ntest | Automatically exported from code.google.com/p/ntest

It has to be like

You are ensure type for RNNs input and hidden state, but it also contain some tensor parameters, weights, biases etc. To make them double, try torch.set_default_tensor_type(t) before RNN object construction, but I personaly would use Float.

try this:

Computing a single-precision square root via double-precision code is going to be inefficient, especially if the hardware provides no native double-precision operations.

The following assumes hardware that conforms to IEEE-754 (2008), except that subnormals are not supported and flushed to zero. Fused-multiply add (FMA) is supported. It further assumes an ISO-C99 compiler that maps float to IEEE-754 binary32, and that maps the hardware's single-precision FMA instruction to the standard math function fmaf().

From a hardware starting approximation for the reciprocal square root with a maximum relative error of 2^-6.75 one can get to a reciprocal square root accurate to 1 single-precision ulp with two Newton-Raphson iterations. Multiplying this with the original argument provides an accurate estimate of the square root. The square of this approximation is subtracted from the orginal argument to compute the approximation error for the square root. This error is then used to apply a correction to the square root approximation, resulting in a correctly-rounded square root.

However, this straightforward algorithm breaks down for arguments that are very small due to underflow or overflow in intermediate computation, in particular when the underlying arithmetic operates in flash-to-zero mode that flushes subnormals to zero. For such arguments we can construct a slowpath code that scales the input towards unity, and scales back the result accordingly once the square root has been computed. Code for handling special operands such as zeros, infinities, NaNs, and negative arguments other than zero is also added to this slowpath code.

The NaN generated by the slowpath code for invalid operations should be adjusted to match the system's existing operations. For example, for x86-based systems this would be a special QNaN called INDEFINITE, with a bit pattern of 0xffc00000, while for a GPU running CUDA it would be the canonical single-precision NaN with a bit pattern of 0x7fffffff.

For performance reasons it may be useful to inline the fastpath code while making the slowpath code a called outlined subroutine. Single-precision math functions with a single argument should always be tested exhaustively against a "golden" reference implementation, which takes just minutes on modern hardware.

I believe your problem is that you use an iterator to move across the buffer in which you are inserting text. As the documentation suggests:

Iterators are not valid indefinitely; whenever the buffer is modified in a way that affects the number of characters in the buffer, all outstanding iterators become invalid.

Instead of using iterators, I suggest using a Gtk::TextBuffer::Mark that refers to the end of the buffer. Unlike iterators, marks represent:

A position in the buffer, preserved across buffer modifications.

The Gtk::TextView widget also has overloads of its scroll_to method that deal with marks, one of which is:

Thanks @donaldh for pointing me to the right direction! By changing all occurences of rule to token fixed the issue!

ssmtp is nearly to be no longer supported. I tried to use the suggested package msmtp, and it works

You have to change the parameters inside the T2V layer and inside your network in order to correctly match the shapes

I figured it out. After poking around SO more, I updated my config file to set the logging level as follows:

/src/main/resources/application.yml