koaning

language: en
pipeline:
- name: WhitespaceTokenizer
- name: CountVectorsFeaturizer
- name: EmbeddingIntentClassifier
policies:
- name: EmbeddingPolicy
  max_history: 10
  epochs: 100
  batch_size:

jamal919

#!/bin/bash
## WRF installation with parallel process.
# Download and install required library and data files for WRF.
# License: LGPL
# Jamal Khan <jamal.khan@legos.obs-mip.fr>
# Tested in Ubuntu 18.04 LTS

# basic package managment
sudo apt update
sudo apt upgrade

tgsmith61591

# -*- coding: utf-8 -*-
#
# Author: Taylor G Smith
#
# Recommender system ranking metrics derived from Spark source for use with
# Python-based recommender libraries (i.e., implicit, 
# http://github.com/benfred/implicit/). These metrics are derived from the
# original Spark Scala source code for recommender metrics.
# https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala

Stonesjtu

import gc

import torch

## MEM utils ##
def mem_report():
    '''Report the memory usage of the tensor.storage in pytorch
    Both on CPUs and GPUs are reported'''

    def _mem_report(tensors, mem_type):

paduvi

import torch
import torch.nn as nn
import torch.nn.init as init

dropout_prob = 0.5


class FlatCnnLayer(nn.Module):
    def __init__(self, embedding_size, sequence_length, filter_sizes=[3, 4, 5], out_channels=128):
        super(FlatCnnLayer, self).__init__()

RyanAkilos

import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix

#Start
train_data_path = 'F://data//Train'

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)