zhpmatrix

# Authors: Mathieu Blondel, Vlad Niculae
# License: BSD 3 clause

import numpy as np


def _gen_pairs(gen, max_iter, max_inner, random_state, verbose):
    rng = np.random.RandomState(random_state)

    # if tuple, interpret as randn

zhpmatrix

import torch

# Credits to AllenNLP for the base implementation and base tests:
# https://github.com/allenai/allennlp/blob/master/allennlp/nn/util.py#L174

# Modified AllenNLP `viterbi_decode` to support `top_k` sequences efficiently.
def viterbi_decode(tag_sequence: torch.Tensor, transition_matrix: torch.Tensor, top_k: int=5):
    """
    Perform Viterbi decoding in log space over a sequence given a transition matrix
    specifying pairwise (transition) potentials between tags and a matrix of shape

zhpmatrix

import tensorflow as tf
from numpy import *
from random import randint

max_length = 2
batch_size = 3

label = array([[[0,0] for _ in range(max_length)] for _ in range(batch_size)])
for i in range(batch_size):
	for j in range(max_length):

zhpmatrix

import tensorflow as tf
from numpy import *
from random import randint

max_length = 3
batch_size = 5

targets = array([[1 for _ in range(max_length)] for _ in range(batch_size)])
logits = array([[[randint(0,10)/10,randint(0,10)/10] for _ in range(max_length)] for _ in range(batch_size)])
sequence_length = array([randint(1,max_length) for _ in range(batch_size)])

zhpmatrix

function top = nms(boxes, overlap)
% top = nms_fast(boxes, overlap)
% Non-maximum suppression. (FAST VERSION)
% Greedily select high-scoring detections and skip detections
% that are significantly covered by a previously selected
% detection.
% NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m),
% but an inner loop has been eliminated to significantly speed it
% up in the case of a large number of boxes
% Tomasz Malisiewicz (tomasz@cmu.edu)

zhpmatrix

from keras.models import Sequential
from keras.layers import Dense

x, y = ...
x_val, y_val = ...

# 1-dimensional MSE linear regression in Keras
model = Sequential()
model.add(Dense(1, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='mse')