我正在进行一个肺炎检测项目。我在kaggle上查看了相关的notebook。有一个用户将两个预训练模型densenet169和mobilenet堆叠在一起。我从该用户那里复制了整个kaggle notebook，他没有遇到任何错误，但在我在google colab上运行时，在这部分出现了错误：
出错的部分：
from keras.layers.merge import concatenate from keras.layers import Input import tensorflow as tf input_shape = (224,224,3) input_layer = Input(shape = (224, 224, 3)) #first model base_mobilenet = MobileNetV2(weights = 'imagenet', include_top = False, input_shape = input_shape) base_densenet = DenseNet169(weights = 'imagenet', include_top = False, input_shape = input_shape) for layer in base_mobilenet.layers: layer.trainable = False for layer in base_densenet.layers: layer.trainable = False model_mobilenet = base_mobilenet(input_layer) model_mobilenet = GlobalAveragePooling2D()(model_mobilenet) output_mobilenet = Flatten()(model_mobilenet) model_densenet = base_densenet(input_layer) model_densenet = GlobalAveragePooling2D()(model_densenet) output_densenet = Flatten()(model_densenet) merged = tf.keras.layers.Concatenate()([output_mobilenet, output_densenet]) x = BatchNormalization()(merged) x = Dense(256,activation = 'relu')(x) x = Dropout(0.5)(x) x = BatchNormalization()(x) x = Dense(128,activation = 'relu')(x) x = Dropout(0.5)(x) x = Dense(1, activation = 'sigmoid')(x) stacked_model = tf.keras.models.Model(inputs = input_layer, outputs = x)错误追溯：
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-35-69c389bc7252> in <module>() 18 model_mobilenet = base_mobilenet(input_layer) 19 model_mobilenet = GlobalAveragePooling2D()(model_mobilenet)---> 20 output_mobilenet = Flatten(data_format=None)(model_mobilenet) 21 22 model_densenet = base_densenet(input_layer)5 frames/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs) 1028 with autocast_variable.enable_auto_cast_variables( 1029 self._compute_dtype_object):-> 1030 outputs = call_fn(inputs, *args, **kwargs) 1031 1032 if self._activity_regularizer:/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py in call(self, inputs) 672 # Full static shape is guaranteed to be available. 673 # Performance: Using `constant_op` is much faster than passing a list.--> 674 flattened_shape = constant_op.constant([inputs.shape[0], -1]) 675 return array_ops.reshape(inputs, flattened_shape) 676 else:/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name) 263 """ 264 return _constant_impl(value, dtype, shape, name, verify_shape=False,--> 265 allow_broadcast=True) 266 267 /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast) 274 with trace.Trace("tf.constant"): 275 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)--> 276 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape) 277 278 g = ops.get_default_graph()/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in _constant_eager_impl(ctx, value, dtype, shape, verify_shape) 299 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape): 300 """Implementation of eager constant."""--> 301 t = convert_to_eager_tensor(value, ctx, dtype) 302 if shape is None: 303 return t/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype) 96 dtype = dtypes.as_dtype(dtype).as_datatype_enum 97 ctx.ensure_initialized()---> 98 return ops.EagerTensor(value, ctx.device_name, dtype) 99 100 ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.回答：
你的导入有些混乱。
这是修复后的代码版本
from tensorflow.keras.layers import concatenatefrom tensorflow.keras.layers import Input, GlobalAveragePooling2D, Flatten, BatchNormalization, Dense, Dropoutfrom tensorflow.keras.applications import MobileNetV2, DenseNet169import tensorflow as tfinput_shape = (224,224,3)input_layer = Input(shape = (224, 224, 3))#first modelbase_mobilenet = MobileNetV2(weights = 'imagenet', include_top = False, input_shape = input_shape)base_densenet = DenseNet169(weights = 'imagenet', include_top = False, input_shape = input_shape)for layer in base_mobilenet.layers: layer.trainable = Falsefor layer in base_densenet.layers: layer.trainable = Falsemodel_mobilenet = base_mobilenet(input_layer)model_mobilenet = GlobalAveragePooling2D()(model_mobilenet)output_mobilenet = Flatten()(model_mobilenet)model_densenet = base_densenet(input_layer)model_densenet = GlobalAveragePooling2D()(model_densenet)output_densenet = Flatten()(model_densenet)merged = tf.keras.layers.Concatenate()([output_mobilenet, output_densenet]) x = BatchNormalization()(merged)x = Dense(256,activation = 'relu')(x)x = Dropout(0.5)(x)x = BatchNormalization()(x)x = Dense(128,activation = 'relu')(x)x = Dropout(0.5)(x)x = Dense(1, activation = 'sigmoid')(x)stacked_model = tf.keras.models.Model(inputs = input_layer, outputs = x)