rajarsheem / libsdae-autoencoder-tensorflow Goto Github PK

Hi,
How to save and restore the model ?

Hi, Rajarshee Mitra!
Thank you very much for your code! But when I tried running your code on my computer, I always got the error shown below:

'''
InternalError: Blas GEMM launch failed : a.shape=(100, 784), b.shape=(784, 200), m=100, n=200, k=784
[[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_arg_x_0_0/_7, Variable/read)]]
'''
where 100 is batch_size, 784 and 200 are dimensions.

Can you offer me some help!
Thanks a lot!
Phil

Hi @rajarsheem, just got across a failure due to l not being defined in this line.

Context:

Hi,

What do the two files inside data/ represent? I found no description. Thanks.

Gordon

Hi Rajarshee Mitra,

I have question regarding your code. I am confused how your code construct the connections between layers. Specifically, in line 63 of stacked_autoencoder.py, you called the run function for different layers . it seems for me that you construct n different auto-encoders instead of one stacked with n hidden layers.

Best regards,
Mohammad

utils seem to be missing

How to decode the transform result, and calculate the similarity between the decoded one with the original matrix ?

Nice library.
How did you evaluate the goodness of your feature representation? Is the global loss the general reconstruction?
So for example, the 2nd DAE gets the encoded version of the image, so is the loss function of the 2nd DAE the followin: LOSS(decoded(noisy encoded input), decoded(encoded output))
This way the network doesn't loose seight of the global goal: Finding an optimal feature representation, right?

What I did was:

1. Train first DAE, get encoded versions of the training data
2. Use the encoded version to train another DAE with LOSS(noisy encoded, encoded)
   I think it was my mistake that I don't consider the completely reconstructed image as the loss..

Is that right?

I have just started learning denoising autoencoders but I want to import dataset of images than mnist what can be done to do so . I have tried giving path name and assigning it to a variable but im getting a type error.

The mini batch function may generate same sample over
There is no removal already used indices

Also issue with mask noise

Following error:

File "algorithms/deepautoencoder/stacked_autoencoder.py", line 42, in add_noise
    frac = float(self.noise.split('-')[1])

Hi,

I'm playing around with this code for a research project and everything works fine with mean-squared-error, however as soon as I switch to cross-entropy (which I really want), it does not converge and loss gets bigger over time... I tried numerous parameters but nothing seems to work. I'm using MNIST with the following model.

model = StackedAutoEncoder(
    dims=[100],
    activations=['softmax'], 
    noise='gaussian', 
    epoch=[1000],
    loss='cross-entropy',
    lr=0.005,
    batch_size=100,
    print_step=100
)

Do you know why this isn't working?

Thanks!