【发布时间】：2020-05-13 07:31:02
【问题描述】：
我无法理解为什么在编译 tf.keras 模型时连接层不能与 year_input 一起使用。
详情：
- 要连接的 3 层是
tf.float32类型。 - 将 Keras 功能 API 与 Tensorflow 2.1.0 GPU 结合使用。
- 3 层的类型为：
<tf.Tensor 'year_input_ll_12:0' shape=(None, 5) dtype=float32>。 - 如果我创建一个没有
year_input的模型，则模型编译正确。
def create_system_classifier_model(df, pretrain_model, create_date_df,
output_cat_nbr, spec_max_length, heading_max_length):
heading_input = tf.keras.layers.Input((heading_max_length,), name="heading_input", dtype=tf.int32)
spec_input = tf.keras.layers.Input((spec_max_length,), name="spec_input", dtype=tf.int32)
year_input_ts = tf.keras.layers.Input((5,), name="year_input_ll", dtype=tf.float32)
spec_embedding = pretrain_model(heading_input)[0]
heading_embedding = pretrain_model(spec_input)[0]
heading_pool_ts = tf.keras.layers.GlobalAveragePooling1D()(spec_embedding)
spec_pool_ts = tf.keras.layers.GlobalAveragePooling1D()(heading_embedding)
concat = tf.keras.layers.concatenate([heading_pool_ts, spec_pool_ts, year_input_ts], axis=1)
dense_ts_1 = tf.keras.layers.Dense(768, activation='relu', name='dense_ts_1')(concat)
dense_ts_2 = tf.keras.layers.Dense(768, activation='relu', name='dense_ts_2')(dense_ts_1)
dense_ts_3 = tf.keras.layers.Dense(768, activation='relu', name='dense_ts_3')(dense_ts_2)
drop_ts = tf.keras.layers.Dropout(0.2)(dense_ts_3)
output_ts = tf.keras.layers.Dense(output_cat_nbr, activation='sigmoid')(drop_ts)
model = tf.keras.models.Model(inputs=[heading_input, spec_input, year_input], outputs=output_ts)
return model
函数调用
system_classifier_model = create_system_classifier_model(df,
distil_bert_model,
create_date_df,
len(system_cat_ls),
spec_max_length,
heading_max_length)
错误
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-171-367b40edc46e> in <module>()
4 len(system_cat_ls),
5 spec_max_length,
----> 6 heading_max_length)
5 frames
<ipython-input-170-6b485af7c7be> in create_system_classifier_model(df, pretrain_model, create_date_df, output_cat_nbr, spec_max_length, heading_max_length)
20 output_ts = tf.keras.layers.Dense(output_cat_nbr, activation='sigmoid')(drop_ts)
21
---> 22 model = tf.keras.models.Model(inputs=[heading_input, spec_input], outputs=output_ts)
23 model = tf.keras.models.Model(inputs=[heading_input, spec_input, year_input], outputs=output_ts)
24
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in __init__(self, *args, **kwargs)
144
145 def __init__(self, *args, **kwargs):
--> 146 super(Model, self).__init__(*args, **kwargs)
147 _keras_api_gauge.get_cell('model').set(True)
148 # initializing _distribution_strategy here since it is possible to call
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in __init__(self, *args, **kwargs)
167 'inputs' in kwargs and 'outputs' in kwargs):
168 # Graph network
--> 169 self._init_graph_network(*args, **kwargs)
170 else:
171 # Subclassed network
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
322 # Keep track of the network's nodes and layers.
323 nodes, nodes_by_depth, layers, _ = _map_graph_network(
--> 324 self.inputs, self.outputs)
325 self._network_nodes = nodes
326 self._nodes_by_depth = nodes_by_depth
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in _map_graph_network(inputs, outputs)
1674 'The following previous layers '
1675 'were accessed without issue: ' +
-> 1676 str(layers_with_complete_input))
1677 for x in nest.flatten(node.output_tensors):
1678 computable_tensors.add(id(x))
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("year_input_ll_11:0", shape=(None, 5), dtype=float32) at layer "year_input_ll". The following previous layers were accessed without issue: ['spec_input', 'heading_input', 'tf_distil_bert_model_2', 'tf_distil_bert_model_2', 'global_average_pooling1d_72', 'global_average_pooling1d_73']
【问题讨论】：
-
在您的代码 sn-p 中，您有 2 个针对
model = ...的语句。复制粘贴到这里是不是错字？还是您的代码中也是这种情况？这肯定会导致您描述的错误，因为inputs列表中缺少year_input。 -
@MarkLoyman 这是一个错字。我做了更正。感谢您指出这一点。
-
你能更新一下你发布的错误信息吗？
-
@MarkLoyman，它现在正在工作。您指出的
Model =声明的重复解决了这个问题......（对此我并不感到自豪）谢谢您的帮助。
标签： python tensorflow keras