# for feat in dense_features]
dnn_feature_columns = sparse_feature_columns + dense_feature_columns
linear_feature_columns = sparse_feature_columns + dense_feature_columns
feature_names = get_fixlen_feature_names(linear_feature_columns +
dnn_feature_columns)
print(feature_names)
train_model_input = [train[name] for name in feature_names]
test_model_input = [test[name] for name in feature_names]
print('PPPP')
print(linear_feature_columns, dnn_feature_columns)
model = xDeepFM_MTL(linear_feature_columns, dnn_feature_columns)
model.compile("adagrad",
loss={
'finish': "binary_crossentropy",
'like': "binary_crossentropy"
},
loss_weights=loss_weights)
print(train_labels)
history = model.fit(train_model_input,
train_labels,
batch_size=4096,
epochs=2,
verbose=1,
validation_split=0.1)
for feat in sparse_features]
dense_feature_list = [SingleFeat(feat, 0)
for feat in dense_features]
train = data.iloc[:train_size]
test = data.iloc[train_size:]
train_model_input = [train[feat.name].values for feat in sparse_feature_list] + \
[train[feat.name].values for feat in dense_feature_list]
test_model_input = [test[feat.name].values for feat in sparse_feature_list] + \
[test[feat.name].values for feat in dense_feature_list]
train_labels = [train[target[0]].values, train[target[1]].values]
test_labels = [test[target[0]].values, test[target[1]].values]
model = xDeepFM_MTL({"sparse": sparse_feature_list,
"dense": dense_feature_list})
model.compile("adagrad", "binary_crossentropy", loss_weights=loss_weights, metrics=[auc])
if ONLINE_FLAG:
history = model.fit(train_model_input, train_labels,
batch_size=4096, epochs=1, verbose=1)
pred_ans = model.predict(test_model_input, batch_size=2**14)
else:
history = model.fit(train_model_input, train_labels,
batch_size=4096, epochs=1, verbose=1, validation_data=(test_model_input, test_labels))
if ONLINE_FLAG:
result = test_data[['uid', 'item_id', 'finish', 'like']].copy()
result.rename(columns={'finish': 'finish_probability',
'like': 'like_probability'}, inplace=True)
sequence_feature = [VarLenFeat('audio', 128, 128, 'max')]
train = data.iloc[:train_size]
test = data.iloc[train_size:]
train_model_input = [train[feat.name].values for feat in sparse_feature_list] + \
[train[feat.name].values for feat in dense_feature_list] + [audio_data[:train_size]]
test_model_input = [test[feat.name].values for feat in sparse_feature_list] + \
[test[feat.name].values for feat in dense_feature_list] + [audio_data[train_size:]]
train_labels = [train[target[0]].values, train[target[1]].values]
test_labels = [test[target[0]].values, test[target[1]].values]
model = xDeepFM_MTL({
"sparse": sparse_feature_list,
"dense": dense_feature_list,
"sequence": sequence_feature
})
model.compile(
"adagrad",
"binary_crossentropy",
loss_weights=loss_weights,
)
if ONLINE_FLAG:
history = model.fit(train_model_input,
train_labels,
batch_size=4096,
epochs=1,
verbose=1)
pred_ans = model.predict(test_model_input, batch_size=2**14)
dnn_feature_columns = fixlen_feature_columns
linear_feature_columns = fixlen_feature_columns
feature_names = get_feature_names(linear_feature_columns +
dnn_feature_columns)
train = data.iloc[:train_size]
test = data.iloc[train_size:]
train_model_input = {name: train[name] for name in feature_names}
test_model_input = {name: test[name] for name in feature_names}
train_labels = [train[target[0]].values, train[target[1]].values]
test_labels = [test[target[0]].values, test[target[1]].values]
model = xDeepFM_MTL(linear_feature_columns,
dnn_feature_columns,
dnn_hidden_units=(128, 128))
# model = xDeepFM_MTL({"sparse": sparse_feature_list,
# "dense": dense_feature_list})
def auc(y_true, y_pred):
auc = tf.metrics.auc(y_true, y_pred)[1]
tf.keras.backend.get_session().run(tf.local_variables_initializer())
return auc
model.compile(
"adagrad",
"binary_crossentropy",
metrics=['binary_crossentropy', auc],
[test[feat.name].values for feat in dense_feature_list]
train_labels = [train[target[0]].values, train[target[1]].values]
test_labels = [test[target[0]].values, test[target[1]].values]
embedding_size = 8
hidden_size = (256, 256)
if VERSION == 3:
embedding_size = 1
if VERSION == 4:
embedding_size = 1
hidden_size = (64, 64)
model = xDeepFM_MTL(
{
"sparse": sparse_feature_list,
"dense": dense_feature_list
},
embedding_size=embedding_size,
hidden_size=hidden_size)
model.compile("adagrad",
loss='binary_crossentropy',
loss_weights=loss_weights,
metrics=[auroc])
my_callbacks = [
EarlyStopping(monitor='loss',
min_delta=1e-2,
patience=10,
verbose=1,
mode='min')
]