A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
class SelfAttention(nn.Module): | |
def __init__(self, attention_size, batch_first=False, non_linearity="tanh"): | |
super(SelfAttention, self).__init__() | |
self.batch_first = batch_first | |
self.attention_weights = Parameter(torch.FloatTensor(attention_size)) | |
self.softmax = nn.Softmax(dim=-1) | |
if non_linearity == "relu": | |
self.non_linearity = nn.ReLU() |
from gensim.models import KeyedVectors | |
# Load gensim word2vec | |
w2v_path = '<Gensim File Path>' | |
w2v = KeyedVectors.load_word2vec_format(w2v_path) | |
import io | |
# Vector file, `\t` seperated the vectors and `\n` seperate the words | |
""" |
from keras import backend as K | |
from keras.engine import InputSpec | |
from keras.engine.topology import Layer | |
import numpy as np | |
class TemporalMaxPooling(Layer): | |
""" | |
This pooling layer accepts the temporal sequence output by a recurrent layer | |
and performs temporal pooling, looking at only the non-masked portion of the sequence. |
#!/usr/bin/env bash | |
set -x -e | |
JUPYTER_PASSWORD=${1:-"myJupyterPassword"} | |
NOTEBOOK_DIR=${2:-"s3://myS3Bucket/notebooks/"} | |
# home backup | |
if [ ! -d /mnt/home_backup ]; then | |
sudo mkdir /mnt/home_backup | |
sudo cp -a /home/* /mnt/home_backup |
from keras import backend as K, initializers, regularizers, constraints | |
from keras.engine.topology import Layer | |
def dot_product(x, kernel): | |
""" | |
Wrapper for dot product operation, in order to be compatible with both | |
Theano and Tensorflow | |
Args: |
from keras.engine.topology import Layer | |
from keras import initializations | |
from keras import backend as K | |
class Attention(Layer): | |
'''Attention operation for temporal data. | |
# Input shape | |
3D tensor with shape: `(samples, steps, features)`. | |
# Output shape | |
2D tensor with shape: `(samples, features)`. |
class AttentionLSTM(LSTM): | |
"""LSTM with attention mechanism | |
This is an LSTM incorporating an attention mechanism into its hidden states. | |
Currently, the context vector calculated from the attended vector is fed | |
into the model's internal states, closely following the model by Xu et al. | |
(2016, Sec. 3.1.2), using a soft attention model following | |
Bahdanau et al. (2014). | |
The layer expects two inputs instead of the usual one: |
from __future__ import print_function | |
import imageio | |
from PIL import Image | |
import numpy as np | |
import keras | |
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation | |
from keras.models import Model | |
from keras.regularizers import l2 | |
from keras.optimizers import SGD |
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
import boto | |
import boto.s3 | |
import os.path | |
import sys | |
# Fill these in - you get them when you sign up for S3 | |
AWS_ACCESS_KEY_ID = '' | |
AWS_ACCESS_KEY_SECRET = '' | |
# Fill in info on data to upload |