class_docstr: Unit test class for testing scikit-learn converter.
class_docstr_tok: ["Unit","test","class","for","testing","scikit-learn","converter","."]
docstr: Test the conversion for a Dense + Dropout
docstr_tok: ["Test","the","conversion","for","a","Dense","+","Dropout"]
code: def test_dropout(self):
      """
      Test the conversion for a Dense + Dropout
      """
      from keras.layers import Dense, Dropout
  
      # Create a simple Keras model
      model = Sequential()
      model.add(Dense(32, input_shape=(16,)))
      model.add(Dropout(0.5))
      model.add(Dense(32, input_shape=(16,)))
  
      input_names = ["input"]
      output_names = ["output"]
      spec = keras.convert(model, input_names, output_names).get_spec()
      self.assertIsNotNone(spec)
  
      # Test the model class
      self.assertIsNotNone(spec.description)
      self.assertTrue(spec.HasField("neuralNetwork"))
  
      # Test the inputs and outputs
      self.assertEquals(len(spec.description.input), len(input_names))
      self.assertEqual(
          sorted(input_names),
          sorted(map(lambda x: x.name, spec.description.input)),
      )
      self.assertEquals(len(spec.description.output), len(output_names))
      self.assertEqual(
          sorted(output_names),
          sorted(map(lambda x: x.name, spec.description.output)),
      )
  
      # Test the layer parameters.
      layers = spec.neuralNetwork.layers
      layer_0 = layers[0]
      self.assertIsNotNone(layer_0.innerProduct)
      self.assertEquals(len(layers), 2)
  
imports: import unittest
  from coremltools._deps import _HAS_KERAS2_TF
  from coremltools.proto import Model_pb2
  from coremltools.proto import FeatureTypes_pb2
  from coremltools.proto import NeuralNetwork_pb2
  import pytest
label_desc: Use image node_id 5 for calling the KerasSingleLayerTest obj's underlying member method code with example usage: obj.test_dropout() without return types