def test_sequential_multi_layer():
model = keras.models.Sequential()
model.add(
keras.layers.Dense(4,
use_bias=False,
activation=None,
input_shape=(2, )))
model.add(keras.layers.Dense(8, use_bias=False, activation=None))
model.build()
graph = KerasConverter(batch_size=1).convert(model)
assert_equal(len(graph.inputs), 1)
ops = traverse.listup_operators(graph)
assert_equal(len(ops), 2)
assert_equal(type(ops[0]), Linear)
assert_equal(type(ops[1]), Linear)
assert_equal(len(graph.outputs), 1)
def test_residual():
x = keras.layers.Input(shape=(4, ))
h1 = keras.layers.Dense(8, use_bias=False, activation=None)(x)
h21 = keras.layers.Dense(4, use_bias=False, activation=None)(h1)
h22 = keras.layers.Dense(4, use_bias=False, activation=None)(h1)
y = keras.layers.add([h21, h22])
model = keras.models.Model([x], [y])
model.build(input_shape=(1, 4))
graph = KerasConverter(batch_size=1).convert(model)
assert_equal(len(graph.inputs), 1)
ops = traverse.listup_operators(graph)
assert_equal(len(ops), 4)
assert_equal(type(ops[0]), Linear)
assert_equal(type(ops[1]), Linear)
assert_equal(type(ops[2]), Linear)
assert_equal(type(ops[3]), ElementwiseAdd)
assert_equal(len(graph.outputs), 1)
