bstriner / dense_tensor Goto Github PK

Hi.
When installing the package, I confronted with the following error.
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/pip-4l69r1fk-build/setup.py'

which means that there is no setup.py file to install package. Could you please add it?

Just a heads up that I haven't tested on Keras-2 yet. Please comment here if you run into issues. Will try to update over the summer.

Cheers

https://arxiv.org/abs/1704.08362

They don't call it a Neural Tensor Network, they just call them 2nd order neurons.

Hello, I have some question about the formula in your program

Dense Tensor Layer: f_i = a( xV_ix^T + W_ix^T + b_i)

Your formula is different of socher's, What is x represent for in above formula？

In socher's paper, The formula should be f_i = a( e_1V_ie_2^T + W_i*(e_1,e_2)^T + b_i).

Hi,
Could you please let me know whether or not it is compatible with keras 2.0.8 as there are some API change in the latest version of keras.

Hi,

I just ran the "examples/example_tensor.py" and got an error
ValueError: Multiple target dimensions are not supported. Expected: None, int, (int, int), Provided: [[1], [1]]

I use Kera 2.2.2 and tensorflow 1.9.0.

Could you help me to check this problem?

# example code
from keras.layers import Input
from keras.models import Model
from keras.optimizers import Adam

from dense_tensor import DenseTensor, simple_tensor_factorization
from dense_tensor.example_utils import experiment
from dense_tensor.utils import l1l2


def tensor_model(input_dim=28 * 28, output_dim=10, reg=lambda: l1l2(1e-6, 1e-6)):
    """
    One layer of a DenseTensor
    """
    _x = Input(shape=(input_dim,))
    factorization = simple_tensor_factorization(tensor_regularizer=reg())
    y = DenseTensor(units=output_dim,
                    activation='softmax',
                    kernel_regularizer=reg(),
                    factorization=factorization)
    _y = y(_x)
    m = Model(_x, _y)
    m.compile(Adam(1e-3, decay=1e-4), loss='categorical_crossentropy', metrics=["accuracy"])
    return m


if __name__ == "__main__":
    path = "output/dense_tensor"
    model = tensor_model()
    experiment(path, model)