def test_PredictionLayer(activation, use_bias):
with CustomObjectScope({'PredictionLayer': layers.PredictionLayer}):
layer_test(layers.PredictionLayer,
kwargs={
'activation': activation,
'use_bias': use_bias
},
input_shape=(BATCH_SIZE, 1))
def test_MLP(hidden_size, use_bn):
with CustomObjectScope({'MLP': layers.MLP}):
layer_test(layers.MLP,
kwargs={
'hidden_size': hidden_size,
'use_bn': use_bn
},
input_shape=(BATCH_SIZE, EMBEDDING_SIZE))
def test_CrossNet_invalid():
with pytest.raises(ValueError):
with CustomObjectScope({'CrossNet': layers.CrossNet}):
layer_test(layers.CrossNet,
kwargs={
'layer_num': 1,
'l2_reg': 0
},
input_shape=(2, 3, 4))
def test_SequencePoolingLayer(seq_len_max, mode):
with CustomObjectScope(
{'SequencePoolingLayer': sequence.SequencePoolingLayer}):
layer_test(sequence.SequencePoolingLayer,
kwargs={
'seq_len_max': seq_len_max,
'mode': mode
},
input_shape=[(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE),
(BATCH_SIZE, 1)])
def test_AttentionSequencePoolingLayer(weight_normalization):
with CustomObjectScope({
'AttentionSequencePoolingLayer':
sequence.AttentionSequencePoolingLayer
}):
layer_test(sequence.AttentionSequencePoolingLayer,
kwargs={'weight_normalization': weight_normalization},
input_shape=[(BATCH_SIZE, 1, EMBEDDING_SIZE),
(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE),
(BATCH_SIZE, 1)])
def test_LocalActivationUnit(hidden_size, activation):
with CustomObjectScope({'LocalActivationUnit':
layers.LocalActivationUnit}):
layer_test(layers.LocalActivationUnit,
kwargs={
'hidden_size': hidden_size,
'activation': activation
},
input_shape=[(BATCH_SIZE, 1, EMBEDDING_SIZE),
(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)])
def test_CrossNet(
layer_num,
l2_reg,
):
with CustomObjectScope({'CrossNet': layers.CrossNet}):
layer_test(layers.CrossNet,
kwargs={
'layer_num': layer_num,
'l2_reg': l2_reg
},
input_shape=(2, 3))
def test_dice():
with CustomObjectScope({'Dice': activations.Dice}):
layer_test(activations.Dice, kwargs={}, input_shape=(2, 3))
def test_BiInteractionPooling():
with CustomObjectScope(
{'BiInteractionPooling': layers.BiInteractionPooling}):
layer_test(layers.BiInteractionPooling,
kwargs={},
input_shape=(BATCH_SIZE, FIELD_SIZE, EMBEDDING_SIZE))
def test_OutterProductLayer(kernel_type):
with CustomObjectScope({'OutterProductLayer': layers.OutterProductLayer}):
layer_test(layers.OutterProductLayer,
kwargs={'kernel_type': kernel_type},
input_shape=[(BATCH_SIZE, 1, EMBEDDING_SIZE)] * FIELD_SIZE)
def test_InnerProductLayer(reduce_sum):
with CustomObjectScope({'InnerProductLayer': layers.InnerProductLayer}):
layer_test(layers.InnerProductLayer,
kwargs={'reduce_sum': reduce_sum},
input_shape=[(BATCH_SIZE, 1, EMBEDDING_SIZE)] * FIELD_SIZE)
def test_AFMLayer():
with CustomObjectScope({'AFMLayer': layers.AFMLayer}):
layer_test(layers.AFMLayer,
kwargs={},
input_shape=[(BATCH_SIZE, 1, EMBEDDING_SIZE)] * FIELD_SIZE)
def test_FM():
with CustomObjectScope({'FM': layers.FM}):
layer_test(layers.FM,
kwargs={},
input_shape=(BATCH_SIZE, FIELD_SIZE, EMBEDDING_SIZE))
def test_test_PredictionLayer_invalid():
# with pytest.raises(ValueError):
with CustomObjectScope({'PredictionLayer': layers.PredictionLayer}):
layer_test(layers.PredictionLayer,
kwargs={'use_bias': use_bias},
input_shape=(BATCH_SIZE, 2, 1))
