TypeError：__init __（）獲得了意外的關鍵字參數“ trainable”

Question

我正在嘗試使用keras.models.model_from_json加載在Keras中訓練的RNN模型架構，並且遇到上述錯誤

with open('model_architecture.json', 'r') as f:
    model = model_from_json(f.read(), custom_objects={'AttLayer':AttLayer})

# Load weights into the new model
model.load_weights('model_weights.h5')

這是我正在使用的自定義層

class AttLayer(Layer):
    def __init__(self, attention_dim):
        self.init = initializers.get('normal')
        self.supports_masking = True
        self.attention_dim = attention_dim
        super(AttLayer, self).__init__()

    def build(self, input_shape):
        assert len(input_shape) == 3
        self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))
        self.b = K.variable(self.init((self.attention_dim, )))
        self.u = K.variable(self.init((self.attention_dim, 1)))
        self.trainable_weights = [self.W, self.b, self.u]
        super(AttLayer, self).build(input_shape)

    def compute_mask(self, inputs, mask=None):
        return None

    def call(self, x, mask=None):
        # size of x :[batch_size, sel_len, attention_dim]
        # size of u :[batch_size, attention_dim]
        # uit = tanh(xW+b)
        uit = K.tanh(K.bias_add(K.dot(x, self.W), self.b))
        ait = K.dot(uit, self.u)
        ait = K.squeeze(ait, -1)

        ait = K.exp(ait)

        if mask is not None:
            # Cast the mask to floatX to avoid float64 upcasting in theano
            ait *= K.cast(mask, K.floatx())
        ait /= K.cast(K.sum(ait, axis=1, keepdims=True) + K.epsilon(), K.floatx())
        ait = K.expand_dims(ait)
        weighted_input = x * ait
        output = K.sum(weighted_input, axis=1)

        return output

    def compute_output_shape(self, input_shape):
        return (input_shape[0], input_shape[-1])

    def get_config(self):
        config = {'attention_dim': self.attention_dim}
        base_config = super(AttLayer, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

錯誤：

File "scripts/Classifier.py", line 254, in test
    model = model_from_json(f.read(), custom_objects={'AttLayer':AttLayer})
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/models.py", line 345, in model_from_json
    return layer_module.deserialize(config, custom_objects=custom_objects)
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/layers/__init__.py", line 54, in deserialize
    printable_module_name='layer')
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/utils/generic_utils.py", line 139, in deserialize_keras_object
    list(custom_objects.items())))
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 2489, in from_config
    process_layer(layer_data)
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 2475, in process_layer
    custom_objects=custom_objects)
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/layers/__init__.py", line 54, in deserialize
    printable_module_name='layer')
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/utils/generic_utils.py", line 139, in deserialize_keras_object
    list(custom_objects.items())))
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/layers/wrappers.py", line 100, in from_config
    custom_objects=custom_objects)
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/layers/__init__.py", line 54, in deserialize
    printable_module_name='layer')
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/utils/generic_utils.py", line 139, in deserialize_keras_object
    list(custom_objects.items())))
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 2489, in from_config
    process_layer(layer_data)
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 2475, in process_layer
    custom_objects=custom_objects)
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/layers/__init__.py", line 54, in deserialize
    printable_module_name='layer')
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/utils/generic_utils.py", line 141, in deserialize_keras_object
    return cls.from_config(config['config'])
  File "/home/biswadip/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 1254, in from_config
    return cls(**config)
TypeError: __init__() got an unexpected keyword argument 'trainable'

版本：

Keras==2.0.8
tensorflow==1.4.1

我嘗試使用不同的版本進行訓練和加載，但是沒有運氣。 最后，我從模型體系結構文件（model_architecture.json）中的自定義層詳細信息中刪除了“可訓練”和“名稱”（鍵值對），並且似乎正在加載模型而沒有任何錯誤。 但這看起來像是一種解決方法，每次訓練模型時都必須這樣做。

Answer 1

我認為您錯過了圖層定義中的一個小細節。 圖層的__init__方法應采用關鍵字參數（ **kwargs ），並將這些關鍵字參數傳遞給父類__init__ ，如下所示：

class AttLayer(Layer):
    def __init__(self, attention_dim, **kwargs):
        self.init = initializers.get('normal')
        self.supports_masking = True
        self.attention_dim = attention_dim
        super(AttLayer, self).__init__(**kwargs)

這樣，任何通用圖層參數都將正確地傳遞給父類，在您的情況下是trainable標志。