python - ResNet50：TypeError：无法转换类型的对象<class 'tensorflow.python.framework.sparse

问题描述

尝试在 poly-u 数据集上实现 resnet 50。我在这里想要做的是我想制作一个模型，将个人的掌纹分类为不同的身份，我发现 resnet 最适合这类问题似乎无法克服这个错误在网上尝试了很多解决方案但由于某种原因无法解决：

TypeError: Failed to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'> to Tensor. Contents: SparseTensor(indices=Tensor("DeserializeSparse:0", shape=(None, 2), dtype=int64), values=Tensor("DeserializeSparse:1", shape=(None,), dtype=float32), dense_shape=Tensor("stack:0", shape=(2,), dtype=int64)). Consider casting elements to a supported type.

身份块

import keras
def identity_block(X,f,filters):
    
#     retrieve filters
    F1,F2,F3 = filters
    X_shortcut = X
    
#     first layer
    X = Conv2D(filters = F1, kernel_size = (1,1),strides= (1,1),padding ='valid') (X)
    X = BatchNormalization(axis = 3 )(X)
    X = Activation('relu')(X)
    
#     second layer
    X = Conv2D(filters = F2, kernel_size = (f,f),strides= (1,1),padding ='same') (X)
    X = BatchNormalization(axis = 3 )(X)
    X = Activation('relu')(X)
    
#     third layer
    X = Conv2D(filters = F3, kernel_size = (1,1),strides= (1,1),padding ='valid') (X)
    X = BatchNormalization(axis = 3 )(X)
    
#     final step > adding shortcut value through relu activation
#     X = Add()([X,X_shortcut])
    X = tf.keras.layers.Add()([X,X_shortcut])
#     X =  keras.layers.concatenate() ([X,X_shortcut])
    X = Activation('relu')(X)
    
    return X

卷积块

def convolutional_block(X,f,filters,s =2):
    
#     retrieve filters
    F1,F2,F3 = filters
    
#     save the input values
    X_shortcut = X
    
#     first layer
    X = Conv2D(F1,(1,1),strides=(s,s))(X)
    X = BatchNormalization(axis =3)(X)
    X = Activation('relu')(X)
    
#     second layer
    X = Conv2D(filters = F2,kernel_size =(f,f),strides = (1,1), padding ="same")(X)
    X = BatchNormalization(axis =3)(X)
    X = Activation('relu')(X)
    
#     third layer
    X = Conv2D(filters = F3,kernel_size =(1,1),strides =(1,1), padding ="valid")(X)
    X = BatchNormalization(axis = 3 )(X)
    
#     shortcut path
    
    X_shortcut = Conv2D(filters = F3,kernel_size =(1,1),strides = (s,s),padding ='valid')(X_shortcut)
    X_shortcut = BatchNormalization( axis =3)(X_shortcut)
    
#     final step > adding shortcut value through relu activation
#     X = ADD()([X,X_shortcut])
    X = tf.keras.layers.Add()([X,X_shortcut])
#     X =  keras.layers.concatenate() ([X,X_shortcut])
    X = Activation('relu')(X)
    
    return X

resnet50 架构

def Resnet50(input_shape= (224,224,3),classes = 309 ):
    
#     implementing the resnet 50 achitechture over here
    
#     define the input with shape input_shape
    X_input = Input(input_shape)
    
#     zero padding 
    X = ZeroPadding2D((3,3))(X_input)
    
#     stage 1 
    X = Conv2D(64,(7,7),strides = (2,2))(X)
    X = BatchNormalization(axis =3 )(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((3,3),strides =(2,2))(X)
    
#     stage 2
    X = convolutional_block(X,f=3 , filters = [64,64,256],s=1)
    
#     these line of code are the conv laters from convolution block function 
#     X = Conv2d(F1,(1,1),strides=(s,s))(X)
#     X = Conv2D(filters = F2,kernel_size =(f,f),strides = (1,1), padding ="same")(X)
#     X = conv2D(F3 ,(1,1),strides = (s,s),name = conv_name_base +'2a')(X)


    X = identity_block(X ,3,[ 64, 64, 256])
#     same line from identity block 

    X = identity_block(X, 3, [ 64, 64,256])
#     same line from identity block

#     stage 3
    X = convolutional_block(X,f =3 , filters = [128,128,512],s=2)
    X = identity_block(X,3,[128,128,512])
    X = identity_block(X,3,[128,128,512])
    X = identity_block(X,3,[128,128,512])
    
#     stage 4
    X = convolutional_block(X,f = 3 , filters = [ 256, 256, 1024], s = 2 )
    X = identity_block(X,3,[256,256,1024])
    X = identity_block(X,3,[256,256,1024])
    X = identity_block(X,3,[256,256,1024])
    X = identity_block(X,3,[256,256,1024])
    X = identity_block(X,3,[256,256,1024])
    
#     stage 5 
    X = convolutional_block(X, f = 3 ,filters = [512,512,2048], s = 2 )
#     X = identity_block(X,3 , [512,512,1024])
#     X = identity_block(X,3 , [512,512,1024])
    
#     AVGPOOL
    X =  AveragePooling2D((2,2),name='avg_pool')(X)
    
#     achitech complete

#     output
    X = Flatten()(X)
    X = Dense (classes, activation = 'softmax',name = 'fc'+str(classes),kernel_initializer = glorot_uniform(seed = 0))(X)
    
#     Create model
    model = Model(inputs = X_input , outputs =X , name='Resnet50')

        
    
    return model

model = Resnet50(input_shape = (224,224,3), classes=309)
model.compile(optimizer = 'adam', loss='categorical_crossentropy', metrics = ['accuracy'])
model.fit(train_x,train_y,epochs= 10 , batch_size =32)

标签： pythontensorflowmachine-learningdeep-learningresnet

python - ResNet50：TypeError：无法转换类型的对象张量

问题描述

解决方案

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