gavincangan

#! /usr/local/bin/python3.6
"""
3-D spline interpolation
(with graph drawing by matplotlib)
"""
import matplotlib.pyplot as plt
import sys
import traceback

class SplineInterpolation:

gavincangan

These commands are based on a askubuntu answer http://askubuntu.com/a/581497
To install gcc-6 (gcc-6.1.1), I had to do more stuff as shown below.
USE THOSE COMMANDS AT YOUR OWN RISK. I SHALL NOT BE RESPONSIBLE FOR ANYTHING.
ABSOLUTELY NO WARRANTY.

If you are still reading let's carry on with the code.

sudo apt-get update && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \

gavincangan

import matplotlib

matplotlib.use('webagg')

import numpy as np
from scipy.special import binom

import matplotlib.pyplot as plt

from matplotlib.lines import Line2D

gavincangan

import tensorflow as tf

def beam_decoder(decoder_inputs, initial_state, cell, loop_function, scope=None,
                 beam_size=7, done_token=0
                ):
    """
    Beam search decoder
    
    Args:
      decoder_inputs: A list of 2D Tensors [batch_size x input_size].

gavincangan

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

gavincangan

class AttentionWithContext(Layer):
    """
    Attention operation, with a context/query vector, for temporal data.
    Supports Masking.
    Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
    "Hierarchical Attention Networks for Document Classification"
    by using a context vector to assist the attention
    # Input shape
        3D tensor with shape: `(samples, steps, features)`.
    # Output shape

gavincangan

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

get_available_gpus()