oseiskar

lirnli

lirnli

fchollet

import keras
import numpy as np

timesteps = 60
input_dim = 64
samples = 10000
batch_size = 128
output_dim = 64

# Test data.

pieceofsummer

#!/usr/bin/python
import os, sys, gzip
from StringIO import StringIO
from datetime import datetime

def readByte(f):
	return ord(f.read(1))

def readInt(f):
	l = 0

tkm2261

cimport cython
import numpy as np
cimport numpy as np
from sklearn.metrics import f1_score


@cython.boundscheck(False)
@cython.wraparound(False)
def f1_opt(np.ndarray[long, ndim=1] label, np.ndarray[double, ndim=1] preds):

timehaven

"""akmtdfgen:  A Keras multithreaded dataframe generator.

Works with Python 2.7 and Keras 2.x.

For Python 3.x, need to fiddle with the threadsafe generator code.


Test the generator_from_df() functions by running this file:

    python akmtdfgen.py

Tushar-N

import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

seqs = ['gigantic_string','tiny_str','medium_str']

# make <pad> idx 0
vocab = ['<pad>'] + sorted(set(''.join(seqs)))

# make model

swyoon

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

apaszke

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen: