我在构建一个具有自定义损失和激活函数的三层神经网络时遇到了以下错误：
File "C:\Users\untitled1.py", line 196, in <module> cModel.fit(X_train, y_train, batch_size=64, epochs=2) File "C:\Users\\Anaconda3\lib\site-packages\keras\engine\training.py", line 1184, in fit tmp_logs = self.train_function(iterator) File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 885, in __call__ result = self._call(*args, **kwds) File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 933, in _call self._initialize(args, kwds, add_initializers_to=initializers) File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 759, in _initialize self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 3066, in _get_concrete_function_internal_garbage_collected graph_function, _ = self._maybe_define_function(args, kwargs) File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 3463, in _maybe_define_function graph_function = self._create_graph_function(args, kwargs) File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 3298, in _create_graph_function func_graph_module.func_graph_from_py_func( File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1007, in func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 668, in wrapped_fn out = weak_wrapped_fn().__wrapped__(*args, **kwds) File "C:\Users\\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py", line 994, in wrapper raise e.ag_error_metadata.to_exception(e)TypeError: in user code: C:\Users\\Anaconda3\lib\site-packages\keras\engine\training.py:853 train_function * return step_function(self, iterator) TypeError: tf__compile() missing 1 required positional argument: 'loss'我使用的代码如下所示。我尝试使用自己的自定义损失函数和Keras的损失函数，但都出现了相同的错误。如果我只是初始化一个Keras的顺序模型并加入我的损失函数，它就能正常工作。但对于我的自定义模型就不行，我使用自定义模型是因为我还想在之后自定义优化方法。这个错误该如何解决？
#%% Functionsclass CustomModel(keras.Model): #initialize the model with the beta needed and the dimensions def __init__(self, b, input_dim): #first i initialized it as self, model and without the call function #but this gave me an error that said i needed a call function thus i changed it to this super(CustomModel, self).__init__() self.dim = keras.Input( shape=(input_dim,)) self.dense1 = keras.layers.Dense(20, name='hidden', kernel_initializer=initializer, bias_initializer=initializer, activation = lambda x: K.tanh(b*x)) self.dense2 = keras.layers.Dense(2, activation='linear', name='output', use_bias=False, trainable=False,kernel_initializer= lambda shape, dtype: initializeOutputWeights(shape, dtype)) def call(self): x1 = self.dense1(self.dim) return self.dense2(x1) def compile(self, optimizer, loss): #for the use of the custom loss function super(CustomModel, self).compile() self.optimizer = optimizer self.loss = loss def train_step(self, data): # Unpack the data. Its structure depends on your model and # on what you pass to `fit()`. x, y = data with tf.GradientTape() as tape: y_pred = self(x, training=True) # Forward pass # Compute the loss value # (the loss function is configured in `compile()`) loss = self.loss(y, y_pred, regularization_losses=self.losses) # Compute gradients trainable_vars = self.trainable_variables gradients = tape.gradient(loss, trainable_vars) # Update weights self.optimizer.apply_gradients(zip(gradients, trainable_vars)) # Update metrics (includes the metric that tracks the loss) self.compiled_metrics.update_state(y, y_pred) # Return a dict mapping metric names to current value return {m.name: m.result() for m in self.metrics}def initializeOutputWeights(shape, dtype=None): #output weights are initialized as 1 or -1 and not changed afterwards randoms = np.random.randint(low=2, size=shape) new = np.where(randoms==0, -1, randoms) return K.variable(new, dtype=dtype)class customLoss(keras.losses.Loss): #the custom loss function of the GVM def __init__(self, d=10, name = "CustomLoss"): #need the margin d super().__init__(name=name) self.d = d def call(self,y_true, y_pred): #calculate the loss N = len(y_true) L = len(y_pred[0]) y_dot = y_pred*y_true y_d = y_dot-self.d y_square= y_d*y_d index_replace = y_dot>self.d idx_replace=tf.where(index_replace==True) y_loss = tf.tensor_scatter_nd_update(y_square, idx_replace, tf.zeros(len(idx_replace))) return tf.divide(K.sum(K.sum(y_loss, axis=1)),tf.cast(N*L, tf.float32))seed(1)tf.random.set_seed(2)acc_metric = keras.metrics.SparseCategoricalAccuracy(name="accuracy")initializer = tf.keras.initializers.RandomUniform(minval=-1, maxval=1)b = np.ones(20)cModel = CustomModel(b, 9)Losscustom = customLoss(d=16)cModel.compile(optimizer='adam',loss=Losscustom)cModel.fit(X_train, y_train, batch_size=64, epochs=2)回答：
您似乎将一些不兼容的构造混淆了。我建议您定义自己的自定义训练循环，这将为您提供所需的灵活性：
首先定义您的模型：
导入tensorflow as tf导入random导入numpy as npdef initializeOutputWeights(shape, dtype=None): #输出权重被初始化为1或-1，并且之后不会更改 randoms = np.random.randint(low=2, size=shape) new = np.where(randoms==0, -1, randoms) return tf.keras.backend.variable(new, dtype=dtype)class CustomModel(tf.keras.Model): def __init__(self, b, input_dim): #最初我将其初始化为self, model并且没有call函数 #但这导致了一个错误，说我需要一个call函数，因此我将其更改为这样 super(CustomModel, self).__init__() initializer = tf.keras.initializers.RandomUniform(minval=-1, maxval=1) self.dense = tf.keras.layers.Dense(20, name='hidden', kernel_initializer=initializer, bias_initializer=initializer, activation = lambda x: tf.tanh(b*x), input_shape=(input_dim,)) self._output = tf.keras.layers.Dense(2, activation='linear', name='output', use_bias=False, trainable=False,kernel_initializer= lambda shape, dtype: initializeOutputWeights(shape, dtype)) def call(self, inputs): x = self.dense(inputs) return self._output(x)