Jason Poulos jvpoulos

<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

NEVER use meta-phrases (e.g., "let me help you", "I can see that").
NEVER summarize unless explicitly requested.
NEVER provide unsolicited advice.
NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
ALWAYS be specific, detailed, and accurate.

	#!/bin/bash

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###

	### to verify your gpu is cuda enable check

	#!/usr/bin/python
	# -- coding: utf-8 --

	# http://stackoverflow.com/questions/5574702/how-to-print-to-stderr-in-python
	from __future__ import print_function
	import sys
	def eprint(args, *kwargs):
	print(args, file=sys.stderr, *kwargs)

	import errno


	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:

	"""
	A keras attention layer that wraps RNN layers.

	Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506)
	and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

	date: 20161101
	author: wassname
	url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
	"""

	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:

	# Keras==1.0.6
	from keras.models import Sequential
	import numpy as np
	from keras.layers.recurrent import LSTM
	from keras.layers.core import TimeDistributedDense, Activation
	from keras.preprocessing.sequence import pad_sequences
	from keras.layers.embeddings import Embedding
	from sklearn.cross_validation import train_test_split
	from keras.layers import Merge
	from keras.backend import tf

	# By Jake VanderPlas
	# License: BSD-style

	import matplotlib.pyplot as plt
	import numpy as np


	def discrete_cmap(N, base_cmap=None):
	"""Create an N-bin discrete colormap from the specified input map"""

	#’ Create a Kaplan-Meier plot using ggplot2
	#’
	#’ @param sfit a \code{\link[survival]{survfit}} object
	#’ @param returns logical: if \code{TRUE}, return an ggplot object
	#’ @param xlabs x-axis label
	#’ @param ylabs y-axis label
	#’ @param ystratalabs The strata labels. \code{Default = levels(summary(sfit)$strata)}
	#’ @param ystrataname The legend name. Default = “Strata”
	#’ @param timeby numeric: control the granularity along the time-axis
	#’ @param main plot title

	# Compiled source #
	###################
	*.com
	*.class
	*.dll
	*.exe
	*.o
	*.so

	# Packages #