- html - 出于某种原因,IE8 对我的 Sass 文件中继承的 html5 CSS 不友好?
- JMeter 在响应断言中使用 span 标签的问题
- html - 在 :hover and :active? 上具有不同效果的 CSS 动画
- html - 相对于居中的 html 内容固定的 CSS 重复背景?
我注意到一个问题,在 evaluation() 期间,我没有看到基于 fit() 中结果的预期结果。我在网上发现了很多讨论,其中人们有类似的问题。例如,this open issue 将 dropout layers 和 batch normalization 讨论为可能的原因,但也有人注意到可能存在与 dropout 和 batch normalization 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归一化,但我不确定这是否是问题所在。
这个demo的数据可以下载here .
这个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:
训练时期 1:0.5417
训练时期 2:0.5847
训练周期 3:0.5819
训练周期 4:0.5944
第 5 轮训练:0.5625
评价:0.5000
我还在两种情况下包含了网络的原始输出。训练期间的一个:
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]]
我觉得很困惑,为什么在测试期间,图像之间的输出差异似乎很小。这似乎与问题的根源有关,但我不知道是什么原因导致的。
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 始终存在低变化,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理解为什么评估结果如此奇怪的情况下继续前进。
最佳答案
我最近遇到了与 MobileNetV3Large model 非常相似的问题.
问题是在设置 weights=None
时,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 friend 向我指出的那样,默认的 BatchNormalization 动量设置为 0.999,这意味着仅在评估期间使用的 BatchNormalization 参数(在训练期间它使用批量均值/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,那也没关系。对于一个小数据集,这些参数没有显着变化,评价都被打破了。
如果您的问题和我的一样,快速解决方法是将所有 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
我希望这对您也有帮助 - 它在这里起作用。
(已编辑).: 在 MobileNet 中设置 BatchNorm 动量的代码 here .
关于tensorflow - fit() 按预期工作,但随后在 evaluate() 模型中随机执行,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/65415799/
我需要将文本放在 中在一个 Div 中,在另一个 Div 中,在另一个 Div 中。所以这是它的样子: #document Change PIN
奇怪的事情发生了。 我有一个基本的 html 代码。 html,头部, body 。(因为我收到了一些反对票,这里是完整的代码) 这是我的CSS: html { backgroun
我正在尝试将 Assets 中的一组图像加载到 UICollectionview 中存在的 ImageView 中,但每当我运行应用程序时它都会显示错误。而且也没有显示图像。 我在ViewDidLoa
我需要根据带参数的 perl 脚本的输出更改一些环境变量。在 tcsh 中,我可以使用别名命令来评估 perl 脚本的输出。 tcsh: alias setsdk 'eval `/localhome/
我使用 Windows 身份验证创建了一个新的 Blazor(服务器端)应用程序,并使用 IIS Express 运行它。它将显示一条消息“Hello Domain\User!”来自右上方的以下 Ra
这是我的方法 void login(Event event);我想知道 Kotlin 中应该如何 最佳答案 在 Kotlin 中通配符运算符是 * 。它指示编译器它是未知的,但一旦知道,就不会有其他类
看下面的代码 for story in book if story.title.length < 140 - var story
我正在尝试用 C 语言学习字符串处理。我写了一个程序,它存储了一些音乐轨道,并帮助用户检查他/她想到的歌曲是否存在于存储的轨道中。这是通过要求用户输入一串字符来完成的。然后程序使用 strstr()
我正在学习 sscanf 并遇到如下格式字符串: sscanf("%[^:]:%[^*=]%*[*=]%n",a,b,&c); 我理解 %[^:] 部分意味着扫描直到遇到 ':' 并将其分配给 a。:
def char_check(x,y): if (str(x) in y or x.find(y) > -1) or (str(y) in x or y.find(x) > -1):
我有一种情况,我想将文本文件中的现有行包含到一个新 block 中。 line 1 line 2 line in block line 3 line 4 应该变成 line 1 line 2 line
我有一个新项目,我正在尝试设置 Django 调试工具栏。首先,我尝试了快速设置,它只涉及将 'debug_toolbar' 添加到我的已安装应用程序列表中。有了这个,当我转到我的根 URL 时,调试
在 Matlab 中,如果我有一个函数 f,例如签名是 f(a,b,c),我可以创建一个只有一个变量 b 的函数,它将使用固定的 a=a1 和 c=c1 调用 f: g = @(b) f(a1, b,
我不明白为什么 ForEach 中的元素之间有多余的垂直间距在 VStack 里面在 ScrollView 里面使用 GeometryReader 时渲染自定义水平分隔线。 Scrol
我想知道,是否有关于何时使用 session 和 cookie 的指南或最佳实践? 什么应该和什么不应该存储在其中?谢谢! 最佳答案 这些文档很好地了解了 session cookie 的安全问题以及
我在 scipy/numpy 中有一个 Nx3 矩阵,我想用它制作一个 3 维条形图,其中 X 轴和 Y 轴由矩阵的第一列和第二列的值、高度确定每个条形的 是矩阵中的第三列,条形的数量由 N 确定。
假设我用两种不同的方式初始化信号量 sem_init(&randomsem,0,1) sem_init(&randomsem,0,0) 现在, sem_wait(&randomsem) 在这两种情况下
我怀疑该值如何存储在“WORD”中,因为 PStr 包含实际输出。? 既然Pstr中存储的是小写到大写的字母,那么在printf中如何将其给出为“WORD”。有人可以吗?解释一下? #include
我有一个 3x3 数组: var my_array = [[0,1,2], [3,4,5], [6,7,8]]; 并想获得它的第一个 2
我意识到您可以使用如下方式轻松检查焦点: var hasFocus = true; $(window).blur(function(){ hasFocus = false; }); $(win
我是一名优秀的程序员,十分优秀!