我想使用'功能API構(gòu)建一個(gè)模型。我的模型非常大，因此我想通過繼承來自 來創(chuàng)建自定義圖層。以下是我的嘗試，靈感來自TensorFlow的文檔。tf.kerastf.keras.layers.Layerimport tensorflow as tfclass Conv2D(tf.keras.layers.Layer): def __init__(self): super().__init__() input_layer = tf.keras.layers.Input( shape=(256, 256, 3) ) self.conv = tf.keras.layers.Conv2D( filters=16, kernel_size=3, strides=(1, 1), padding="same" )(input_layer) def call(self, inputs): return self.conv(inputs)outer_input_layer = tf.keras.layers.Input( shape=(256, 256, 3))x = Conv2D()(outer_input_layer)此代碼崩潰，出現(xiàn)以下錯(cuò)誤。Traceback (most recent call last): File "c:\Users\user\.vscode\extensions\ms-python.python-2020.2.64397\pythonFiles\ptvsd_launcher.py", line 48, in <module> main(ptvsdArgs) File "c:\Users\user\.vscode\extensions\ms-python.python-2020.2.64397\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 432, in main run() File "c:\Users\user\.vscode\extensions\ms-python.python-2020.2.64397\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 316, in run_file runpy.run_path(target, run_name='__main__') File "C:\Users\user\code\.env\lib\runpy.py", line 263, in run_path pkg_name=pkg_name, script_name=fname) File "C:\Users\user\code\.env\lib\runpy.py", line 96, in _run_module_code mod_name, mod_spec, pkg_name, script_name) File "C:\Users\user\code\.env\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "c:\Users\user\code\tests.py", line 23, in <module> x = Conv2D()(outer_input_layer) File "C:\Users\user\code\.env\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 773, in __call__ outputs = call_fn(cast_inputs, *args, **kwargs) File "C:\Users\user\code\.env\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 237, in wrapper raise e.ag_error_metadata.to_exception(e)TypeError: in converted code:我的方法有什么問題？
1 回答
慕桂英3389331
TA貢獻(xiàn)2036條經(jīng)驗(yàn) 獲得超8個(gè)贊
沒有自定義圖層具有“輸入”圖層。這沒有多大意義。輸入是您在調(diào)用圖層時(shí)傳遞給圖層的內(nèi)容。
Vo_
import tensorflow as tf
class ConvBN(tf.keras.layers.Layer):
def __init__(self, activation, name):
super().__init__()
#here you just "store" the layers, you don't use them
#you also store any other property you find necessary for the call
self.conv = tf.keras.layers.Conv2D(
filters=16,
kernel_size=3,
strides=(1, 1),
padding="same",
name = name+'_conv'
)
self.bn = tf.keras.layers.BatchNormalization(name = name + "_bn")
self.activation = tf.keras.layers.Activation(activation, name = name + "_act")
def call(self, inputs):
#here you "use" the layers with the given input to produce an output
out = self.conv(inputs)
out = self.bn(out)
out = self.activation(out)
return out
如果您不打算多次使用“同一層”，也可以創(chuàng)建更簡單的 blok：
def convGroup(input_tensor, activation, name):
out = tf.keras.layers.Conv2D(
filters=16,
kernel_size=3,
strides=(1, 1),
padding="same",
name = name+'_conv'
)(input_tensor)
out = tf.keras.layers.BatchNormalization(name = name + "_bn")(out)
out = tf.keras.layers.Activation(activation, name = name + "_act")(out)
return out
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