11%
30.11.2020
):
11
12 s = 0.0
13 s += h * f(a)
14 for i in range(1, n):
15 s += 2.0 * h * f(a + i*h)
16 # end for
17 s += h * f(b)
18 return (s/2.)
19 # end def
20
21
22 # Main section
23 comm = MPI
11%
16.05.2013
://wiki.scilab.org/Linalg%20performances
Compiling
http://wiki.scilab.org/Compiling%20Scilab%205.x%20under%20GNU-Linux%20Unix
Parallel computing
http
11%
15.09.2020
} (default: yes
)
-o cache_timeout=N
– sets timeout for caches in seconds (default: 20
)
-o cache_X_timeout=N
– sets timeout for {stat
,dir
,link
} caches
-o compression=BOOL
– enables data
11%
30.11.2020
timeout for caches in seconds (default: 20)
* -o cache_X_timeout=N
Sets timeout for {stat,dir,link} caches
* -o compression=BOOL
Enables data compression {yes, no}
* -o
11%
17.06.2017
type for variable "a"
18 type(my_struct) :: a
19 ! ...
20 write(*,*) "i is ",a%i
21
22 ! Structures (variables) of the the derived type my_struct
23 type(my_struct) :: data
24 type
11%
04.10.2018
for the configuration commands), VLAN20 needs to be created on SW1 and SW2 for red hosts to communicate, whereas VLAN 30 must be on SW2 and SW3 for the blue hosts to communicate. If you want the green hosts
11%
03.12.2024
, MaxPooling
2D
:
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2,2)))
A max pooling layer has a pool size, 2x2 in this case, that is used to scan the entire input image (left
11%
26.01.2025
version isn't even 3.x. I haven't tested the code with Keras 3.x yet, so your mileage may vary if you go that route.
CIFAR-10
The model and dataset I use is CIFAR-10 [6]. It is a very common dataset
11%
18.10.2017
ab7000)
libc.so.6 => /lib64/libc.so.6 (0x00007f5bc46f4000)
libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00007f5bc44de000)
/lib64/ld-linux-x86-64.so.2 (0x000056123e669000
11%
31.07.2013
_record;
14 int counter_limit;
15
16 counter_limit = 100;
17
18 for ( counter=1; counter <= counter_limit; counter++)
19 {
20 my_record.x = counter;
21 my_record.y = counter + 1
