View feed_forward_neural_network.py
import numpy as np
class Network:
def __init__(self, num_inputs, num_hidden, num_output,
init_weight_scale=0.5):
self.w1 = np.random.normal(
0, init_weight_scale, (num_inputs + 1, num_hidden))
self.w2 = np.random.normal(
View 2016-09-24T05-54-08-dqn-gather.yaml
algorithms:
- config:
| frame_skip: 6
| history: 6
| replay_capacity: 5e4
name: DQN
train_steps: 200000
type: DQN
envs:
- SimpleGather-v0
View 2016-06-10-char-rnn-sample.md

Char-RNN Sample

Parameters

dataset = ArxivAbstracts(
    categories='stat.ML cs.NE cs.LG math.OC',
    keywords='neural network deep')
max_length = 50
sampling_temperature = 0.5
View relations_semeval_glove_not_found.txt
keygen
uprises
non-infected
counter-weight
pipetted
quispel
rooster-whistles
moisturisers
enfeoffing
pre-adolescents
View blog_tensorflow_variable_sequence_labelling.py
# Working example for my blog post at:
# http://danijar.com/variable-sequence-lengths-in-tensorflow/
import functools
import sets
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn
def lazy_property(function):
View blog_tensorflow_variable_sequence_classification.py
# Working example for my blog post at:
# http://danijar.com/variable-sequence-lengths-in-tensorflow/
import functools
import sets
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn
def lazy_property(function):
View script_tfidf.py
import argparse
import collections
import re
TOKEN_REGEX = re.compile(r'[A-Za-z]+')
BLACKLIST = set([
'pdf', 'and', 'the', 'proceedings', 'conference', 'ieee', 'for',
'about', 'details', 'data', 'with', 'arxiv', 'preprint', 'advances'])
def tokenize(line):
View blog_tensorflow_sequence_labelling.py
# Example for my blog post at:
# http://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf
def lazy_property(function):
attribute = '_' + function.__name__
View blog_tensorflow_sequence_classification.py
# Example for my blog post at:
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf
def lazy_property(function):
attribute = '_' + function.__name__
View blog_tensorflow_scope_decorator.py
# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def doublewrap(function):
"""
A decorator decorator, allowing to use the decorator to be used without