尽管对 keras 中的零填充小批量 LSTM 训练提供了掩码支持，但预测仍为零
Zero predictions despite masking support for zero-padded mini batch LSTM training in keras
问题陈述
我正在使用带标签的文本序列（tf 版本 1.13.1）在 keras 中训练多对多 LSTM，以使用预训练的 GloVe 嵌入预测序列中每个元素的标签。我的训练制度涉及小批量随机梯度下降，每个小批量矩阵按列填充零以确保输入到网络的长度相等。
至关重要的是，由于任务和数据的性质对我的小批量有自定义限制，我没有使用 keras 嵌入层。我的目标是为我的零填充单元格实施屏蔽机制，以确保损失计算不会将这些单元格误认为是真正的数据点。
接近
正如 keras documentation 中所解释的，keras 有三种设置掩蔽层的方法：
- 使用
mask_zero 配置 keras.layers.Embedding 图层
设置为 True.
- 添加
keras.layers.Masking 层；
- 调用循环层时手动传递掩码参数。
因为我没有使用嵌入层来编码我的训练数据，所以我无法使用带有屏蔽嵌入层的选项 (1)。因此，我选择了 (2) 并在初始化我的模型后立即添加了一个遮罩层。然而，这种变化似乎并没有产生影响。事实上，不仅我的模型的准确性没有提高，在预测阶段模型仍然产生零预测。为什么我的屏蔽层不屏蔽零填充单元格？这可能与我在密集层中指定 3 个 class 而不是 2 个（因此包括 0 作为单独的 class）有关吗？
现有资源的限制
已经提出并回答了类似的问题，但我无法使用它们来解决我的问题。而 this post received no direct response, a post mentioned in a comment focuses on how to preprocess data to assign mask value, which is uncontroversial here. The masking layer initializtion, however, is identical to the one used here. post mentions the same problem - a masking layer has no effect on performance - and the answer defines the masking layer in the same way as I do, but again focuses on converting specific values to mask values. Finally, the answer in post 提供了相同的层初始化，不再赘述。
玩具数据生成
为了重现我的问题，我生成了一个包含两个 classes (1,2) 的玩具 10 批次数据集。批处理是一个可变长度序列 post-用零填充到最大长度为 20 个嵌入，每个嵌入向量由 5 个单元组成，因此 input_shape=(20,5)。两个 classes 的嵌入值是从不同但部分重叠的截断正态分布生成的，从而为网络创建了一个可学习但并非微不足道的问题。我在下面包含了玩具数据，因此您可以重现该问题。
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Dropout, Masking
from keras import optimizers
# *** model initialization ***
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(20, 5))) # <- masking layer here
model.add(Bidirectional(LSTM(20, return_sequences=True), input_shape=(20, 5)))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(3, activation='sigmoid')))
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd, metrics=['mse'])
# *** model training ***
for epoch in range(10):
for X,y in data_train:
X = X.reshape(1, 20, 5)
y = y.reshape(1, 20, 1)
history = model.fit(X, y, epochs=1, batch_size=20, verbose=0)
# *** model prediction ***
preds = pd.DataFrame(columns=['true', 'pred'])
for index, (X,y) in enumerate(data_test):
X = X.reshape(1, 20, 5)
y = y.reshape(1, 20, 1)
y_pred = model.predict_classes(X, verbose=0)
df = pd.DataFrame(columns=['true', 'pred'])
df['true'] = [y[0, i][0] for i in range(20)]
df['pred'] = [y_pred[0, i] for i in range(20)]
preds = preds.append(df, ignore_index=True)
# convert true labels to int & drop padded rows (where y_true=0)
preds['true'] = [int(label) for label in preds['true']]
preds = preds[preds['true']!=0]
这是带掩蔽的模型的摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking_2 (Masking) (None, 20, 5) 0
_________________________________________________________________
bidirectional_4 (Bidirection (None, 20, 40) 4160
_________________________________________________________________
dropout_4 (Dropout) (None, 20, 40) 0
_________________________________________________________________
time_distributed_4 (TimeDist (None, 20, 3) 123
=================================================================
Total params: 4,283
Trainable params: 4,283
Non-trainable params: 0
我训练了一个有和没有掩蔽层的模型，并使用以下方法计算准确率：
np.round(sum(preds['true']==preds['pred'])/len(preds)*100,1)
没有掩蔽的模型的准确率为 53.3%，有掩蔽的模型的准确率为 33.3%。更令人惊讶的是，在这两个模型中，我一直将零作为预测标签。为什么掩蔽层无法忽略零填充单元格？
重现问题的数据:
data_train = list(zip(X_batches_train, y_batches_train))
data_test = list(zip(X_batches_test, y_batches_test))
X_batches_train
[array([[-1.00612917, 1.47313952, 2.68021318, 1.54875809, 0.98385996,
1.49465265, 0.60429106, 1.12396908, -0.24041602, 1.77266187,
0.1961381 , 1.28019637, 1.78803092, 2.05151245, 0.93606708,
0.51554755, 0. , 0. , 0. , 0. ],
[-0.97596563, 2.04536053, 0.88367922, 1.013342 , -0.16605355,
3.02994344, 2.04080806, -0.25153046, -0.5964068 , 2.9607247 ,
-0.49722121, 0.02734492, 2.16949987, 2.77367066, 0.15628842,
2.19823207, 0. , 0. , 0. , 0. ],
[ 0.31546283, 3.27420503, 3.23550769, -0.63724013, 0.89150128,
0.69774266, 2.76627308, -0.58408384, -0.45681779, 1.98843041,
-0.31850477, 0.83729882, 0.45471165, 3.61974147, -1.45610756,
1.35217453, 0. , 0. , 0. , 0. ],
[ 1.03329532, 1.97471646, 1.33949611, 1.22857243, -1.46890642,
1.74105506, 1.40969261, 0.52465603, -0.18895266, 2.81025597,
2.64901037, -0.83415186, 0.76956826, 1.48730868, -0.16190164,
2.24389007, 0. , 0. , 0. , 0. ],
[-1.0676654 , 3.08429323, 1.7601179 , 0.85448051, 1.15537064,
2.82487842, 0.27891413, 0.57842569, -0.62392063, 1.00343057,
1.15348843, -0.37650332, 3.37355345, 2.22285473, 0.43444434,
0.15743873, 0. , 0. , 0. , 0. ]]),
array([[ 1.05258873, -0.17897376, -0.99932932, -1.02854121, 0.85159208,
2.32349131, 1.96526709, -0.08398597, -0.69474809, 1.32820222,
1.19514151, 1.56814867, 0.86013263, 1.48342922, 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.1920635 , -0.48702788, 1.24353985, -1.3864121 , 0.16713229,
3.10134683, 0.61658271, -0.63360643, 0.86000807, 2.74876157,
2.87604877, 0.16339724, 2.87595396, 3.2846962 , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.1380241 , -0.76783029, 0.18814436, -1.18165209, -0.02981728,
1.49908113, 0.61521007, -0.98191097, 0.31250199, 1.39015803,
3.16213211, -0.70891214, 3.83881766, 1.92683533, 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.39080778, -0.59179216, 0.80348201, 0.64638205, -1.40144268,
1.49751413, 3.0092166 , 1.33099666, 1.43714841, 2.90734268,
3.09688943, 0.32934884, 1.14592787, 1.58152023, 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.77164353, 0.50293096, 0.0717377 , 0.14487556, -0.90246591,
2.32612179, 1.98628857, 1.29683166, -0.12399569, 2.60184685,
3.20136653, 0.44056647, 0.98283455, 1.79026663, 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[-0.93359914, 2.31840281, 0.55691601, 1.90930758, -1.58260431,
-1.05801881, 3.28012523, 3.84105406, -1.2127093 , 0.00490079,
1.28149304, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-1.03105486, 2.7703693 , 0.16751813, 1.12127987, -0.44070271,
-0.0789227 , 2.79008301, 1.11456745, 1.13982551, -1.10128658,
0.87430834, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.69710668, 1.72702833, -2.62599502, 2.34730002, 0.77756661,
0.16415884, 3.30712178, 1.67331828, -0.44022431, 0.56837829,
1.1566811 , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.71845983, 1.79908544, 0.37385522, 1.3870915 , -1.48823234,
-1.487419 , 3.0879945 , 1.74617784, -0.91538815, -0.24244522,
0.81393954, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-1.38501563, 3.73330047, -0.52494265, 2.37133716, -0.24546709,
-0.28360782, 2.89384717, 2.42891743, 0.40144022, -1.21850571,
2.00370751, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[ 1.27989188, 1.16254538, -0.06889142, 1.84133355, 1.3234908 ,
1.29611702, 2.0019294 , -0.03220116, 1.1085194 , 1.96495985,
1.68544302, 1.94503544, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.3004439 , 2.48768923, 0.59809607, 2.38155155, 2.78705889,
1.67018683, 0.21731778, -0.59277191, 2.87427207, 2.63950475,
2.39211459, 0.93083423, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.39239371, 0.30900383, -0.97307155, 1.98100711, 0.30613735,
1.12827171, 0.16987791, 0.31959096, 1.30366416, 1.45881023,
2.45668401, 0.5218711 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.0826574 , 2.05100254, 0.013161 , 2.95120798, 1.15730011,
0.75537024, 0.13708569, -0.44922143, 0.64834001, 2.50640862,
2.00349347, 3.35573624, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.47135124, 2.10258532, 0.70212032, 2.56063126, 1.62466971,
2.64026892, 0.21309489, -0.57752813, 2.21335957, 0.20453233,
0.03106993, 3.01167822, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[-0.42125521, 0.54016939, 1.63016057, 2.01555253, -0.10961255,
-0.42549555, 1.55793753, -0.0998756 , 0.36417335, 3.37126414,
1.62151191, 2.84084192, 0.10831384, 0.89293054, -0.08671363,
0.49340353, 0. , 0. , 0. , 0. ],
[-0.37615411, 2.00581062, 2.30426605, 2.02205839, 0.65871664,
1.34478836, -0.55379752, -1.42787727, 0.59732227, 0.84969282,
0.54345723, 0.95849568, -0.17131602, -0.70425277, -0.5337757 ,
1.78207229, 0. , 0. , 0. , 0. ],
[-0.13863276, 1.71490034, 2.02677925, 2.60608619, 0.26916522,
0.35928298, -1.26521844, -0.59859219, 1.19162219, 1.64565259,
1.16787165, 2.95245196, 0.48681084, 1.66621053, 0.918077 ,
-1.10583747, 0. , 0. , 0. , 0. ],
[ 0.87763797, 2.38740754, 2.9111822 , 2.21184069, 0.78091173,
-0.53270909, 0.40100338, -0.83375593, 0.9860009 , 2.43898437,
-0.64499989, 2.95092003, -1.52360727, 0.44640918, 0.78131922,
-0.24401283, 0. , 0. , 0. , 0. ],
[ 0.92615066, 3.45437746, 3.28808981, 2.87207404, -1.60027223,
-1.14164941, -1.63807699, 0.33084805, 2.92963629, 3.51170824,
-0.3286093 , 2.19108385, 0.97812366, -1.82565766, -0.34034678,
-2.0485913 , 0. , 0. , 0. , 0. ]]),
array([[ 1.96438618e+00, 1.88104784e-01, 1.61114494e+00,
6.99567690e-04, 2.55271963e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 2.41578815e+00, -5.70625661e-01, 2.15545894e+00,
-1.80948908e+00, 1.62049331e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 1.97017040e+00, -1.62556528e+00, 2.49469152e+00,
4.18785985e-02, 2.61875866e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 3.14277819e+00, 3.01098398e-02, 7.40376369e-01,
1.76517344e+00, 2.68922918e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 2.06250296e+00, 4.67605528e-01, 1.55927230e+00,
1.85788889e-01, 1.30359922e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00]]),
array([[ 1.22152427, 3.74926839, 0.64415552, 2.35268329, 1.98754653,
2.89384829, 0.44589817, 3.94228743, 2.72405657, 0.86222004,
0.68681903, 3.89952458, 1.43454512, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.02203262, 0.95065123, 0.71669023, 0.02919391, 2.30714524,
1.91843002, 0.73611294, 1.20560482, 0.85206836, -0.74221506,
-0.72886308, 2.39872927, -0.95841402, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.55775319, 0.33773314, 0.79932151, 1.94966883, 3.2113281 ,
2.70768249, -0.69745554, 1.23208345, 1.66199957, 1.69894081,
0.13124461, 1.93256147, -0.17787952, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.45089205, 2.62430534, -1.9517961 , 2.24040577, 1.75642049,
1.94962325, 0.26796497, 2.28418304, 1.44944487, 0.28723885,
-0.81081633, 1.54840214, 0.82652939, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.27678173, 1.17204606, -0.24738322, 1.02761617, 1.81060444,
2.37830861, 0.55260134, 2.50046334, 1.04652821, 0.03467176,
-2.07336654, 1.2628897 , 0.61604732, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[ 3.86138405, 2.35068317, -1.90187438, 0.600788 , 0.18011722,
1.3469559 , -0.54708828, 1.83798823, -0.01957845, 2.88713217,
3.1724991 , 2.90802072, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.26785642, 0.51076756, 0.32070756, 2.33758816, 2.08146669,
-0.60796736, 0.93777509, 2.70474711, 0.44785738, 1.61720609,
1.52890594, 3.03072971, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 3.30219394, 3.1515445 , 1.16550716, 2.07489374, 0.66441859,
0.97529244, 0.35176367, 1.22593639, -1.80698271, 1.19936482,
3.34017172, 2.15960657, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.34839018, 2.24827352, -1.61070856, 2.81044265, -1.21423372,
0.24633846, -0.82196609, 2.28616568, 0.033922 , 2.7557593 ,
1.16178372, 3.66959512, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.32913219, 1.63231852, 0.58642744, 1.55873546, 0.86354741,
2.06654246, -0.44036504, 3.22723595, 1.33279468, 0.05975892,
2.48518999, 3.44690602, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[ 0.61424344, -1.03068819, -1.47929328, 2.91514641, 2.06867196,
1.90384921, -0.45835234, 1.22054782, 0.67931536, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.76480464, 1.12442631, -2.36004758, 2.91912726, 1.67891181,
3.76873596, -0.93874096, -0.32397781, -0.55732374, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.39953353, -1.26828104, 0.44482517, 2.85604975, 3.08891062,
2.60268725, -0.15785176, 1.58549879, -0.32948578, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.65156484, -1.56545168, -1.42771206, 2.74216475, 1.8758154 ,
3.51169147, 0.18353058, -0.14704149, 0.00442783, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.27736372, 0.37407608, -1.25713475, 0.53171176, 1.53714914,
0.21015523, -1.06850669, -0.09755327, -0.92373834, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[-1.39160433, 0.21014669, -0.89792475, 2.6702794 , 1.54610601,
0.84699037, 2.96726482, 1.84236946, 0.02211578, 0.32842575,
1.02718924, 1.78447936, -1.20056829, 2.26699318, -0.23156537,
2.50124959, 1.93372501, 0.10264369, -1.70813962, 0. ],
[ 0.38823591, -1.30348049, -0.31599117, 2.60044143, 2.32929389,
1.40348483, 3.25758736, 1.92210728, -0.34150988, -1.22336921,
2.3567069 , 1.75456835, 0.28295694, 0.68114898, -0.457843 ,
1.83372069, 2.10177851, -0.26664178, -0.26549595, 0. ],
[ 0.08540346, 0.71507504, 1.78164285, 3.04418137, 1.52975256,
3.55159169, 3.21396003, 3.22720346, 0.68147142, 0.12466013,
-0.4122895 , 1.97986653, 1.51671949, 2.06096825, -0.6765908 ,
2.00145086, 1.73723014, 0.50186043, -2.27525744, 0. ],
[ 0.00632717, 0.3050794 , -0.33167875, 1.48109172, 0.19653696,
1.97504239, 2.51595821, 1.74499313, -1.65198805, -1.04424953,
-0.23786945, 1.18639347, -0.03568057, 3.82541131, 2.84039446,
2.88325909, 1.79827675, -0.80230291, 0.08165052, 0. ],
[ 0.89980086, 0.34690991, -0.60806566, 1.69472308, 1.38043417,
0.97139487, 0.21977176, 1.01340944, -1.69946943, -0.01775586,
-0.35851919, 1.81115864, 1.15105661, 1.21410373, 1.50667558,
1.70155313, 3.1410754 , -0.54806167, -0.51879299, 0. ]])]
y_batches_train
[array([1., 2., 2., 1., 1., 2., 2., 1., 1., 2., 1., 1., 2., 2., 1., 2., 0.,
0., 0., 0.]),
array([1., 1., 1., 1., 1., 2., 2., 1., 1., 2., 2., 1., 2., 2., 0., 0., 0.,
0., 0., 0.]),
array([1., 2., 1., 2., 1., 1., 2., 2., 1., 1., 2., 0., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([2., 2., 1., 2., 2., 2., 1., 1., 2., 2., 2., 2., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([1., 2., 2., 2., 1., 1., 1., 1., 2., 2., 1., 2., 1., 1., 1., 1., 0.,
0., 0., 0.]),
array([2., 1., 2., 1., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([1., 2., 1., 2., 2., 2., 1., 2., 2., 1., 1., 2., 1., 0., 0., 0., 0.,
0., 0., 0.]),
array([2., 2., 1., 2., 1., 1., 1., 2., 1., 2., 2., 2., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([2., 1., 1., 2., 2., 2., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([1., 1., 1., 2., 2., 2., 2., 2., 1., 1., 1., 2., 1., 2., 1., 2., 2.,
1., 1., 0.])]
X_batches_test
[array([[ 0.74119496, 1.97273418, 1.76675805, 0.51484268, 1.39422086,
2.97184667, -1.35274514, 2.08825434, -1.2521965 , 1.11556387,
0.19776789, 2.38259223, -0.57140597, -0.79010112, 0.17038974,
1.28075761, 0.696398 , 3.0920007 , -0.41138503, 0. ],
[-1.39081797, 0.41079718, 3.03698894, -2.07333633, 2.05575621,
2.73222939, -0.98182787, 1.06741172, -1.36310914, 0.20174856,
0.35323654, 2.70305775, 0.52549713, -0.7786237 , 1.80857093,
0.96830907, -0.23610863, 1.28160768, 0.7026651 , 0. ],
[ 1.16357113, 0.43907935, 3.40158623, -0.73923043, 1.484668 ,
1.52809569, -0.02347205, 1.65349967, 1.79635118, -0.46647772,
-0.78400883, 0.82695404, -1.34932627, -0.3200281 , 2.84417045,
0.01534261, 0.10047148, 2.70769609, -1.42669461, 0. ],
[-1.05475682, 3.45578027, 1.58589338, -0.55515227, 2.13477478,
1.86777473, 0.61550335, 1.05781415, -0.45297406, -0.04317595,
-0.15255388, 0.74669395, -1.43621979, 1.06229278, 0.99792794,
1.24391783, -1.86484584, 1.92802343, 0.56148011, 0. ],
[-0.0835337 , 1.89593955, 1.65769335, -0.93622246, 1.05002869,
1.49675624, -0.00821712, 1.71541053, 2.02408452, 0.59011484,
0.72719784, 3.44801858, -0.00957537, 0.37176007, 1.93481168,
2.23125062, 1.67910471, 2.80923862, 0.34516993, 0. ]]),
array([[ 0.40691415, 2.31873444, -0.83458005, -0.17018249, -0.39177831,
1.90353251, 2.98241467, 0.32808584, 3.09429553, 2.27183083,
3.09576659, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.6862473 , 1.0690102 , -0.07415598, -0.09846767, 1.14562424,
2.52211963, 1.71911351, 0.41879894, 1.62787544, 3.50533394,
2.69963456, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 3.27824216, 2.25067953, 0.40017321, -1.36011162, -1.41010106,
0.98956203, 2.30881584, -0.29496046, 2.29748247, 3.24940966,
1.06431776, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.80167214, 3.88324559, -0.6984172 , 0.81889567, 1.86945352,
3.07554419, 3.10357189, 1.31426767, 0.28163147, 2.75559628,
2.00866885, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.54574419, 1.00720596, -1.55418837, 0.70823839, 0.14715209,
1.03747262, 0.82988672, -0.54006372, 1.4960777 , 0.34578788,
1.10558132, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]])]
y_batches_test
[array([1., 2., 2., 1., 2., 2., 1., 2., 1., 1., 1., 2., 1., 1., 2., 2., 1.,
2., 1., 0.]),
array([2., 2., 1., 1., 1., 2., 2., 1., 2., 2., 2., 0., 0., 0., 0., 0., 0.,
0., 0., 0.])]
第一个问题：你reshaping后的X数据不是你期望的。再看reshape后的第一个sample是：
array([[[-1.00612917, 1.47313952, 2.68021318, 1.54875809,
0.98385996],
[ 1.49465265, 0.60429106, 1.12396908, -0.24041602,
1.77266187],
[ 0.1961381 , 1.28019637, 1.78803092, 2.05151245,
0.93606708],
[ 0.51554755, 0. , 0. , 0. ,
0. ],
[-0.97596563, 2.04536053, 0.88367922, 1.013342 ,
-0.16605355],
[ 3.02994344, 2.04080806, -0.25153046, -0.5964068 ,
2.9607247 ],
[-0.49722121, 0.02734492, 2.16949987, 2.77367066,
0.15628842],
[ 2.19823207, 0. , 0. , 0. ,
0. ],
[ 0.31546283, 3.27420503, 3.23550769, -0.63724013,
0.89150128],
[ 0.69774266, 2.76627308, -0.58408384, -0.45681779,
1.98843041],
[-0.31850477, 0.83729882, 0.45471165, 3.61974147,
-1.45610756],
[ 1.35217453, 0. , 0. , 0. ,
0. ],
[ 1.03329532, 1.97471646, 1.33949611, 1.22857243,
-1.46890642],
[ 1.74105506, 1.40969261, 0.52465603, -0.18895266,
2.81025597],
[ 2.64901037, -0.83415186, 0.76956826, 1.48730868,
-0.16190164],
[ 2.24389007, 0. , 0. , 0. ,
0. ],
[-1.0676654 , 3.08429323, 1.7601179 , 0.85448051,
1.15537064],
[ 2.82487842, 0.27891413, 0.57842569, -0.62392063,
1.00343057],
[ 1.15348843, -0.37650332, 3.37355345, 2.22285473,
0.43444434],
[ 0.15743873, 0. , 0. , 0. ,
0. ]]])
所以实际上没有时间步被屏蔽，因为Masking层只屏蔽所有特征为0的时间步，所以上面20个时间步没有被屏蔽，因为其中none个完全为0。
对于Masking层，为了确保将mask成功传播到输出层，您可以这样做：
for i, l in enumerate(model.layers):
print(f'layer {i}: {l}')
print(f'has input mask: {l.input_mask}')
print(f'has output mask: {l.output_mask}')
layer 0: <tensorflow.python.keras.layers.core.Masking object at 0x6417b7f60>
has input mask: None
has output mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
layer 1: <tensorflow.python.keras.layers.wrappers.Bidirectional object at 0x641e25cf8>
has input mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
has output mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
layer 2: <tensorflow.python.keras.layers.core.Dropout object at 0x641814128>
has input mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
has output mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
layer 3: <tensorflow.python.keras.layers.wrappers.TimeDistributed object at 0x6433b6ba8>
has input mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
has output mask: Tensor("time_distributed/Reshape_3:0", shape=(None, 20), dtype=bool)
所以你可以看到最后一层也有output_mask，这意味着掩码传播成功。您似乎对Masking在Keras中的工作方式有误解，它实际上是会生成一个掩码，这是一个布尔数组，掩码的形状是（None，Timesteps），因为在您的模型中定义中，Timestep 维度始终保持不变，因此掩码将传播到末尾而不会发生任何变化。然后，当 Keras 计算损失时（当然还有计算梯度时），掩码值为 False 的时间步长将被忽略。 Masking 层不会改变输出值，当然你的模型仍然会预测 class 0，它所做的只是生成一个布尔数组，指示应该跳过哪个时间步并将它传递到最后（如果所有图层接受蒙版）。
所以你可以做的是如下更改模型定义的一行，并使你的 y_labels 移动 1，这意味着你当前的 classes:
0 -> 0（因为这些时间步的损失会被忽略，对模型的训练没有贡献，所以是0还是1无所谓）
1 -> 0
2 -> 1
# I would prefer softmax if doing classification
# here we only need to specify 2 classes
# and actually TimeDistributed can be thrown away (at least in recent Keras versions)
model.add(TimeDistributed(Dense(2, activation='softmax')))
您也可以在此处查看我的回答，了解如何使用/不使用掩码计算损失。
问题陈述
我正在使用带标签的文本序列（tf 版本 1.13.1）在 keras 中训练多对多 LSTM，以使用预训练的 GloVe 嵌入预测序列中每个元素的标签。我的训练制度涉及小批量随机梯度下降，每个小批量矩阵按列填充零以确保输入到网络的长度相等。
至关重要的是，由于任务和数据的性质对我的小批量有自定义限制，我没有使用 keras 嵌入层。我的目标是为我的零填充单元格实施屏蔽机制，以确保损失计算不会将这些单元格误认为是真正的数据点。
接近
正如 keras documentation 中所解释的，keras 有三种设置掩蔽层的方法：
- 使用
mask_zero配置keras.layers.Embedding图层 设置为True. - 添加
keras.layers.Masking层； - 调用循环层时手动传递掩码参数。
因为我没有使用嵌入层来编码我的训练数据，所以我无法使用带有屏蔽嵌入层的选项 (1)。因此，我选择了 (2) 并在初始化我的模型后立即添加了一个遮罩层。然而，这种变化似乎并没有产生影响。事实上，不仅我的模型的准确性没有提高，在预测阶段模型仍然产生零预测。为什么我的屏蔽层不屏蔽零填充单元格？这可能与我在密集层中指定 3 个 class 而不是 2 个（因此包括 0 作为单独的 class）有关吗？
现有资源的限制
已经提出并回答了类似的问题，但我无法使用它们来解决我的问题。而 this post received no direct response, a
玩具数据生成
为了重现我的问题，我生成了一个包含两个 classes (1,2) 的玩具 10 批次数据集。批处理是一个可变长度序列 post-用零填充到最大长度为 20 个嵌入，每个嵌入向量由 5 个单元组成，因此 input_shape=(20,5)。两个 classes 的嵌入值是从不同但部分重叠的截断正态分布生成的，从而为网络创建了一个可学习但并非微不足道的问题。我在下面包含了玩具数据，因此您可以重现该问题。
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Dropout, Masking
from keras import optimizers
# *** model initialization ***
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(20, 5))) # <- masking layer here
model.add(Bidirectional(LSTM(20, return_sequences=True), input_shape=(20, 5)))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(3, activation='sigmoid')))
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd, metrics=['mse'])
# *** model training ***
for epoch in range(10):
for X,y in data_train:
X = X.reshape(1, 20, 5)
y = y.reshape(1, 20, 1)
history = model.fit(X, y, epochs=1, batch_size=20, verbose=0)
# *** model prediction ***
preds = pd.DataFrame(columns=['true', 'pred'])
for index, (X,y) in enumerate(data_test):
X = X.reshape(1, 20, 5)
y = y.reshape(1, 20, 1)
y_pred = model.predict_classes(X, verbose=0)
df = pd.DataFrame(columns=['true', 'pred'])
df['true'] = [y[0, i][0] for i in range(20)]
df['pred'] = [y_pred[0, i] for i in range(20)]
preds = preds.append(df, ignore_index=True)
# convert true labels to int & drop padded rows (where y_true=0)
preds['true'] = [int(label) for label in preds['true']]
preds = preds[preds['true']!=0]
这是带掩蔽的模型的摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking_2 (Masking) (None, 20, 5) 0
_________________________________________________________________
bidirectional_4 (Bidirection (None, 20, 40) 4160
_________________________________________________________________
dropout_4 (Dropout) (None, 20, 40) 0
_________________________________________________________________
time_distributed_4 (TimeDist (None, 20, 3) 123
=================================================================
Total params: 4,283
Trainable params: 4,283
Non-trainable params: 0
我训练了一个有和没有掩蔽层的模型，并使用以下方法计算准确率：
np.round(sum(preds['true']==preds['pred'])/len(preds)*100,1)
没有掩蔽的模型的准确率为 53.3%，有掩蔽的模型的准确率为 33.3%。更令人惊讶的是，在这两个模型中，我一直将零作为预测标签。为什么掩蔽层无法忽略零填充单元格？
重现问题的数据:
data_train = list(zip(X_batches_train, y_batches_train))
data_test = list(zip(X_batches_test, y_batches_test))
X_batches_train
[array([[-1.00612917, 1.47313952, 2.68021318, 1.54875809, 0.98385996,
1.49465265, 0.60429106, 1.12396908, -0.24041602, 1.77266187,
0.1961381 , 1.28019637, 1.78803092, 2.05151245, 0.93606708,
0.51554755, 0. , 0. , 0. , 0. ],
[-0.97596563, 2.04536053, 0.88367922, 1.013342 , -0.16605355,
3.02994344, 2.04080806, -0.25153046, -0.5964068 , 2.9607247 ,
-0.49722121, 0.02734492, 2.16949987, 2.77367066, 0.15628842,
2.19823207, 0. , 0. , 0. , 0. ],
[ 0.31546283, 3.27420503, 3.23550769, -0.63724013, 0.89150128,
0.69774266, 2.76627308, -0.58408384, -0.45681779, 1.98843041,
-0.31850477, 0.83729882, 0.45471165, 3.61974147, -1.45610756,
1.35217453, 0. , 0. , 0. , 0. ],
[ 1.03329532, 1.97471646, 1.33949611, 1.22857243, -1.46890642,
1.74105506, 1.40969261, 0.52465603, -0.18895266, 2.81025597,
2.64901037, -0.83415186, 0.76956826, 1.48730868, -0.16190164,
2.24389007, 0. , 0. , 0. , 0. ],
[-1.0676654 , 3.08429323, 1.7601179 , 0.85448051, 1.15537064,
2.82487842, 0.27891413, 0.57842569, -0.62392063, 1.00343057,
1.15348843, -0.37650332, 3.37355345, 2.22285473, 0.43444434,
0.15743873, 0. , 0. , 0. , 0. ]]),
array([[ 1.05258873, -0.17897376, -0.99932932, -1.02854121, 0.85159208,
2.32349131, 1.96526709, -0.08398597, -0.69474809, 1.32820222,
1.19514151, 1.56814867, 0.86013263, 1.48342922, 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.1920635 , -0.48702788, 1.24353985, -1.3864121 , 0.16713229,
3.10134683, 0.61658271, -0.63360643, 0.86000807, 2.74876157,
2.87604877, 0.16339724, 2.87595396, 3.2846962 , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.1380241 , -0.76783029, 0.18814436, -1.18165209, -0.02981728,
1.49908113, 0.61521007, -0.98191097, 0.31250199, 1.39015803,
3.16213211, -0.70891214, 3.83881766, 1.92683533, 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.39080778, -0.59179216, 0.80348201, 0.64638205, -1.40144268,
1.49751413, 3.0092166 , 1.33099666, 1.43714841, 2.90734268,
3.09688943, 0.32934884, 1.14592787, 1.58152023, 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.77164353, 0.50293096, 0.0717377 , 0.14487556, -0.90246591,
2.32612179, 1.98628857, 1.29683166, -0.12399569, 2.60184685,
3.20136653, 0.44056647, 0.98283455, 1.79026663, 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[-0.93359914, 2.31840281, 0.55691601, 1.90930758, -1.58260431,
-1.05801881, 3.28012523, 3.84105406, -1.2127093 , 0.00490079,
1.28149304, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-1.03105486, 2.7703693 , 0.16751813, 1.12127987, -0.44070271,
-0.0789227 , 2.79008301, 1.11456745, 1.13982551, -1.10128658,
0.87430834, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.69710668, 1.72702833, -2.62599502, 2.34730002, 0.77756661,
0.16415884, 3.30712178, 1.67331828, -0.44022431, 0.56837829,
1.1566811 , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.71845983, 1.79908544, 0.37385522, 1.3870915 , -1.48823234,
-1.487419 , 3.0879945 , 1.74617784, -0.91538815, -0.24244522,
0.81393954, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-1.38501563, 3.73330047, -0.52494265, 2.37133716, -0.24546709,
-0.28360782, 2.89384717, 2.42891743, 0.40144022, -1.21850571,
2.00370751, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[ 1.27989188, 1.16254538, -0.06889142, 1.84133355, 1.3234908 ,
1.29611702, 2.0019294 , -0.03220116, 1.1085194 , 1.96495985,
1.68544302, 1.94503544, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.3004439 , 2.48768923, 0.59809607, 2.38155155, 2.78705889,
1.67018683, 0.21731778, -0.59277191, 2.87427207, 2.63950475,
2.39211459, 0.93083423, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.39239371, 0.30900383, -0.97307155, 1.98100711, 0.30613735,
1.12827171, 0.16987791, 0.31959096, 1.30366416, 1.45881023,
2.45668401, 0.5218711 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.0826574 , 2.05100254, 0.013161 , 2.95120798, 1.15730011,
0.75537024, 0.13708569, -0.44922143, 0.64834001, 2.50640862,
2.00349347, 3.35573624, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.47135124, 2.10258532, 0.70212032, 2.56063126, 1.62466971,
2.64026892, 0.21309489, -0.57752813, 2.21335957, 0.20453233,
0.03106993, 3.01167822, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[-0.42125521, 0.54016939, 1.63016057, 2.01555253, -0.10961255,
-0.42549555, 1.55793753, -0.0998756 , 0.36417335, 3.37126414,
1.62151191, 2.84084192, 0.10831384, 0.89293054, -0.08671363,
0.49340353, 0. , 0. , 0. , 0. ],
[-0.37615411, 2.00581062, 2.30426605, 2.02205839, 0.65871664,
1.34478836, -0.55379752, -1.42787727, 0.59732227, 0.84969282,
0.54345723, 0.95849568, -0.17131602, -0.70425277, -0.5337757 ,
1.78207229, 0. , 0. , 0. , 0. ],
[-0.13863276, 1.71490034, 2.02677925, 2.60608619, 0.26916522,
0.35928298, -1.26521844, -0.59859219, 1.19162219, 1.64565259,
1.16787165, 2.95245196, 0.48681084, 1.66621053, 0.918077 ,
-1.10583747, 0. , 0. , 0. , 0. ],
[ 0.87763797, 2.38740754, 2.9111822 , 2.21184069, 0.78091173,
-0.53270909, 0.40100338, -0.83375593, 0.9860009 , 2.43898437,
-0.64499989, 2.95092003, -1.52360727, 0.44640918, 0.78131922,
-0.24401283, 0. , 0. , 0. , 0. ],
[ 0.92615066, 3.45437746, 3.28808981, 2.87207404, -1.60027223,
-1.14164941, -1.63807699, 0.33084805, 2.92963629, 3.51170824,
-0.3286093 , 2.19108385, 0.97812366, -1.82565766, -0.34034678,
-2.0485913 , 0. , 0. , 0. , 0. ]]),
array([[ 1.96438618e+00, 1.88104784e-01, 1.61114494e+00,
6.99567690e-04, 2.55271963e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 2.41578815e+00, -5.70625661e-01, 2.15545894e+00,
-1.80948908e+00, 1.62049331e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 1.97017040e+00, -1.62556528e+00, 2.49469152e+00,
4.18785985e-02, 2.61875866e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 3.14277819e+00, 3.01098398e-02, 7.40376369e-01,
1.76517344e+00, 2.68922918e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 2.06250296e+00, 4.67605528e-01, 1.55927230e+00,
1.85788889e-01, 1.30359922e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00]]),
array([[ 1.22152427, 3.74926839, 0.64415552, 2.35268329, 1.98754653,
2.89384829, 0.44589817, 3.94228743, 2.72405657, 0.86222004,
0.68681903, 3.89952458, 1.43454512, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[-0.02203262, 0.95065123, 0.71669023, 0.02919391, 2.30714524,
1.91843002, 0.73611294, 1.20560482, 0.85206836, -0.74221506,
-0.72886308, 2.39872927, -0.95841402, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.55775319, 0.33773314, 0.79932151, 1.94966883, 3.2113281 ,
2.70768249, -0.69745554, 1.23208345, 1.66199957, 1.69894081,
0.13124461, 1.93256147, -0.17787952, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.45089205, 2.62430534, -1.9517961 , 2.24040577, 1.75642049,
1.94962325, 0.26796497, 2.28418304, 1.44944487, 0.28723885,
-0.81081633, 1.54840214, 0.82652939, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.27678173, 1.17204606, -0.24738322, 1.02761617, 1.81060444,
2.37830861, 0.55260134, 2.50046334, 1.04652821, 0.03467176,
-2.07336654, 1.2628897 , 0.61604732, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[ 3.86138405, 2.35068317, -1.90187438, 0.600788 , 0.18011722,
1.3469559 , -0.54708828, 1.83798823, -0.01957845, 2.88713217,
3.1724991 , 2.90802072, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.26785642, 0.51076756, 0.32070756, 2.33758816, 2.08146669,
-0.60796736, 0.93777509, 2.70474711, 0.44785738, 1.61720609,
1.52890594, 3.03072971, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 3.30219394, 3.1515445 , 1.16550716, 2.07489374, 0.66441859,
0.97529244, 0.35176367, 1.22593639, -1.80698271, 1.19936482,
3.34017172, 2.15960657, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.34839018, 2.24827352, -1.61070856, 2.81044265, -1.21423372,
0.24633846, -0.82196609, 2.28616568, 0.033922 , 2.7557593 ,
1.16178372, 3.66959512, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.32913219, 1.63231852, 0.58642744, 1.55873546, 0.86354741,
2.06654246, -0.44036504, 3.22723595, 1.33279468, 0.05975892,
2.48518999, 3.44690602, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[ 0.61424344, -1.03068819, -1.47929328, 2.91514641, 2.06867196,
1.90384921, -0.45835234, 1.22054782, 0.67931536, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.76480464, 1.12442631, -2.36004758, 2.91912726, 1.67891181,
3.76873596, -0.93874096, -0.32397781, -0.55732374, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0.39953353, -1.26828104, 0.44482517, 2.85604975, 3.08891062,
2.60268725, -0.15785176, 1.58549879, -0.32948578, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.65156484, -1.56545168, -1.42771206, 2.74216475, 1.8758154 ,
3.51169147, 0.18353058, -0.14704149, 0.00442783, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.27736372, 0.37407608, -1.25713475, 0.53171176, 1.53714914,
0.21015523, -1.06850669, -0.09755327, -0.92373834, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]),
array([[-1.39160433, 0.21014669, -0.89792475, 2.6702794 , 1.54610601,
0.84699037, 2.96726482, 1.84236946, 0.02211578, 0.32842575,
1.02718924, 1.78447936, -1.20056829, 2.26699318, -0.23156537,
2.50124959, 1.93372501, 0.10264369, -1.70813962, 0. ],
[ 0.38823591, -1.30348049, -0.31599117, 2.60044143, 2.32929389,
1.40348483, 3.25758736, 1.92210728, -0.34150988, -1.22336921,
2.3567069 , 1.75456835, 0.28295694, 0.68114898, -0.457843 ,
1.83372069, 2.10177851, -0.26664178, -0.26549595, 0. ],
[ 0.08540346, 0.71507504, 1.78164285, 3.04418137, 1.52975256,
3.55159169, 3.21396003, 3.22720346, 0.68147142, 0.12466013,
-0.4122895 , 1.97986653, 1.51671949, 2.06096825, -0.6765908 ,
2.00145086, 1.73723014, 0.50186043, -2.27525744, 0. ],
[ 0.00632717, 0.3050794 , -0.33167875, 1.48109172, 0.19653696,
1.97504239, 2.51595821, 1.74499313, -1.65198805, -1.04424953,
-0.23786945, 1.18639347, -0.03568057, 3.82541131, 2.84039446,
2.88325909, 1.79827675, -0.80230291, 0.08165052, 0. ],
[ 0.89980086, 0.34690991, -0.60806566, 1.69472308, 1.38043417,
0.97139487, 0.21977176, 1.01340944, -1.69946943, -0.01775586,
-0.35851919, 1.81115864, 1.15105661, 1.21410373, 1.50667558,
1.70155313, 3.1410754 , -0.54806167, -0.51879299, 0. ]])]
y_batches_train
[array([1., 2., 2., 1., 1., 2., 2., 1., 1., 2., 1., 1., 2., 2., 1., 2., 0.,
0., 0., 0.]),
array([1., 1., 1., 1., 1., 2., 2., 1., 1., 2., 2., 1., 2., 2., 0., 0., 0.,
0., 0., 0.]),
array([1., 2., 1., 2., 1., 1., 2., 2., 1., 1., 2., 0., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([2., 2., 1., 2., 2., 2., 1., 1., 2., 2., 2., 2., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([1., 2., 2., 2., 1., 1., 1., 1., 2., 2., 1., 2., 1., 1., 1., 1., 0.,
0., 0., 0.]),
array([2., 1., 2., 1., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([1., 2., 1., 2., 2., 2., 1., 2., 2., 1., 1., 2., 1., 0., 0., 0., 0.,
0., 0., 0.]),
array([2., 2., 1., 2., 1., 1., 1., 2., 1., 2., 2., 2., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([2., 1., 1., 2., 2., 2., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0.]),
array([1., 1., 1., 2., 2., 2., 2., 2., 1., 1., 1., 2., 1., 2., 1., 2., 2.,
1., 1., 0.])]
X_batches_test
[array([[ 0.74119496, 1.97273418, 1.76675805, 0.51484268, 1.39422086,
2.97184667, -1.35274514, 2.08825434, -1.2521965 , 1.11556387,
0.19776789, 2.38259223, -0.57140597, -0.79010112, 0.17038974,
1.28075761, 0.696398 , 3.0920007 , -0.41138503, 0. ],
[-1.39081797, 0.41079718, 3.03698894, -2.07333633, 2.05575621,
2.73222939, -0.98182787, 1.06741172, -1.36310914, 0.20174856,
0.35323654, 2.70305775, 0.52549713, -0.7786237 , 1.80857093,
0.96830907, -0.23610863, 1.28160768, 0.7026651 , 0. ],
[ 1.16357113, 0.43907935, 3.40158623, -0.73923043, 1.484668 ,
1.52809569, -0.02347205, 1.65349967, 1.79635118, -0.46647772,
-0.78400883, 0.82695404, -1.34932627, -0.3200281 , 2.84417045,
0.01534261, 0.10047148, 2.70769609, -1.42669461, 0. ],
[-1.05475682, 3.45578027, 1.58589338, -0.55515227, 2.13477478,
1.86777473, 0.61550335, 1.05781415, -0.45297406, -0.04317595,
-0.15255388, 0.74669395, -1.43621979, 1.06229278, 0.99792794,
1.24391783, -1.86484584, 1.92802343, 0.56148011, 0. ],
[-0.0835337 , 1.89593955, 1.65769335, -0.93622246, 1.05002869,
1.49675624, -0.00821712, 1.71541053, 2.02408452, 0.59011484,
0.72719784, 3.44801858, -0.00957537, 0.37176007, 1.93481168,
2.23125062, 1.67910471, 2.80923862, 0.34516993, 0. ]]),
array([[ 0.40691415, 2.31873444, -0.83458005, -0.17018249, -0.39177831,
1.90353251, 2.98241467, 0.32808584, 3.09429553, 2.27183083,
3.09576659, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.6862473 , 1.0690102 , -0.07415598, -0.09846767, 1.14562424,
2.52211963, 1.71911351, 0.41879894, 1.62787544, 3.50533394,
2.69963456, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 3.27824216, 2.25067953, 0.40017321, -1.36011162, -1.41010106,
0.98956203, 2.30881584, -0.29496046, 2.29748247, 3.24940966,
1.06431776, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2.80167214, 3.88324559, -0.6984172 , 0.81889567, 1.86945352,
3.07554419, 3.10357189, 1.31426767, 0.28163147, 2.75559628,
2.00866885, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 1.54574419, 1.00720596, -1.55418837, 0.70823839, 0.14715209,
1.03747262, 0.82988672, -0.54006372, 1.4960777 , 0.34578788,
1.10558132, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]])]
y_batches_test
[array([1., 2., 2., 1., 2., 2., 1., 2., 1., 1., 1., 2., 1., 1., 2., 2., 1.,
2., 1., 0.]),
array([2., 2., 1., 1., 1., 2., 2., 1., 2., 2., 2., 0., 0., 0., 0., 0., 0.,
0., 0., 0.])]
第一个问题：你reshaping后的X数据不是你期望的。再看reshape后的第一个sample是：
array([[[-1.00612917, 1.47313952, 2.68021318, 1.54875809,
0.98385996],
[ 1.49465265, 0.60429106, 1.12396908, -0.24041602,
1.77266187],
[ 0.1961381 , 1.28019637, 1.78803092, 2.05151245,
0.93606708],
[ 0.51554755, 0. , 0. , 0. ,
0. ],
[-0.97596563, 2.04536053, 0.88367922, 1.013342 ,
-0.16605355],
[ 3.02994344, 2.04080806, -0.25153046, -0.5964068 ,
2.9607247 ],
[-0.49722121, 0.02734492, 2.16949987, 2.77367066,
0.15628842],
[ 2.19823207, 0. , 0. , 0. ,
0. ],
[ 0.31546283, 3.27420503, 3.23550769, -0.63724013,
0.89150128],
[ 0.69774266, 2.76627308, -0.58408384, -0.45681779,
1.98843041],
[-0.31850477, 0.83729882, 0.45471165, 3.61974147,
-1.45610756],
[ 1.35217453, 0. , 0. , 0. ,
0. ],
[ 1.03329532, 1.97471646, 1.33949611, 1.22857243,
-1.46890642],
[ 1.74105506, 1.40969261, 0.52465603, -0.18895266,
2.81025597],
[ 2.64901037, -0.83415186, 0.76956826, 1.48730868,
-0.16190164],
[ 2.24389007, 0. , 0. , 0. ,
0. ],
[-1.0676654 , 3.08429323, 1.7601179 , 0.85448051,
1.15537064],
[ 2.82487842, 0.27891413, 0.57842569, -0.62392063,
1.00343057],
[ 1.15348843, -0.37650332, 3.37355345, 2.22285473,
0.43444434],
[ 0.15743873, 0. , 0. , 0. ,
0. ]]])
所以实际上没有时间步被屏蔽，因为Masking层只屏蔽所有特征为0的时间步，所以上面20个时间步没有被屏蔽，因为其中none个完全为0。
对于Masking层，为了确保将mask成功传播到输出层，您可以这样做：
for i, l in enumerate(model.layers):
print(f'layer {i}: {l}')
print(f'has input mask: {l.input_mask}')
print(f'has output mask: {l.output_mask}')
layer 0: <tensorflow.python.keras.layers.core.Masking object at 0x6417b7f60>
has input mask: None
has output mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
layer 1: <tensorflow.python.keras.layers.wrappers.Bidirectional object at 0x641e25cf8>
has input mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
has output mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
layer 2: <tensorflow.python.keras.layers.core.Dropout object at 0x641814128>
has input mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
has output mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
layer 3: <tensorflow.python.keras.layers.wrappers.TimeDistributed object at 0x6433b6ba8>
has input mask: Tensor("masking/Identity_1:0", shape=(None, 20), dtype=bool)
has output mask: Tensor("time_distributed/Reshape_3:0", shape=(None, 20), dtype=bool)
所以你可以看到最后一层也有output_mask，这意味着掩码传播成功。您似乎对Masking在Keras中的工作方式有误解，它实际上是会生成一个掩码，这是一个布尔数组，掩码的形状是（None，Timesteps），因为在您的模型中定义中，Timestep 维度始终保持不变，因此掩码将传播到末尾而不会发生任何变化。然后，当 Keras 计算损失时（当然还有计算梯度时），掩码值为 False 的时间步长将被忽略。 Masking 层不会改变输出值，当然你的模型仍然会预测 class 0，它所做的只是生成一个布尔数组，指示应该跳过哪个时间步并将它传递到最后（如果所有图层接受蒙版）。
所以你可以做的是如下更改模型定义的一行，并使你的 y_labels 移动 1，这意味着你当前的 classes:
0 -> 0（因为这些时间步的损失会被忽略，对模型的训练没有贡献，所以是0还是1无所谓）
1 -> 0
2 -> 1
# I would prefer softmax if doing classification
# here we only need to specify 2 classes
# and actually TimeDistributed can be thrown away (at least in recent Keras versions)
model.add(TimeDistributed(Dense(2, activation='softmax')))
您也可以在此处查看我的回答