我想使用多個圖像作為網絡的輸入。我想添加Conv2D圖層，類似這樣的：from tensorflow.keras.layers import *from tensorflow.keras.models import Sequentialmodel = Sequential([ Input(shape=(1, 128, 128, 1)), Conv2D(32, 3), Flatten(),])但這段代碼會引發錯誤：Input 0 of layer conv2d_40 is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: [None, 1, 128, 128, 1]但下面的代碼工作正常：model = Sequential([ Input(shape=(1, 512, 512, 1)), Dense(32), Flatten(),])我知道，我可以添加多個Input圖層，但我想知道有沒有辦法做到這樣？我的意思是我想使用輸入形狀的數據[NUMBER_OF_IMAGES, WIDTH, HEIGHT, N_CHANNELS]并且NUMBER_OF_IMAGES不是所有圖像的數量。這是當前輸入的金額
1 回答
慕的地8271018
TA貢獻1796條經驗 獲得超4個贊
Conv2D需要 4D 輸入，您無法更改它。我不太確定你想要完成什么，但你可以使用Conv3D：
from tensorflow.keras.layers import *
from tensorflow.keras.models import Sequential
import tensorflow as tf
model = Sequential([
Input(shape=(None, 128, 128, 1)),
Conv3D(32, kernel_size=(1, 3, 3)),
Flatten()
])
multiple_images = tf.random.uniform((10, 10, 128, 128, 1), dtype=tf.float32)
model(multiple_images)
<tf.Tensor: shape=(10, 5080320), dtype=float32, numpy=
array([[-0.26742983, -0.09689523, -0.12120364, ..., -0.02987139,
0.05515741, 0.12026916],
[-0.18898709, 0.12448274, -0.17439063, ..., 0.23424357,
-0.06001307, -0.13852882],
[-0.14464797, 0.26356792, -0.34748033, ..., 0.07819699,
-0.11639086, 0.10701762],
...,
[-0.1536693 , 0.13642962, -0.18564 , ..., 0.07165999,
-0.0173855 , -0.04348694],
[-0.32320747, 0.09207243, -0.22274591, ..., 0.11940736,
-0.02635285, -0.1140241 ],
[-0.21126074, -0.00094431, -0.10933039, ..., 0.06002581,
-0.09649743, 0.09335127]], dtype=float32)>
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