drawbridge/keras-mmoe

A TensorFlow Keras implementation of "Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts" (KDD 2018)

keras-mmoe's Issues

Running example got topological sort failed with message: The graph couldn't be sorted in topological order.

Train on 199523 samples, validate on 49881 samples
Epoch 1/100
2019-12-16 19:35:43.742576: I tensorflow/stream_executor/dso_loader.cc:153] successfully opened CUDA library libcublas.so.10 locally
199328/199523 [============================>.] - ETA: 0s - loss: 0.5737 - income_loss: 0.3506 - marital_loss: 0.2231 - income_acc: 0.9344 - marital_acc: 0.92562019-12-16 19:
36:23.028460: E tensorflow/core/grappler/optimizers/dependency_optimizer.cc:704] Iteration = 0, topological sort failed with message: The graph couldn't be sorted in topolog
ical order.
2019-12-16 19:36:23.029628: E tensorflow/core/grappler/optimizers/dependency_optimizer.cc:704] Iteration = 1, topological sort failed with message: The graph couldn't be sor
ted in topological order.
2019-12-16 19:36:23.036058: E tensorflow/core/grappler/optimizers/dependency_optimizer.cc:704] Iteration = 0, topological sort failed with message: The graph couldn't be sor
ted in topological order.
2019-12-16 19:36:23.037032: E tensorflow/core/grappler/optimizers/dependency_optimizer.cc:704] Iteration = 1, topological sort failed with message: The graph couldn't be sor
ted in topological order.

I run both examples will get above warning in the first few epochs. It will cause some errors when run model inference. How to solve it? It seems a bug from TF.

pandas 1.3.1 need python version 3.7+?

I can't install pandas1.3.1 in python 3.6.13, for which I searched for a reason saying I need to upgrade to python 3.7+. Is python 3.7 OK?

Process finished with exit code -1073741819 (0xC0000005)

windows10+python36 +pycharm
how to solve? thx~

some questions in run model about TypeError!

when I run the example census_income_demo.py, a problem occured,I dont know how to solve it,can you give some advices? thanks.

Training data shape = (10000, 100)
Validation data shape = (1000, 100)
Test data shape = (1000, 100)
Traceback (most recent call last):
File "E:\softWare\conda\envs\AppKG\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 558, in make_tensor_proto
str_values = [compat.as_bytes(x) for x in proto_values]
File "E:\softWare\conda\envs\AppKG\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 558, in
str_values = [compat.as_bytes(x) for x in proto_values]
File "E:\softWare\conda\envs\AppKG\lib\site-packages\tensorflow\python\util\compat.py", line 65, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got Dimension(100)

During handling of the above exception, another exception occurred:

File "E:\softWare\conda\envs\AppKG\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 562, in make_tensor_proto
"supported type." % (type(values), values))
TypeError: Failed to convert object of type <class 'tuple'> to Tensor. Contents: (Dimension(100), 16, 8). Consider casting elements to a supported type.

Single-task classification

Can we use the MMoE to deal with single-task classification problem?

Some questions about mmoe implementation

keras-mmoe/census_income_demo.py

Line 177 in 09bb267

units=4,

Hi, thx for your code, and i wanna confirm some questions,
both the mmoe and tower layers are single-layer?
and the size of mmoe hidden layer is 4 , the size of tower layer is 2 ?

some questions about the trade off between the loss of two towers

hi,Emin Orhan,it's so nice of you to share the beautiful code,i have some questions about the loss
in synthetic_demo.py
output_layer:[two towers]
model.compile( loss={'y0': 'mean_squared_error', 'y1': 'mean_squared_error'}, optimizer=adam_optimizer, metrics=[metrics.mae] )
does the model fit the two tower separately? i mean the basic method of multiple task is loss = 0.5loss1 + 0.5 loss2.or does the procedure of this implemented by keras

Have you implemented this in pytorch

Hi

I reimplemented your code in pytorch, I found a strange phenomenon, the auc of income is just 0.56 and the auc of marital is 0.96. I checked the detail and don't know why it does not work?

Here is the model code:

class Model(nn.Module):
    def __init__(self,config):
        super(Model, self).__init__()

    # accept_unit = config.field_size*config.embed_size
    accept_unit = config.num_feature
    self.expert_kernels = torch.nn.Parameter(torch.randn(accept_unit, config.units, config.num_experts, device=config.device),
                                             requires_grad=True)
    self.gate_kernels = torch.nn.ParameterList(
        [nn.Parameter(torch.randn(accept_unit, config.num_experts, device=config.device), requires_grad=True) for i in
         range(config.num_tasks)])

    self.expert_kernels_bias = torch.nn.Parameter(torch.randn(config.units, config.num_experts, device=config.device),
                                                  requires_grad=True)
    self.gate_kernels_bias = torch.nn.ParameterList(
        [torch.nn.Parameter(torch.randn(config.num_experts, device=config.device), requires_grad=True) for i in range(config.num_tasks)])

    self.output_layer = nn.ModuleList([nn.Sequential(
        nn.Linear(config.units,config.hidden_units),
        nn.ReLU(),
        nn.Linear(config.hidden_units,unit),
    )
        for unit in config.label_dict
    ])

    self.expert_activation = nn.ReLU()

    # self.embedding_layer = nn.Embedding(config.num_feature,config.embed_size)

def forward(self,x):
    gate_outputs = []
    final_outputs = []
    # xi =x[0]
    # xv = x[1]

    # self.embeddings = self.embedding_layer(xi)
    # feat_value = xv.view(-1,xv.size(1),1)
    #
    # self.embeddings = feat_value * self.embeddings
    # self.embeddings = self.embeddings.view(xv.size(0),-1)


    expert_outputs = torch.einsum("ab,bcd->acd", (x, self.expert_kernels))
    expert_outputs += self.expert_kernels_bias
    expert_outputs = self.expert_activation(expert_outputs)

    for index, gate_kernel in enumerate(self.gate_kernels):
        gate_output = torch.einsum("ab,bc->ac", (x, gate_kernel))
        gate_output += self.gate_kernels_bias[index]
        gate_output = nn.Softmax(dim=-1)(gate_output)
        gate_outputs.append(gate_output)

    for gate_output in gate_outputs:
        expanded_gate_output = torch.unsqueeze(gate_output, 1)
        weighted_expert_output = expert_outputs * expanded_gate_output.expand_as(expert_outputs)
        final_outputs.append(torch.sum(weighted_expert_output, 2))

    output_layers = []
    for i,output in enumerate(final_outputs):
        output_layers.append(torch.sigmoid(self.output_layer[i](output)))

    return output_layers

numbers of experts?

I didn't find the ablation experiment results of the number of experts in your paper. When I have a total of 9 tasks, how many experts do I need to set?

请问遇到过Auc和Acc在测试集上都是1.0的情况吗？

some questions about model effect

Hi, thanks for your share. Have you ever encountered the following problem？When the average predictive value of one task is 0.74, the value of the other task is as high as 0.92. However, the proportion of positive samples of this task is only about 12.5%. Whether the label column of the previous task (the predicted average value is 0.74) affected the second task?

all gate net share one expert net ?

keras-mmoe/mmoe.py

Line 191 in 2718f56

weighted_expert_output = expert_outputs * K.repeat_elements(expanded_gate_output, self.units, axis=1)

Question about expert implement.

Thanks for your sharing, it's very intelligible.
I have q question about experts implement. In paper, expert are implemented by multi layers(MLP), but I just see only one layer here. Do I have misconceptions about this?
Looking forward to your reply, thank you!

how to save the model as a .pb file

Hi，do you know how to save the model as a Pb file for tensorflow serving？

run synthetic_demo ValueError: Data cardinality is ambiguous

Traceback (most recent call last):
File "./synthetic_demo.py", line 159, in
main()
File "./synthetic_demo.py", line 154, in main
epochs=100
File "/data/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1064, in fit
steps_per_execution=self._steps_per_execution)
File "/data/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 1112, in init
model=model)
File "/data/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 274, in init
_check_data_cardinality(inputs)
File "/data/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 1529, in _check_data_cardinality
raise ValueError(msg)
ValueError: Data cardinality is ambiguous:
x sizes: 10000
y sizes: 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, (line too long truncated)
Make sure all arrays contain the same number of samples.

got an error when run the demo

Training data shape = (10000, 100)
Validation data shape = (1000, 100)
Test data shape = (1000, 100)
Traceback (most recent call last):
File "synthetic_demo.py", line 152, in
main()
File "synthetic_demo.py", line 110, in main
)(input_layer)
File "/home/chenjw/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py", line 603, in call
output = self.call(inputs, **kwargs)
File "/home/chenjw/Project/WTX/mmoe/keras-mmoe-master/mmoe.py", line 175, in call
expert_outputs = K.bias_add(x=expert_outputs, bias=self.expert_bias)
File "/home/chenjw/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 3551, in bias_add
if len(bias_shape) != 1 and len(bias_shape) != ndim(x) - 1:
TypeError: unsupported operand type(s) for -: 'NoneType' and 'int'

How to solve the both regression task and classification task by MMOE model

Hi~

I want to combine regression task and classification task in one model, but I found the regression loss and classification loss are so different, so it's too hard to optimize?

Can you give me some advice to handle this problem?
Thx~~

data preparation

HI, thank you for your sharing, read you codes i have some questuons.
during feature transform, will need to normalize the dense features? Deep networks are said to be sensitive to dense features.
and sparse feature, why use one-hot encoder instead of label encoder?