def test_unidirectional_lstm_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),
numpy.random.rand(2, 2),
numpy.random.rand(2, 2),
numpy.random.rand(2, 2)
]
W_x = [
numpy.random.rand(2, 8),
numpy.random.rand(2, 8),
numpy.random.rand(2, 8),
numpy.random.rand(2, 8)
]
b = [
numpy.random.rand(2, 1),
numpy.random.rand(2, 1),
numpy.random.rand(2, 1),
numpy.random.rand(2, 1)
]
p = [
numpy.zeros(shape=(2, 1)),
numpy.zeros(shape=(2, 1)),
numpy.zeros(shape=(2, 1))
]
builder.add_unilstm(name='LSTM',
W_h=W_h,
W_x=W_x,
b=b,
hidden_size=2,
input_size=8,
input_names=['input'],
output_names=['output'],
inner_activation='SIGMOID',
cell_state_update_activation='TANH',
output_activation='TANH',
peep=p,
output_all=False,
forget_bias=False,
coupled_input_forget_gate=False,
cell_clip_threshold=10000,
reverse_input=False)
context = ConvertContext()
node = UniDirectionalLSTMLayerConverter.convert(
context, builder.spec.neuralNetwork.layers[0],
['input', 'h_init', 'c_init'], ['output', 'h', 'c'])
self.assertTrue(node is not None)
def test_unidirectional_lstm_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),
numpy.random.rand(2, 2),
numpy.random.rand(2, 2),
numpy.random.rand(2, 2)
]
W_x = [
numpy.random.rand(2, 8),
numpy.random.rand(2, 8),
numpy.random.rand(2, 8),
numpy.random.rand(2, 8)
]
b = [
numpy.random.rand(2, 1),
numpy.random.rand(2, 1),
numpy.random.rand(2, 1),
numpy.random.rand(2, 1)
]
p = [
numpy.zeros(shape=(2, 1)),
numpy.zeros(shape=(2, 1)),
numpy.zeros(shape=(2, 1))
]
builder.add_unilstm(name='LSTM',
W_h=W_h,
W_x=W_x,
b=b,
hidden_size=2,
input_size=8,
input_names=['input'],
output_names=['output'],
inner_activation='SIGMOID',
cell_state_update_activation='TANH',
output_activation='TANH',
peep=p,
output_all=False,
forget_bias=False,
coupled_input_forget_gate=False,
cell_clip_threshold=10000,
reverse_input=False)
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
