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' )

我需要遍历一个预先训练的非顺序模型，并在该模型中发现分支的地方进行分割，然后将其划分为子模型。然后，我需要第一个模型的模型预测结果的输出，第二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”部分中查看此示例礼貌-tensorflow教程。

我们创建了一个类，该类本身不过是一个resnet模型块，最后，您可以看到还添加了identify函数。

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.

让我知道是否需要其他信息。

如何将一个模型的输出作为输入提供给另一个模型，输入模型的张量必须来自keras.layers.Input。

1 个答案: