如何将一个模型的输出作为输入提供给另一个模型，将输入张量传递给模型必须来自`keras.layers.Input`

Question

def profiler(model, starting_layer_name, test_input):
# print(starting_layer_name)
layer_input = layers.Input( batch_shape=model.get_layer( 
starting_layer_name ).get_input_shape_at( 0 ) )
print( layer_input )

x = layer_input
for layer in model.layers:
  x = layer( x )
  intermediate_model = keras.Model( layer_input, x )
  start = time.time()
  intermediate_model = intermediate_model.predict( test_input )
  end = time.time() - start

def split(model, input):
 # this is the split point, i.e. the starting layer in our sub-model
 starting_layer_name = input
 new_output = input

 new_input = layers.Input( batch_shape=model.get_layer( starting_layer_name ).get_input_shape_at( 0 ) )

layer_outputs = {}

def get_output_of_layer(layer):

 if layer.name in layer_outputs:
   return layer_outputs[layer.name]

 if layer.name == starting_layer_name:
   out = layer( new_input )
   layer_outputs[layer.name] = out
  return out
 prev_layers = []

 for node in layer._inbound_nodes:
   prev_layers.extend( node.inbound_layers )

 # get the output of connected layers
 pl_outs = []

 for pl in prev_layers:
   pl_outs.extend( [get_output_of_layer( pl )] )

 out = layer( pl_outs[0] if len( pl_outs ) == 1 else pl_outs )
 layer_outputs[layer.name] = out
 return out

 if starting_layer_name == 'input_1':
  new_output = get_output_of_layer( model.layers[-139] )
 else:
  new_output = get_output_of_layer( model.layers[-131] )


 if starting_layer_name == 'input_1':
  model = models.Model( new_input, new_output )
  profiler( model, starting_layer_name, processed_image )


 elif starting_layer_name == 'block_1_project_BN':
  model = models.Model( starting_layer_name, new_output )
  profiler( model, starting_layer_name, processed_image )



split( model, 'input_1' )
split( model, 'block_1_project_BN' )

我需要遍历一个预训练的非序列模型，并在模型中找到分支的地方进行拆分，并将其划分为子模型。然后，我需要从第一个模型到第二个模型以及从第二个模型到第三个模型的模型预测结果的输出。

例如模型A（最后一层预测结果的输出）->模型B

当上面的代码在下面编译时会引发错误

  Input tensors to a Model must come from `keras.layers.Input`. Received: 
  block_1_project_BN (missing previous layer metadata).

Answer 1

请在how to use block i.e. output of a layer to other 的自定义层部分查看此示例 礼貌 - tensorflow 教程。

我们创建了一个类，它本身就是一个 resnet 模型块，最后你可以看到识别功能也被添加了。

Models: Composing layers

Many interesting layer-like things in machine learning models are implemented by composing existing layers. For example, each residual block in a resnet is a composition of convolutions, batch normalizations, and a shortcut. Layers can be nested inside other layers.

Typically you inherit from keras.Model when you need the model methods like: Model.fit,Model.evaluate, and Model.save (see Custom Keras layers and models for details).

One other feature provided by keras.Model (instead of keras.layers.Layer) is that in addition to tracking variables, a keras.Model also tracks its internal layers, making them easier to inspect.

class ResnetIdentityBlock(tf.keras.Model):
  def __init__(self, kernel_size, filters):
    super(ResnetIdentityBlock, self).__init__(name='')
    filters1, filters2, filters3 = filters

    self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1))
    self.bn2a = tf.keras.layers.BatchNormalization()

    self.conv2b = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same')
    self.bn2b = tf.keras.layers.BatchNormalization()

    self.conv2c = tf.keras.layers.Conv2D(filters3, (1, 1))
    self.bn2c = tf.keras.layers.BatchNormalization()

  def call(self, input_tensor, training=False):
    x = self.conv2a(input_tensor)
    x = self.bn2a(x, training=training)
    x = tf.nn.relu(x)

    x = self.conv2b(x)
    x = self.bn2b(x, training=training)
    x = tf.nn.relu(x)

    x = self.conv2c(x)
    x = self.bn2c(x, training=training)

    x += input_tensor
    return tf.nn.relu(x)

object of resnet class
block = ResnetIdentityBlock(1, [1, 2, 3])

def chain_blocks(input):
    x1 = block(input)
    x2 = block(x1)
    .... 
    ....
    return xn

so on you can bind up the resnet flow in sequential way within a function., moreover if you want to add another layer after a block you can do that, just make sure output shape of block should be same as input shape of next layer.

如果您需要更多信息，请告诉我。