mrubash1

# Install SSHFS:
sudo apt-get install sshfs

# Make a local directory to mount the volume to:
sudo mkdir /mnt/aws

# Mount the volume 
# Note:  Make sure you use abosulute paths for all of the directories.
sudo sshfs -o allow_other,IdentityFile=/home/yourname/.ssh/key.pem ubuntu@ec2-XX-XXX-XX-XX.us-west-2.compute.amazonaws.com:/home/ubuntu /mnt/aws

mrubash1

# Set parameters for generating different images
parameters = [
    {'name': uuid.uuid4(), 't_obj_filter': 139, 'img0': img0, 'iter_n':10, 'step':1.5, 'octave_n':4, 'octave_scale':1.4},
    {'name': uuid.uuid4(), 't_obj_filter': 139, 'img0': img0, 'iter_n':10, 'step':1.5, 'octave_n':4, 'octave_scale':1.0}, ]
output_data={}

# Iterate through all parameters and track performance
for i in range(0,len(parameters)):
    start_time=time.time()
    # Perform the deep dream rendering and store output array, which is called output_image

mrubash1

def setup_summary_statistics(self):
  # Create a placeholder for the summary statistics
  with tf.name_scope("accuracy"):
    
  # Compute the edit (Levenshtein) distance of the top path
  distance = tf.edit_distance(tf.cast(self.decoded[0], tf.int32), self.targets)

  # Compute the label error rate (accuracy)
  self.ler = tf.reduce_mean(distance, name='label_error_rate')
  self.ler_placeholder = tf.placeholder(dtype=tf.float32, shape=[])

mrubash1

with tf.name_scope('lstm'):
    # Forward direction cell:
    lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(n_cell_dim, forget_bias=1.0, state_is_tuple=True)
    # Backward direction cell:
    lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(n_cell_dim, forget_bias=1.0, state_is_tuple=True)

    # Now we feed `layer_3` into the LSTM BRNN cell and obtain the LSTM BRNN output.
    outputs, output_states = tf.nn.bidirectional_dynamic_rnn(cell_fw=lstm_fw_cell,
                                                             cell_bw=lstm_bw_cell,
                                                             inputs=layer_3,

mrubash1

# Load wav files
fs, audio = wav.read(audio_filename)

# Get mfcc coefficients
orig_inputs = mfcc(audio, samplerate=fs, numcep=numcep)

# For each time slice of the training set, we need to copy the context this makes
train_inputs = np.array([], np.float32)
train_inputs.resize((orig_inputs.shape[0], numcep + 2 * numcep * numcontext))

mrubash1

class DataSet:
    def __init__(self, txt_files, thread_count, batch_size, numcep, numcontext):
        # ...

    def from_directory(self, dirpath, start_idx=0, limit=0, sort=None):
        return txt_filenames(dirpath, start_idx=start_idx, limit=limit, sort=sort)

    def next_batch(self, batch_size=None):
        idx_list = range(_start_idx, end_idx)
        txt_files = [_txt_files[i] for i in idx_list]

mrubash1

def create_and_persist_graph():
  with tf.Session() as persisted_sess:
    # Load Graph
    with tf.gfile.FastGFile(modelFullPath,’rb’) as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      persisted_sess.graph.as_default()
      tf.import_graph_def(graph_def, name=’’)
    return persisted_sess.graph

mrubash1

Keybase proof

I hereby claim:

• I am mrubash1 on github.
• I am mrubash1 (https://keybase.io/mrubash1) on keybase.
• I have a public key ASDSagzJIK0kOintkYJLPoVm59TF1VcxnwIwR-mAC-BcWgo

To claim this, I am signing this object:

mrubash1

# -*- coding: utf-8 -*-
import itertools
import re
import urlparse
 
import boto
import warc
 
from boto.s3.key import Key
from gzipstream import GzipStreamFile