我在尝试使用prelu激活函数训练模型时，遇到了以下错误
---------------------------------------------------------------------------ValueError Traceback (most recent call last)/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name) 2965 shape = constant_op._tensor_shape_tensor_conversion_function(-> 2966 tensor_shape.TensorShape(shape)) 2967 except (TypeError, ValueError):31 framesValueError: Cannot convert a partially known TensorShape to a Tensor: (None, None, 64)During handling of the above exception, another exception occurred:ValueError Traceback (most recent call last)/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.我使用了下面的代码，请告诉我如何修正它。
from tensorflow.keras.applications import MobileNetfrom tensorflow.keras.layers import (Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dropout, Dense)from tensorflow.keras import Modelfrom tensorflow import kerasCLASSES = 2#model.compile()# setup modelbase_model = MobileNet(weights='imagenet', include_top=False)input = (224, 224, 3)x = base_model.outputx = Conv2D(64, (3,3), padding='same', activation = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None), strides= (2,2), name='layer1')(x)x = MaxPooling2D(pool_size=(2,2))(x)x = Conv2D(128, (3,3), padding='same', activation = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None), name='layer2')(x)x = GlobalAveragePooling2D(name='avg_pool')(x)x = Dropout(0.4)(x)predictions = Dense(CLASSES, activation=tf.keras.activations.sigmoid)(x)model = Model(inputs=base_model.input, outputs=predictions) # transfer learningfor layer in base_model.layers: layer.trainable = False model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])回答：
你的张量输入有误。你需要像这样设置它
input_s = layers.Input((224, 224, 3))base_model = keras.applications.MobileNet(weights='imagenet', include_top=False, input_tensor=input_s)...完整的工作代码
from tensorflow.keras.applications import MobileNetfrom tensorflow.keras.layers import (Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dropout, Dense)from tensorflow.keras import Modelfrom tensorflow import kerasfrom tensorflow.keras import layersimport tensorflow as tf CLASSES = 2# setup modelinput_s = layers.Input((224, 224, 3))base_model = keras.applications.MobileNet(weights='imagenet', include_top=False, input_tensor=input_s)x = layers.Conv2D(64, (3,3), padding='same', activation = keras.layers.PReLU( alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None), strides= (2,2), name='layer1')(base_model.output)x = layers.MaxPooling2D(pool_size=(2,2))(x)x = layers.Conv2D(128, (3,3), padding='same', activation = keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=None), name='layer2')(x)x = layers.GlobalAveragePooling2D(name='avg_pool')(x)x = layers.Dropout(0.4)(x)predictions = layers.Dense(CLASSES, activation=tf.keras.activations.sigmoid)(x)model = tf.keras.Model(inputs=base_model.input, outputs=predictions)# transfer learningfor layer in base_model.layers: layer.trainable = Falsemodel.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])