Siamese Network Using Rstudio Keras
I am trying to implement a Siamese network using the Rstudio Keras package. The network I am trying to implement is the same network as in this post .
So, basically, I am porting the code to R and using the Keras Rstudio implementation. So far, my code looks like this:
library(keras)
inputShape <- c(105, 105, 1)
leftInput <- layer_input(inputShape)
rightInput <- layer_input(inputShape)
model<- keras_model_sequential()
model %>%
layer_conv_2d(filter=64,
kernel_size=c(10,10),
activation = "relu",
input_shape=inputShape,
kernel_initializer = initializer_random_normal(0, 1e-2),
kernel_regularizer = regularizer_l2(2e-4)) %>%
layer_max_pooling_2d() %>%
layer_conv_2d(filter=128,
kernel_size=c(7,7),
activation = "relu",
kernel_initializer = initializer_random_normal(0, 1e-2),
kernel_regularizer = regularizer_l2(2e-4),
bias_initializer = initializer_random_normal(0.5, 1e-2)) %>%
layer_max_pooling_2d() %>%
layer_conv_2d(filter=128,
kernel_size=c(4,4),
activation = "relu",
kernel_initializer = initializer_random_normal(0, 1e-2),
kernel_regularizer = regularizer_l2(2e-4),
bias_initializer = initializer_random_normal(0.5, 1e-2)) %>%
layer_max_pooling_2d() %>%
layer_conv_2d(filter=256,
kernel_size=c(4,4),
activation = "relu",
kernel_initializer = initializer_random_normal(0, 1e-2),
kernel_regularizer = regularizer_l2(2e-4),
bias_initializer = initializer_random_normal(0.5, 1e-2)) %>%
layer_flatten() %>%
layer_dense(4096,
activation = "sigmoid",
kernel_initializer = initializer_random_normal(0, 1e-2),
kernel_regularizer = regularizer_l2(1e-3),
bias_initializer = initializer_random_normal(0.5, 1e-2))
encoded_left <- leftInput %>% model
encoded_right <- rightInput %>% model
However, when running the last two lines, the following error appears:
Error in py_call_impl(callable, dots$args, dots$keywords) :
AttributeError: 'Model' object has no attribute '_losses'
Detailed traceback:
File "/home/rstudio/.virtualenvs/r-tensorflow/lib/python2.7/site-packages/tensorflow/contrib/keras/python/keras/engine/topology.py", line 432, in __call__
output = super(Layer, self).__call__(inputs, **kwargs)
File "/home/rstudio/.virtualenvs/r-tensorflow/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 441, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/home/rstudio/.virtualenvs/r-tensorflow/lib/python2.7/site-packages/tensorflow/contrib/keras/python/keras/models.py", line 560, in call
return self.model.call(inputs, mask)
File "/home/rstudio/.virtualenvs/r-tensorflow/lib/python2.7/site-packages/tensorflow/contrib/keras/python/keras/engine/topology.py", line 1743, in call
output_tensors, _, _ = self.run_internal_graph(inputs, masks)
File "/home/rstudio/.virtualenvs/r-tensorflow/lib/python2.7/site-packages/tensorflow/contrib/keras/python
I've looked at similar implementations and questions all over StackOverflow, but I couldn't find a solution. I think I might be missing something really obvious.
Any ideas how to solve this?
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As Daniel Falbel pointed out in his comment, the solution was updating the R-keras package and then updating the tensorflow installation.
However, the tensorflow package in R did not install the latest version 1.3 of tensorflow (it was reinstalling version 1.2).
To fix this issue, the correct version URL can be provided to the install_tensorflow function. URLs for different implementations can be found here . In this case, I used Linux. Running this command should fix the problem for anyone else facing the same problem:
library(tensorflow)
install_tensorflow(package_url = "https://pypi.python.org/packages/b8/d6/af3d52dd52150ec4a6ceb7788bfeb2f62ecb6aa2d1172211c4db39b349a2/tensorflow-1.3.0rc0-cp27-cp27mu-manylinux1_x86_64.whl#md5=1cf77a2360ae2e38dd3578618eacc03b")
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I tried GAN and got this error. When I use the same code in the CPU version it is ok for tensorflow, but it was not in the GPU version.
I found this problem was caused by using a parameter in the GPU version. You can delete the parameter and try again. I don't know why this solved the problem. I think it might be a bug when handling reusable models. kernel_regularizer
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