domluna

struct CPolygon
    vertexlist::Ptr{Cint}
    numberofvertices::Cint
end

struct CFacet{T}
    polygonlist::Ptr{CPolygon}
    numberofpolygons::Cint

domluna

export @esc, isexpr, isline, rmlines, unblock, block, inexpr, namify, isdef,
  longdef, shortdef, @expand, makeif, prettify, splitdef, splitarg

"""
    assoc!(d, k, v)

is the same as `d[k] = v` but returns `d` rather than `v`.
"""
assoc!(d, k, v) = (d[k] = v; d)

domluna

Transformer notes

• current models have trouble learning dependencies over distance (i.e. between characters/words), # ops scale O(n) or O(log n).

• transformer is O(1) in number of ops

• encoder-decoder with residual conns. Encoder/decodes feed into themselves N times.

• We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, **ensures that the predictions for position i can depend only on the known outputs at positions less than i **.

def subsequent_mask(size):

domluna

import tensorflow as tf
import numpy as np


x = tf.constant(np.random.randn(1, 4, 4, 2), dtype=tf.float32)
# TODO: Use `tf.layers.conv2d_transpose` to return a tensor
# with the shape (1, 8, 8, 5)
conv = 0

with tf.Session() as sess:

domluna

 
def iou(img, y, c):
    intersection = 0.
    union = 0.
    img = img.reshape(-1)
    y = y.reshape(-1)
    for i in range(len(img)):
         intersection += img[i] == c and y[i] == c
         union += img[i] == c or y[i] == c
    return intersection / union

domluna

"""
Load SavedModel

Output graphdef and checkpoint files
"""

import tensorflow as tf
import argparse
import sys

domluna

# Required downloads:
# NVIDIA-Linux-x86_64-367.27.run
# cuda_8.0.27_linux.run
# cudnn-8.0-linux-x64-v5.0-ga.tgz

sudo apt-get install build-essential
sudo apt-get install linux-image-extra-`uname -r`
sudo ./NVIDIA-Linux-x86_64-367.27.run
./cuda_8.0.27_linux.run --extract=`pwd`/extracts
sudo ./extracts/cuda-linux64-rel-8.0.27-20733550.run

domluna

import  tensorflow as tf
import numpy as np

w = np.arange(1, 10, dtype=np.float32).reshape((3,3,1,1))
f = tf.Variable(tf.constant(w))
input = tf.placeholder(tf.float32, (None, 28, 28, 1))
conv = tf.nn.conv2d(input, f, [1,2,2,1], 'SAME')

s = tf.Session()
x = np.zeros((28, 28), dtype=np.float32)

domluna

import  tensorflow as tf
import numpy as np

w = np.arange(1, 10, dtype=np.float32).reshape((3,3,1,1))
f = tf.Variable(tf.constant(w))
input = tf.placeholder(tf.float32, (None, 28, 28, 1))
conv = tf.nn.conv2d(input, f, [1,2,2,1], 'VALID')

s = tf.Session()
x = np.zeros((28, 28), dtype=np.float32)

domluna

name: CarND-Term1
channels:
    - https://conda.anaconda.org/menpo
dependencies:
    - python==3.5.2
    - numpy
    - matplotlib
    - tensorflow
    - jupyter
    - opencv3