exited with non-zero error code 256, output: 
Relying on driver to perform ptx compilation. 
Modify $PATH to customize ptxas location.
This message will be only logged once.
2020-12-22 15:29:52.577156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
y_true:[[1.]
 [1.]
 [0.]
 [1.]
 [1.]
 [1.]
 [1.]
 [0.]
 [1.]
 [1.]]
y_pred:[[0.58956003 0.41043994]
 [0.63762885 0.36237112]
 [0.53731585 0.46268415]
 [0.5393683  0.4606317 ]
 [0.90735996 0.09264001]
 [0.552977   0.44702297]
 [0.7115651  0.28843486]
 [0.4068687  0.59313136]
 [0.5482196  0.4517804 ]
 [0.4330527  0.56694734]]
72/72 - 81s - loss: 0.9134 - accuracy: 0.5417 - utility_metric: 0.0000e+00
Epoch 2/5
72/72 - 81s - loss: 0.7027 - accuracy: 0.5847 - utility_metric: 0.0000e+00
Epoch 3/5
72/72 - 83s - loss: 0.6851 - accuracy: 0.5819 - utility_metric: 0.0000e+00
Epoch 4/5
72/72 - 83s - loss: 0.6810 - accuracy: 0.5944 - utility_metric: 0.0000e+00
Epoch 5/5
72/72 - 83s - loss: 0.6895 - accuracy: 0.5625 - utility_metric: 0.0000e+00
starting testing
y_true:[[1.]
 [1.]
 [0.]
 [0.]
 [0.]
 [1.]
 [1.]
 [0.]
 [0.]
 [1.]]
y_pred:[[0.39538118 0.6046188 ]
 [0.39505056 0.6049495 ]
 [0.39406297 0.605937  ]
 [0.3947329  0.60526717]
 [0.3935887  0.60641134]
 [0.39452523 0.60547477]
 [0.39451653 0.6054835 ]
 [0.39475334 0.60524666]
 [0.39559898 0.604401  ]
 [0.3951175  0.60488254]]
90/90 - 37s - loss: 0.7157 - accuracy: 0.5000 - utility_metric: 0.0000e+00
script complete

要关注的输出部分是准确性：

训练纪元 1：0.5417

训练纪元 2：0.5847

训练时期 3：0.5819

训练纪元 4：0.5944

训练纪元 5：0.5625

评价：0.5000

我还在两种情况下包含了网络的原始输出。训练时的一：

y_true:[[1.]
     [1.]
     [0.]
     [1.]
     [1.]
     [1.]
     [1.]
     [0.]
     [1.]
     [1.]]
y_pred:[[0.58956003 0.41043994]
     [0.63762885 0.36237112]
     [0.53731585 0.46268415]
     [0.5393683  0.4606317 ]
     [0.90735996 0.09264001]
     [0.552977   0.44702297]
     [0.7115651  0.28843486]
     [0.4068687  0.59313136]
     [0.5482196  0.4517804 ]
     [0.4330527  0.56694734]]

还有一个在测试期间：

y_true:[[1.]
     [1.]
     [0.]
     [0.]
     [0.]
     [1.]
     [1.]
     [0.]
     [0.]
     [1.]]
    y_pred:[[0.39538118 0.6046188 ]
     [0.39505056 0.6049495 ]
     [0.39406297 0.605937  ]
     [0.3947329  0.60526717]
     [0.3935887  0.60641134]
     [0.39452523 0.60547477]
     [0.39451653 0.6054835 ]
     [0.39475334 0.60524666]
     [0.39559898 0.604401  ]
     [0.3951175  0.60488254]]

我发现为什么在测试过程中，图像之间的输出变化似乎很小，我感到很困惑。这似乎与问题的根源有关，但我不知道是什么原因造成的。

我现在已经多次运行这个脚本，有些事情是一致的。评估期间的准确性总是完全偶然的。在评估期间 y_pred 的变化总是很小，并且所有输出似乎都是相同的标签（例如，在评估期间，模型可能会将每个输入图像报告为“狗”）。

有时在训练期间，准确率会超过 60%。这不影响问题。我可以继续增加数据集的大小和 epoch 的数量，并尝试改善训练结果，但我害怕在没有首先了解为什么评估结果像现在这样奇怪的情况下继续前进。

4

1 回答 1

0

我最近遇到了与MobileNetV3Large 模型非常相似的问题。

问题是在设置时weights=None，它会重置所有参数，包括评估期间使用的 BatchNormalization 指标。

不仅如此，正如一位朋友向我指出的那样，默认 BatchNormalization 的动量设置为 0.999，这意味着仅在评估期间使用的 BatchNormalization 参数（在训练期间使用批量均值/方差）移动非常非常缓慢。

如果您在几个时期内训练数百万步，那没关系。对于一个小数据集，这些参数没有显着变化，评估都被打破了。

如果你的问题和我的一样，一个快速的解决方法是将所有 BatchNormalization 层的动量设置为 0.9。这可以通过这个简单的递归函数来实现：

def SetBatchNormalizationMomentum(model, new_value, prefix='', verbose=False):
  for ii, layer in enumerate(model.layers):
    if hasattr(layer, 'layers'):
      SetBatchNormalizationMomentum(layer, new_value, f'{prefix}Layer {ii}/', verbose)
      continue
    elif isinstance(layer, tf.keras.layers.BatchNormalization):
      if verbose:
        print(f'{prefix}Layer {ii}: name={layer.name} momentum={layer.momentum} --> set momentum={new_value}')
      layer.momentum = new_value

我希望这对你也有帮助——它在这里工作。

（已编辑） .: 在此处设置 MobileNet 中的 BatchNorm 动量的代码。

于 2021-03-18T14:06:02.807 回答