kevinzakka

import torch
import torch.nn as nn
import torch.nn.functional as F


class SpatialSoftArgmax(nn.Module):
    """Spatial softmax as defined in [1].

    Concretely, the spatial softmax of each feature
    map is used to compute a weighted mean of the pixel

williamFalcon

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F

"""
Blog post:
Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health:
https://medium.com/@_willfalcon/taming-lstms-variable-sized-mini-batches-and-why-pytorch-is-good-for-your-health-61d35642972e
"""

khanhnamle1994

#--------------------------
# USER-SPECIFIED DATA
#--------------------------

# Tune these parameters

num_classes = 2
image_shape = (160, 576)
EPOCHS = 40
BATCH_SIZE = 16

tokestermw

import torch, torch.nn as nn
from torch.autograd import Variable

text = ['BOS', 'How', 'are', 'you', 'EOS']
seq_len = len(text)
batch_size = 1
embedding_size = 1
hidden_size = 1
output_size = 1

ceshine

hiwonjoon

import numpy as np
import sys
import random
import tensorflow as tf
import itertools
slim = tf.contrib.slim

BATCH_SIZE = 32
TRAIN_ITER = 1000
EARLY_TRAIN_TERMINATE_CRITERIA = 1e-3;

spro

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
from torch import optim
import numpy as np
import math, random

# Generating a noisy multi-sin wave 

shamatar

from keras.layers import Recurrent
import keras.backend as K
from keras import activations
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.engine import Layer
from keras.engine import InputSpec

awjuliani

awjuliani