ConvLSTM1D的屏蔽输入

Question

我正在用keras做一个二元回归问题。输入形状为：(None, 2, 94, 3)(通道为最后一个维度(

我有以下架构：

input1 = Input(shape=(time, n_rows, n_channels))
masking = Masking(mask_value=-999)(input1)
convlstm = ConvLSTM1D(filters=16, kernel_size=15,
data_format='channels_last',
activation="tanh")(masking)
dropout = Dropout(0.2)(convlstm)
flatten1 = Flatten()(dropout)
outputs = Dense(n_outputs, activation='sigmoid')(flatten1)
model = Model(inputs=input1, outputs=outputs)
model.compile(loss=keras.losses.BinaryCrossentropy(),
optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))

然而，当训练时，我得到了这个错误：Dimensions must be equal, but are 94 and 80 for '{{node conv_lstm1d/while/SelectV2}} = SelectV2[T=DT_FLOAT](conv_lstm1d/while/Tile, conv_lstm1d/while/mul_5, conv_lstm1d/while/Placeholder_2)' with input shapes: [?,94,16], [?,80,16], [?,80,16].

如果我去除了掩蔽层，这个错误就会消失，那么掩蔽是怎么触发这个错误的呢？此外，我能够运行上述体系结构的唯一方法是使用1的kernel_size。

Answer 1

根据文档，ConvLSTM1D层似乎需要一个形状为(samples, timesteps)的遮罩。您正在计算的遮罩的形状为(samples, time, rows)。这里有一个解决方案可以解决你的问题，但我不确定这是否是"正确的"方法：

import tensorflow as tf
input1 = tf.keras.layers.Input(shape=(2, 94, 3))
masking = tf.keras.layers.Masking(mask_value=-999)(input1)
convlstm = tf.keras.layers.ConvLSTM1D(filters=16, kernel_size=15,
data_format='channels_last',
activation="tanh")(inputs = masking, mask = tf.reduce_all(masking._keras_mask, axis=-1))
dropout = tf.keras.layers.Dropout(0.2)(convlstm)
flatten1 = tf.keras.layers.Flatten()(dropout)
outputs = tf.keras.layers.Dense(1, activation='sigmoid')(flatten1)
model = tf.keras.Model(inputs=input1, outputs=outputs)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(),
optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))

这条线mask = tf.reduce_all(masking._keras_mask, axis=-1)通过对遮罩的最后一个维度应用AND运算，实质上将遮罩简化为(samples, timesteps)。或者，你可以创建自己的自定义遮罩层：

import tensorflow as tf
class Reduce(tf.keras.layers.Layer):
def __init__(self):
super(Reduce, self).__init__()
def call(self, inputs):
return tf.reduce_all(tf.reduce_any(tf.not_equal(inputs, -999), axis=-1, keepdims=False), axis=1)
input1 = tf.keras.layers.Input(shape=(2, 94, 3))
reduce_layer = Reduce()
boolean_mask = reduce_layer(input1)
convlstm = tf.keras.layers.ConvLSTM1D(filters=16, kernel_size=15,
data_format='channels_last',
activation="tanh")(inputs = input1, mask = boolean_mask)
dropout = tf.keras.layers.Dropout(0.2)(convlstm)
flatten1 = tf.keras.layers.Flatten()(dropout)
outputs = tf.keras.layers.Dense(1, activation='sigmoid')(flatten1)
model = tf.keras.Model(inputs=input1, outputs=outputs)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(),
optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))
print(model.summary(expand_nested=True))
x = tf.random.normal((50, 2, 94, 3))
y = tf.random.uniform((50, ), maxval=3, dtype=tf.int32)
model.fit(x, y)