图形已断开连接:无法获得层“ x”上的张量“ x”张量的值顺利访问了以下先前的层:[]

如何解决图形已断开连接:无法获得层“ x”上的张量“ x”张量的值顺利访问了以下先前的层:[]

我正在为每个用例使用一些自定义网络框构建一个小型网络,如下所示:

def top_block(dropout = None,training = None):
    
    # scaled input
    input_1 = tf.keras.Input(shape=(1,15),dtype='float32')
    input_2 = tf.keras.Input(shape=(1,dtype='float32')
    
    if dropout:
        layer_one = tf.keras.layers.Dropout(rate = dropout)(input_1,training = training)
        layer_two = tf.keras.layers.Dropout(rate = dropout)(input_2,training = training)
        return [layer_one,layer_two]
    return [input_1,input_2]
    

def bottom_layer(input_layers):
    
    data = tf.reduce_mean(input_layers,0)
    cls_layer     = tf.keras.layers.Dense(1,kernel_initializer = keras.initializers.glorot_uniform(seed=200),activation = 'sigmoid')(data)
    
    model         = tf.keras.Model([input_layers[0],input_layers[1]],cls_layer,name = 'model_1')
    model.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics=['accuracy'])
    model.summary()
    return model

如果我尝试不丢失就访问该网络,则工作正常:

top_          = top_block()
model         = bottom_layer(top_ )

但是如果我通过辍学进行访问,则会出现错误:

top_          = top_block(dropout = 0.2,training = True)
model         = bottom_layer(top_ )

ValueError:图表已断开连接:无法在“ input_72”层获得张量Tensor(“ input_72:0”,shape =(None,1,15),dtype = float32)的值。可以顺利访问以下先前的图层:[]

  1. 如何访问带有辍学层的模型?
  2. 如何在评估期间禁用training = False?我需要加载完整的模型权重还是旧模型权重?

谢谢!

解决方法

我刚刚意识到我的输入来自中间层(辍学层),它应该直接来自输入层:

def top_block():
    
    # scaled input
    input_1 = tf.keras.Input(shape=(1,15),dtype='float32')
    input_2 = tf.keras.Input(shape=(1,dtype='float32')
    
    return [input_1,input_2]
    
def apply_dropout(layers_data,dropout_val,training):
    
    layer_one = tf.keras.layers.Dropout(rate = dropout_val)(layers_data[0],training = training)
    layer_two = tf.keras.layers.Dropout(rate = dropout_val)(layers_data[1],training = training)
    return [layer_one,layer_two]

def bottom_layer(input_layers,data):
    
    data = tf.reduce_mean(data,0)
    cls_layer     = tf.keras.layers.Dense(1,kernel_initializer = keras.initializers.glorot_uniform(seed=200),activation = 'sigmoid')(data)
    
    model         = tf.keras.Model(input_layers,cls_layer,name = 'model_1')
    model.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics=['accuracy'])
    model.summary()
    return model

现在正在工作

top_          = top_block()
dropout_      = apply_dropout(top_,0.2,True)
model         = bottom_layer(top_,dropout_)

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