Question

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=(1,15), 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：[]

如何访问带有辍学层的模型？
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！

Answer 1

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=(1,15), 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_)

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：[]

1 个答案: