def test_megnet(self):
units_v = [13, 14, 16]
units_e = [16, 16, 17]
units_u = [13, 14, 18]
layer = MEGNetLayer(units_v, units_e, units_u)
out = layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out], [units_v[-1], units_e[-1], units_u[-1]])
new_layer = MEGNetLayer.from_config(layer.get_config())
out2 = new_layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out2], [units_v[-1], units_e[-1], units_u[-1]])
int32 = 'int32'
x1 = np.random.rand(1, 5, 10)
x2 = np.random.rand(1, 6, 5)
x3 = np.random.rand(1, 2, 20)
x4 = np.array([0, 1, 2, 3, 3, 4]).reshape([1, -1])
x5 = np.array([1, 0, 3, 2, 4, 3]).reshape([1, -1])
x6 = np.array([[0, 0, 1, 1, 1]])
x7 = np.array([[0, 0, 1, 1, 1, 1]])
x1_ = Input(shape=(None, 10))
x2_ = Input(shape=(None, 5))
x3_ = Input(shape=(None, 20))
x4_ = Input(shape=(None,), dtype=int32)
x5_ = Input(shape=(None,), dtype=int32)
x6_ = Input(shape=(None,), dtype=int32)
x7_ = Input(shape=(None,), dtype=int32)
out = MEGNetLayer([10, 5], [20, 4], [30, 3])(
[x1_, x2_, x3_, x4_, x5_, x6_, x7_])
model = Model(inputs=[x1_, x2_, x3_, x4_, x5_, x6_, x7_], outputs=out)
model.compile('adam', 'mse')
ans = model.predict([x1, x2, x3, x4, x5, x6, x7])
self.assertEqual(ans[0].shape, (1, 5, 5))
def test_megnet(self):
units_v = [13, 14, 16]
units_e = [16, 16, 17]
units_u = [13, 14, 18]
layer = MEGNetLayer(units_v, units_e, units_u)
out = layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out],
[units_v[-1], units_e[-1], units_u[-1]])
new_layer = MEGNetLayer.from_config(layer.get_config())
out2 = new_layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out2],
[units_v[-1], units_e[-1], units_u[-1]])
