def test_DCN(embedding_size, cross_num, hidden_size, sparse_feature_num):
model_name = "DCN"
sample_size = 64
feature_dim_dict = {"sparse": {}, 'dense': []}
for name, num in zip(["sparse", "dense"],
[sparse_feature_num, sparse_feature_num]):
if name == "sparse":
for i in range(num):
feature_dim_dict[name][name + '_' +
str(i)] = np.random.randint(1, 10)
else:
for i in range(num):
feature_dim_dict[name].append(name + '_' + str(i))
sparse_input = [
np.random.randint(0, dim, sample_size)
for dim in feature_dim_dict['sparse'].values()
]
dense_input = [
np.random.random(sample_size) for name in feature_dim_dict['dense']
]
y = np.random.randint(0, 2, sample_size)
x = sparse_input + dense_input
model = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, batch_size=100, epochs=1, validation_split=0.5)
print(model_name + " test train valid pass!")
model.save_weights(model_name + '_weights.h5')
model.load_weights(model_name + '_weights.h5')
print(model_name + " test save load weight pass!")
save_model(model, model_name + '.h5')
model = load_model(model_name + '.h5', custom_objects)
print(model_name + " test save load model pass!")
print(model_name + " test pass!")
def test_DCN(embedding_size, cross_num, hidden_size):
name = "DCN"
sample_size = 64
feature_dim_dict = {
'sparse': {
'sparse_1': 2,
'sparse_2': 5,
'sparse_3': 10
},
'dense': ['dense_1', 'dense_2', 'dense_3']
}
sparse_input = [
np.random.randint(0, dim, sample_size)
for dim in feature_dim_dict['sparse'].values()
]
dense_input = [
np.random.random(sample_size) for name in feature_dim_dict['dense']
]
y = np.random.randint(0, 2, sample_size)
x = sparse_input + dense_input
model = DCN(
feature_dim_dict,
embedding_size=embedding_size,
cross_num=cross_num,
hidden_size=hidden_size,
keep_prob=0.5,
)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
model.fit(x, y, batch_size=100, epochs=1, validation_split=0.5)
print(name + " test train valid pass!")
model.save_weights(name + '_weights.h5')
model.load_weights(name + '_weights.h5')
print(name + " test save load weight pass!")
save_model(model, name + '.h5')
model = load_model(name + '.h5', custom_objects)
print(name + " test save load model pass!")
print(name + " test pass!")
lbe = LabelEncoder()
data[feature] = lbe.fit_transform(data[feature])
# 计算每个特征中的 不同特征值的个数
fixlen_feature_columns = [SparseFeat(feature, data[feature].nunique()) for feature in sparse_features]
linear_feature_columns = fixlen_feature_columns
dnn_feature_columns = fixlen_feature_columns
feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)
print(fixlen_feature_columns)
print(feature_names)
# 将数据集切分成训练集和测试集
train, test = train_test_split(data, test_size=0.2)
train_model_input = {name:train[name].values for name in feature_names}
test_model_input = {name:test[name].values for name in feature_names}
# 使用DCN进行训练
#model = DCN(linear_feature_columns, dnn_feature_columns, task='regression')
model = DCN(linear_feature_columns, dnn_feature_columns, task='binary')
model.compile("adam", "binary_crossentropy", metrics=['binary_crossentropy'], )
history = model.fit(train_model_input, train[target].values, batch_size=256, epochs=1, verbose=True, validation_split=0.2, )
# 使用DCN进行预测
pred_ans = model.predict(test_model_input, batch_size=256)
# 输出RMSE或MSE
mse = round(mean_squared_error(test[target].values, pred_ans), 4)
rmse = mse ** 0.5
print("test RMSE", rmse)
# 输出LogLoss
from sklearn.metrics import log_loss
score = log_loss(test[target].values, pred_ans)
print("LogLoss", score)
