def test_simple_recurrent_converter(self):
input_dim = (1, 8)
output_dim = (1, 2)
inputs = [('input', datatypes.Array(*input_dim))]
outputs = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(inputs, outputs)
W_h = numpy.random.rand(2, 2)
W_x = numpy.random.rand(2, 8)
b = numpy.random.rand(2, 1)
builder.add_simple_rnn(name='RNN',
W_h=W_h,
W_x=W_x,
b=b,
hidden_size=2,
input_size=8,
input_names=['input', 'h_init'],
output_names=['output', 'h'],
activation='TANH',
output_all=False,
reverse_input=False)
context = ConvertContext()
node = SimpleRecurrentLayerConverter.convert(
context, builder.spec.neuralNetwork.layers[0], ['input'],
['output'])
self.assertTrue(node is not None)
def test_simple_recurrent_converter(self):
input_dim = (1, 8)
output_dim = (1, 2)
inputs = [('input', datatypes.Array(*input_dim))]
outputs = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(inputs, outputs)
W_h = numpy.random.rand(2, 2)
W_x = numpy.random.rand(2, 8)
b = numpy.random.rand(2, 1)
builder.add_simple_rnn(name='RNN', W_h=W_h, W_x=W_x, b=b, hidden_size=2, input_size=8,
input_names=['input', 'h_init'], output_names=['output', 'h'], activation='TANH',
output_all=False, reverse_input=False)
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
