tensorflow - 在 4 维（4D 张量）的输入上使用 MaxPool1D？

Question

我正在尝试为多实例学习创建一个 NN 架构，因此这些实例实际上是时间序列段的包。我想在特征（最后一个维度）上执行 COnv1D 和 MaxPool1D，我将输入指定为具有 4 个维度，并且对 Conv1D 工作正常，但在 MaxPool1D 中引发错误：

n = 6
sample_size = 300
code_size = 50
learning_rate = 0.001
bag_size = None

# autoencoder: n_bags X bag_size X n_samples (timesteps) X n_measurements
input_window = Input(shape=(bag_size,sample_size, n)) 
x = Conv1D(filters=40, kernel_size=21, activation='relu', padding='valid')(input_window)
x = MaxPooling1D(pool_size=2)(x)

错误是：

ValueError: Input 0 of layer max_pooling1d_4 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, None, 280, 40]

根据TF 文档 MaxPool1D 仅适用于 3D 张量。有解决办法吗？

标签： tensorflowmachine-learningkeras

Answer 1

虽然尚不清楚您要在哪个轴上进行池化，但您可以使用MaxPooling2D正确的池化大小，在这种情况下，IIUC 将是(1,2)

from tensorflow.keras import layers, Model

n = 6
sample_size = 300
code_size = 50
learning_rate = 0.001
n_bags= None

# autoencoder: n_bags X n_instances_in_bag X n_samples (timesteps) X n_measurements
input_window = layers.Input(shape=(n_bags,sample_size, n)) 
x = layers.Conv1D(filters=40, kernel_size=21, activation='relu', padding='valid')(input_window)

x = layers.MaxPooling2D(pool_size=(1,2))(x)

model = Model(input_window, x)

model.summary()

Model: "model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         [(None, None, 300, 6)]    0         
_________________________________________________________________
conv1d_3 (Conv1D)            (None, None, 280, 40)     5080      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, None, 140, 40)     0         
=================================================================
Total params: 5,080
Trainable params: 5,080
Non-trainable params: 0
_________________________________________________________________