original_docstring: 
          19-layered VGG model implemented in Keras' Functional API trained on Imagenet.
          
docstring: 19-layered VGG model implemented in Keras' Functional API trained on Imagenet.
docstring_tokens: 19 - layered VGG model implemented in Keras ' Functional API trained on Imagenet .
code: def VGG_19_ImageNet(self, nOutput, input_shape):
          """
          19-layered VGG model implemented in Keras' Functional API trained on Imagenet.
          """
          # Define inputs and outputs IDs
          self.ids_inputs = ['input_1']
          self.ids_outputs = ['predictions']
          from keras.applications.vgg19 import VGG19
  
          # Load VGG19 model pre-trained on ImageNet
          self.model = VGG19(weights='imagenet', layers_lr=0.001)
  
          # Recover input layer
          image = self.model.get_layer(self.ids_inputs[0]).output
  
          # Recover last layer kept from original model
          out = self.model.get_layer('fc2').output
          out = Dense(nOutput, name=self.ids_outputs[0], activation='softmax')(out)
  
          self.model = Model(inputs=[image], outputs=[out])
code_tokens: def VGG_19_ImageNet ( self , nOutput , input_shape ) : self . ids_inputs = [ 'input_1' ] self . ids_outputs = [ 'predictions' ] from keras . applications . vgg19 import VGG19 self . model = VGG19 ( weights = 'imagenet' , layers_lr = 0.001 ) image = self . model . get_layer ( self . ids_inputs [ 0 ] ) . output out = self . model . get_layer ( 'fc2' ) . output out = Dense ( nOutput , name = self . ids_outputs [ 0 ] , activation = 'softmax' ) ( out ) self . model = Model ( inputs = [ image ] , outputs = [ out ] )
short_docstring: 19-layered VGG model implemented in Keras' Functional API trained on Imagenet.
short_docstring_tokens: 19 - layered VGG model implemented in Keras ' Functional API trained on Imagenet .
comment: """
          19-layered VGG model implemented in Keras' Functional API trained on Imagenet.
          """$SEP$# Define inputs and outputs IDs$SEP$# Load VGG19 model pre-trained on ImageNet$SEP$# Recover input layer$SEP$# Recover last layer kept from original model