Question

我是Keras的新手。给定一个输入数组，我想执行一维卷积。我该怎么办？我已经编写了代码，但无法运行

from keras.models import Sequential
from keras.layers import Conv1D
import numpy as np
import tensorflow as tf
from keras.models import Model

input = np.array(tf.constant([1,2,3,4,5,6,7,8]))   
model = Sequential()
model.add(Conv1D(1,3,strides=1,padding='same', name='conv'))

layer_model = Model(inputs=input,outputs=model.get_layer('conv').output)
conv_output = layer_model.predict(f)    
print (conv_output)

这是错误

    AttributeError                            Traceback (most recent call last)
<ipython-input-13-31db6e7dcb90> in <module>()
     10 
     11 layer_model = Model(inputs=input,
---> 12                                 outputs=model.get_layer('conv').output)
     13 conv_output = layer_model.predict(f)
     14 print (conv_output)

/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in output(self)
    808         if not self._inbound_nodes:
    809             raise AttributeError('Layer ' + self.name +
--> 810                                  ' has no inbound nodes.')
    811         if len(self._inbound_nodes) > 1:
    812             raise AttributeError('Layer ' + self.name +

AttributeError: Layer conv has no inbound nodes.

Answer 1

您不应通过混合使用功能性API和顺序性API来使自己复杂化，更简单的方法是：

from keras.models import Sequential
from keras.layers import Conv1D
import numpy as np

input = np.array([1,2,3,4,5,6,7,8])
input = input.reshape(1, 8, 1) # (samples, width, channels)   

model = Sequential()
model.add(Conv1D(1,3,padding='same', input_shape=(8, 1)))

conv_output = model.predict(input)    
print(conv_output)

请注意，我们尚未设置卷积权重，因此它们会自动初始化为随机值。

无法将1D信号前馈给Keras中的1D卷积

1 个答案: