def test_CCPM_without_seq(sparse_feature_num, dense_feature_num):
model_name = "CCPM"
sample_size = SAMPLE_SIZE
x, y, feature_dim_dict = get_test_data(
sample_size, sparse_feature_num, dense_feature_num, sequence_feature=())
model = CCPM(feature_dim_dict, conv_kernel_width=(3, 2), conv_filters=(2, 1), hidden_size=[32, ], keep_prob=0.5, )
check_model(model, model_name, x, y)
def test_CCPM_without_seq(sparse_feature_num, dense_feature_num):
if tf.__version__ >= "2.0.0":
return
model_name = "CCPM"
sample_size = SAMPLE_SIZE
x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num,
dense_feature_num=dense_feature_num, sequence_feature=())
model = CCPM(feature_columns, feature_columns, conv_kernel_width=(3, 2), conv_filters=(
2, 1), dnn_hidden_units=[32, ], dnn_dropout=0.5)
check_model(model, model_name, x, y)
def test_CCPM(sparse_feature_num, dense_feature_num):
model_name = "CCPM"
sample_size = 32
x, y, feature_columns = get_test_data(sample_size, sparse_feature_num,
dense_feature_num)
model = CCPM(feature_columns,
feature_columns,
conv_kernel_width=(3, 2),
conv_filters=(2, 1),
dnn_hidden_units=[
32,
],
dnn_dropout=0.5)
check_model(model, model_name, x, y)