rreece

"""
Writes float16 data to a tfrecord as raw bytes and reads it back.

Based on:
https://stackoverflow.com/questions/40184812/tensorflow-is-it-possible-to-store-tf-record-sequence-examples-as-float16
"""

import argparse
import numpy as np
import tensorflow as tf

rreece

def path_is_in_git(repo, path):
    """ 
    Check if path is tracked by git.
    """
    returncode = None
    try:
        cmd = 'git ls-files --error-unmatch %s' % (path)
        _ = subprocess.check_output(cmd,
                cwd=repo,
                shell=True,

rreece

# install chrome with .deb
# setup terminal profile

sudo apt update
sudo apt dist-upgrade
sudo apt install vim htop screen tmux
sudo apt install git

# create ssh key:
ssh-keygen -t rsa

rreece

import argparse
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2

from model import model_fn
from data import input_fn
from utils import (
    DEFAULT_PARAMS_FILE,
    get_params,

rreece

To remove a submodule you need to:

• Delete the relevant section from the .gitmodules file.
• Stage the .gitmodules changes git add .gitmodules
• Delete the relevant section from .git/config.
• Run git rm --cached path_to_submodule (no trailing slash).
• Run rm -rf .git/modules/path_to_submodule (no trailing slash).
• Commit git commit -m "Removed submodule "
• Delete the now untracked submodule files rm -rf path_to_submodule

rreece

"""
Main training script.

Call by:

    python3 train.py --params=params.yaml
"""

import argparse
import numpy as np

rreece

import tensorflow as tf

def segment_mean(x, bounds):
    n = len(bounds)
    segs = [ 
        tf.reduce_mean(x[:,bounds[i]:bounds[i+1]], axis=1) for i in range(n-1)]
    y = tf.stack(segs, axis=1)
    return y

rreece

def print_total_parameters():
    total_parameters = 0 
    for variable in tf.trainable_variables():
        # shape is an array of tf.Dimension
        shape = variable.get_shape()
        variable_parameters = 1 
        for dim in shape:
            variable_parameters *= dim.value
        print('%s  dim=%i shape=%s params=%i' % ( 
                    variable.name,

rreece

import tensorflow as tf
from google.protobuf.json_format import MessageToJson

filename = 'myfile.tfrecords'

for record_str in tf.python_io.tf_record_iterator(filename):
    example = tf.train.Example.FromString(record_str)
    json = MessageToJson(example)
    print(json)

rreece

from __future__ import print_function

import multiprocessing
import ctypes
import numpy as np

def shared_array(shape):
    """
    Form a shared memory numpy array.