First, make sure we have the right environment. Comment out the conda command in ~/.bashrc
and run
source ~/.bashrc
conda activate corl
After that, comment out the conda command and open a new tab will get back to python 2.7 environment.
import tensorflow as tf | |
import numpy | |
from sklearn.datasets import fetch_mldata | |
FLAGS = tf.app.flags.FLAGS | |
tf.app.flags.DEFINE_integer('seed', 1, "initial random seed") | |
tf.app.flags.DEFINE_string('layer_sizes', '784-1200-600-300-150-10', "layer sizes") |
require 'torch' | |
require 'nn' | |
require 'optim' | |
-- to specify these at runtime, you can do, e.g.: | |
-- $ lr=0.001 th main.lua | |
opt = { | |
dataset = 'video2', -- indicates what dataset load to use (in data.lua) | |
nThreads = 32, -- how many threads to pre-fetch data | |
batchSize = 64, -- self-explanatory |
I've tried to make SequentialDataset
support Cython fused types, but it seems really expensive.
You can find the modified code in this branch.
tl;dr - seq_dataset.pyx
is heavily bound with sag_fast.pyx
, sgd_fast.pyx
.
After I modified seq_dataset.pyx
, this line in sag_fast.pyx
requires to change as well since this pointer is passed into SequentialDataset
's function. However, my past experience is that one can only declare local floating
variable when at least one of the function's argument variable also belongs to floating
type. Nonetheless, that's not the case here, unless we make this function's arguments
np.ndarray[double, ndim=2, mode='c'] weights_array
np.ndarray[double, ndim=1, mode='c'] intercept_array
Note on how to install caffe on Ubuntu. Sucessfully install using CPU, more information for GPU see this link
###Installation
lspci | grep -i nvidia
import timeit
import numpy as np
from sklearn.cluster import KMeans
np.random.seed(5)
X = np.random.rand(200000, 20)
X = np.float32(X)
estimator = KMeans()
In case you don't know, HTTP is stateless, which means the server you are communicating will not know who you are or what you've said to it before.
Say you logged in to a website, you will notice that you don't need to type your username, password etc when you visit the site again.
It looks like the server knows who you are, how could this be possible?
That's because a "session" is handling this for you.
Remember that HTTP is stateless, which means the server has no memories about what it said or what it heard.
import numpy as np
from scipy import sparse as sp
from sklearn.datasets.samples_generator import make_blobs
from csr_row_norms import csr_row_norms
import timeit
centers = np.array([
[0.0, 5.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 4.0, 0.0, 0.0],