alexbw

[
	{"keys": ["alt+shift+up"], "command": "move_by_paragraph", "args": {"forward": false, "extend": true}},
	{"keys": ["alt+shift+down"], "command": "move_by_paragraph", "args": {"forward": true, "extend": true}},
	{"keys": ["alt+up"], "command": "move_by_paragraph", "args": {"forward": false, "extend": false}},
	{"keys": ["alt+down"], "command": "move_by_paragraph", "args": {"forward": true, "extend": false}},
]

alexbw

%pylab inline

#!/usr/bin/env python
from stat import S_ISREG, ST_CTIME, ST_MODE
import os, sys, time

# path to the directory (relative or absolute)
dirpath = "/Users/Alex/Code/autograd/.asv/results/quincy.local/"

# get all entries in the directory w/ stats

alexbw

from pylab import *
from autograd import grad
import autograd.numpy as np
import torch
from torch.autograd import Variable
from memory_profiler import memory_usage
from autograd.core import forward_pass, make_vjp
from autograd.convenience_wrappers import safe_type,as_scalar,cast_to_same_dtype

batch_size = 16

alexbw

import autograd.numpy as np
from autograd import grad

##################################################################

def autograd_rnn(params, x, label):
    W, b, Wout, bout = params
    h1 = x
    for i in range(10):
        h1 = np.tanh(np.dot(h1, W) + b)

alexbw

from pylab import *
from autograd import grad
import autograd.numpy as np
import torch
from torch.autograd import Variable
from memory_profiler import memory_usage

batch_size = 16

D = 2**10

alexbw

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     6                                           def grad_fn(x):
     7         1            5      5.0      0.3      x_stack = deque()
     8         1            2      2.0      0.1      (_eebd10_stack, _eebd10) = (deque(), None)
     9         1            1      1.0      0.1      (_dab857_stack, _dab857) = (deque(), None)
    10         1            1      1.0      0.1      (_14c3e8_stack, _14c3e8) = (deque(), None)
    11         1            3      3.0      0.2      _eebd10 = range(100)
    12         1            2      2.0      0.1      _eebd10_stack.append(_eebd10)
    13       101           73      0.7      5.0      for i in _eebd10:

alexbw

```python
def f(x):
    return np.sum(x)
grad(f)(np.eye(3))
```

```python
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-a3386d50981f> in <module>()

alexbw

def f(params,image):
    h1 = layer1(params,image)
    h2 = layer2(params,h1)
    
    with gradsthru(h2) as gh2:
        print("Only evaluated in the backwards pass!")
        print(np.linalg.norm(gh2))
        print("Clipping just this gradient!")
        gh2 = np.clip(gh2,-10,10)
        

alexbw

package="lbase64"
version="20120820-1"
source = {
   url = "https://www.dropbox.com/s/p5qei9czulhox6w/lbase64.tar.gz?dl=0",
   dir = "base64"
}
description = {
   summary = "A Base64 library for Lua",
   detailed = [[
      A Base64 library for Lua. Base64 is a base-64 transfer encoding that

alexbw

[
	{"keys": ["alt+shift+up"], "command": "move_by_paragraph", "args": {"forward": false, "extend": true}},
	{"keys": ["alt+shift+down"], "command": "move_by_paragraph", "args": {"forward": true, "extend": true}},
	{"keys": ["alt+up"], "command": "move_by_paragraph", "args": {"forward": false, "extend": false}},
	{"keys": ["alt+down"], "command": "move_by_paragraph", "args": {"forward": true, "extend": false}},

]


/*