ResNet50：TypeError：无法转换类型的 object<class 'tensorflow.python.framework.sparse_tensor.sparsetensor'> 张量</class>

Question

Trying to implement resnet 50 over poly-u dataset. what i am trying to do over here is that i want to make a model which classifies the palm print of individuals into seperate identities, i found that resnet is working best for this kind of problems cant seems to get over this error tried many solutions online but cant resolve for some reason:

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.

identity block

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 

convolution block

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 architecture

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)

Answer 1

Looking at your resnet50 it seems your missing the last two identity blocks of stage 5

X = convolutional_block(X, f = 3 ,filters = [512,512,2048], s = 2 )
X = identity_block(X,3,[512,512,2048]]) # this part seems missing
X = identity_block(X,3,[512,512,2048]])

Btw you can just use keras applications for a shortcut

import tensorflow as tf
tf.keras.applications.ResNet50(include_top=True, classes=200, weights=None)