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The helper function `toplevel_defs()` helps saving and restoring the environment before and after redefining the function under repair. | class Repairer(Repairer):
def toplevel_defs(self, tree: ast.AST) -> List[str]:
"""Return a list of names of defined functions and classes in `tree`"""
visitor = DefinitionVisitor()
visitor.visit(tree)
assert hasattr(visitor, 'definitions')
return visitor.definitions
class DefinitionVisitor(NodeVisitor):
def __init__(self) -> None:
self.definitions: List[str] = []
def add_definition(self, node: Union[ast.ClassDef,
ast.FunctionDef,
ast.AsyncFunctionDef]) -> None:
self.definitions.append(node.name)
def visit_FunctionDef(self, node: ast.FunctionDef) -> None:
self.add_definition(node)
def visit_AsyncFunctionDef(self, node: ast.AsyncFunctionDef) -> None:
self.add_definition(node)
def visit_ClassDef(self, node: ast.ClassDef) -> None:
self.add_definition(node) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here's an example for `fitness()`: | repairer = Repairer(middle_debugger, log=1)
good_fitness = repairer.fitness(middle_tree())
good_fitness
# ignore
assert good_fitness >= 0.99, "fitness() failed"
bad_middle_tree = ast.parse("def middle(x, y, z): return x")
bad_fitness = repairer.fitness(bad_middle_tree)
bad_fitness
# ignore
assert bad_fitness < 0.5, "fitness() failed" | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
RepairingNow for the actual `repair()` method, which creates a `population` and then evolves it until the fitness is 1.0 or the given number of iterations is spent. | import traceback
class Repairer(Repairer):
def initial_population(self, size: int) -> List[ast.AST]:
"""Return an initial population of size `size`"""
return [self.target_tree] + \
[self.mutator.mutate(copy.deepcopy(self.target_tree))
for i in range(size - 1)]
def repair(self, population_size: int = POPULATION_SIZE, iterations: int = 100) -> \
Tuple[ast.AST, float]:
"""
Repair the function we collected test runs from.
Use a population size of `population_size` and
at most `iterations` iterations.
Returns a pair (`ast`, `fitness`) where
`ast` is the AST of the repaired function, and
`fitness` is its fitness (between 0 and 1.0)
"""
self.validate()
population = self.initial_population(population_size)
last_key = ast.dump(self.target_tree)
for iteration in range(iterations):
population = self.evolve(population)
best_tree = population[0]
fitness = self.fitness(best_tree)
if self.log:
print(f"Evolving population: "
f"iteration{iteration:4}/{iterations} "
f"fitness = {fitness:.5} \r", end="")
if self.log >= 2:
best_key = ast.dump(best_tree)
if best_key != last_key:
print()
print()
self.log_tree(f"New best code (fitness = {fitness}):",
best_tree)
last_key = best_key
if fitness >= 1.0:
break
if self.log:
print()
if self.log and self.log < 2:
self.log_tree(f"Best code (fitness = {fitness}):", best_tree)
best_tree = self.reduce(best_tree)
fitness = self.fitness(best_tree)
self.log_tree(f"Reduced code (fitness = {fitness}):", best_tree)
return best_tree, fitness | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
EvolvingThe evolution of our population takes place in the `evolve()` method. In contrast to the `evolve_middle()` function, above, we use crossover to create the offspring, which we still mutate afterwards. | class Repairer(Repairer):
def evolve(self, population: List[ast.AST]) -> List[ast.AST]:
"""Evolve the candidate population by mutating and crossover."""
n = len(population)
# Create offspring as crossover of parents
offspring: List[ast.AST] = []
while len(offspring) < n:
parent_1 = copy.deepcopy(random.choice(population))
parent_2 = copy.deepcopy(random.choice(population))
try:
self.crossover.crossover(parent_1, parent_2)
except CrossoverError:
pass # Just keep parents
offspring += [parent_1, parent_2]
# Mutate offspring
offspring = [self.mutator.mutate(tree) for tree in offspring]
# Add it to population
population += offspring
# Keep the fitter part of the population
population.sort(key=self.fitness_key, reverse=True)
population = population[:n]
return population | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
A second difference is that we not only sort by fitness, but also by tree size – with equal fitness, a smaller tree thus will be favored. This helps keeping fixes and patches small. | class Repairer(Repairer):
def fitness_key(self, tree: ast.AST) -> Tuple[float, int]:
"""Key to be used for sorting the population"""
tree_size = len([node for node in ast.walk(tree)])
return (self.fitness(tree), -tree_size) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
SimplifyingThe last step in repairing is simplifying the code. As demonstrated in the chapter on [reducing failure-inducing inputs](DeltaDebugger.ipynb), we can use delta debugging on code to get rid of superfluous statements. To this end, we convert the tree to lines, run delta debugging on them, and then convert it back to a tree. | class Repairer(Repairer):
def reduce(self, tree: ast.AST) -> ast.AST:
"""Simplify `tree` using delta debugging."""
original_fitness = self.fitness(tree)
source_lines = astor.to_source(tree).split('\n')
with self.reducer:
self.test_reduce(source_lines, original_fitness)
reduced_lines = self.reducer.min_args()['source_lines']
reduced_source = "\n".join(reduced_lines)
return ast.parse(reduced_source) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
As dicussed above, we simplify the code by having the test function (`test_reduce()`) declare reaching the maximum fitness obtained so far as a "failure". Delta debugging will then simplify the input as long as the "failure" (and hence the maximum fitness obtained) persists. | class Repairer(Repairer):
def test_reduce(self, source_lines: List[str], original_fitness: float) -> None:
"""Test function for delta debugging."""
try:
source = "\n".join(source_lines)
tree = ast.parse(source)
fitness = self.fitness(tree)
assert fitness < original_fitness
except AssertionError:
raise
except SyntaxError:
raise
except IndentationError:
raise
except Exception:
# traceback.print_exc() # Uncomment to see internal errors
raise | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
End of Excursion Repairer in ActionLet us go and apply `Repairer` in practice. We initialize it with `middle_debugger`, which has (still) collected the passing and failing runs for `middle_test()`. We also set `log` for some diagnostics along the way. | repairer = Repairer(middle_debugger, log=True) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We now invoke `repair()` to evolve our population. After a few iterations, we find a best tree with perfect fitness. | best_tree, fitness = repairer.repair()
print_content(astor.to_source(best_tree), '.py')
fitness | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Again, we have a perfect solution. Here, we did not even need to simplify the code in the last iteration, as our `fitness_key()` function favors smaller implementations. Removing HTML MarkupLet us apply `Repairer` on our other ongoing example, namely `remove_html_markup()`. | def remove_html_markup(s): # type: ignore
tag = False
quote = False
out = ""
for c in s:
if c == '<' and not quote:
tag = True
elif c == '>' and not quote:
tag = False
elif c == '"' or c == "'" and tag:
quote = not quote
elif not tag:
out = out + c
return out
def remove_html_markup_tree() -> ast.AST:
return ast.parse(inspect.getsource(remove_html_markup)) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
To run `Repairer` on `remove_html_markup()`, we need a test and a test suite. `remove_html_markup_test()` raises an exception if applying `remove_html_markup()` on the given `html` string does not yield the `plain` string. | def remove_html_markup_test(html: str, plain: str) -> None:
outcome = remove_html_markup(html)
assert outcome == plain, \
f"Got {repr(outcome)}, expected {repr(plain)}" | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Now for the test suite. We use a simple fuzzing scheme to create dozens of passing and failing test cases in `REMOVE_HTML_PASSING_TESTCASES` and `REMOVE_HTML_FAILING_TESTCASES`, respectively. Excursion: Creating HTML Test Cases | def random_string(length: int = 5, start: int = ord(' '), end: int = ord('~')) -> str:
return "".join(chr(random.randrange(start, end + 1)) for i in range(length))
random_string()
def random_id(length: int = 2) -> str:
return random_string(start=ord('a'), end=ord('z'))
random_id()
def random_plain() -> str:
return random_string().replace('<', '').replace('>', '')
def random_string_noquotes() -> str:
return random_string().replace('"', '').replace("'", '')
def random_html(depth: int = 0) -> Tuple[str, str]:
prefix = random_plain()
tag = random_id()
if depth > 0:
html, plain = random_html(depth - 1)
else:
html = plain = random_plain()
attr = random_id()
value = '"' + random_string_noquotes() + '"'
postfix = random_plain()
return f'{prefix}<{tag} {attr}={value}>{html}</{tag}>{postfix}', \
prefix + plain + postfix
random_html()
def remove_html_testcase(expected: bool = True) -> Tuple[str, str]:
while True:
html, plain = random_html()
outcome = (remove_html_markup(html) == plain)
if outcome == expected:
return html, plain
REMOVE_HTML_TESTS = 100
REMOVE_HTML_PASSING_TESTCASES = \
[remove_html_testcase(True) for i in range(REMOVE_HTML_TESTS)]
REMOVE_HTML_FAILING_TESTCASES = \
[remove_html_testcase(False) for i in range(REMOVE_HTML_TESTS)] | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
End of Excursion Here is a passing test case: | REMOVE_HTML_PASSING_TESTCASES[0]
html, plain = REMOVE_HTML_PASSING_TESTCASES[0]
remove_html_markup_test(html, plain) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here is a failing test case (containing a double quote in the plain text) | REMOVE_HTML_FAILING_TESTCASES[0]
with ExpectError():
html, plain = REMOVE_HTML_FAILING_TESTCASES[0]
remove_html_markup_test(html, plain) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We run our tests, collecting the outcomes in `html_debugger`. | html_debugger = OchiaiDebugger()
for html, plain in (REMOVE_HTML_PASSING_TESTCASES +
REMOVE_HTML_FAILING_TESTCASES):
with html_debugger:
remove_html_markup_test(html, plain) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The suspiciousness distribution will not be of much help here – pretty much all lines in `remove_html_markup()` have the same suspiciousness. | html_debugger | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Let us create our repairer and run it. | html_repairer = Repairer(html_debugger, log=True)
best_tree, fitness = html_repairer.repair(iterations=20) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We see that the "best" code is still our original code, with no changes. And we can set `iterations` to 50, 100, 200... – our `Repairer` won't be able to repair it. | quiz("Why couldn't `Repairer()` repair `remove_html_markup()`?",
[
"The population is too small!",
"The suspiciousness is too evenly distributed!",
"We need more test cases!",
"We need more iterations!",
"There is no statement in the source with a correct condition!",
"The population is too big!",
], '5242880 >> 20') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
You can explore all of the hypotheses above by changing the appropriate parameters, but you won't be able to change the outcome. The problem is that, unlike `middle()`, there is no statement (or combination thereof) in `remove_html_markup()` that could be used to make the failure go away. For this, we need to mutate another aspect of the code, which we will explore in the next section. Mutating ConditionsThe `Repairer` class is very configurable. The individual steps in automated repair can all be replaced by providing own classes in the keyword arguments of its `__init__()` constructor:* To change fault localization, pass a different `debugger` that is a subclass of `RankingDebugger`.* To change the mutation operator, set `mutator_class` to a subclass of `StatementMutator`.* To change the crossover operator, set `crossover_class` to a subclass of `CrossoverOperator`.* To change the reduction algorithm, set `reducer_class` to a subclass of `Reducer`.In this section, we will explore how to extend the mutation operator such that it can mutate _conditions_ for control constructs such as `if`, `while`, or `for`. To this end, we introduce a new class `ConditionMutator` subclassing `StatementMutator`. Collecting ConditionsLet us start with a few simple supporting functions. The function `all_conditions()` retrieves all control conditions from an AST. | def all_conditions(trees: Union[ast.AST, List[ast.AST]],
tp: Optional[Type] = None) -> List[ast.expr]:
"""
Return all conditions from the AST (or AST list) `trees`.
If `tp` is given, return only elements of that type.
"""
if not isinstance(trees, list):
assert isinstance(trees, ast.AST)
trees = [trees]
visitor = ConditionVisitor()
for tree in trees:
visitor.visit(tree)
conditions = visitor.conditions
if tp is not None:
conditions = [c for c in conditions if isinstance(c, tp)]
return conditions | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
`all_conditions()` uses a `ConditionVisitor` class to walk the tree and collect the conditions: | class ConditionVisitor(NodeVisitor):
def __init__(self) -> None:
self.conditions: List[ast.expr] = []
self.conditions_seen: Set[str] = set()
super().__init__()
def add_conditions(self, node: ast.AST, attr: str) -> None:
elems = getattr(node, attr, [])
if not isinstance(elems, list):
elems = [elems]
elems = cast(List[ast.expr], elems)
for elem in elems:
elem_str = astor.to_source(elem)
if elem_str not in self.conditions_seen:
self.conditions.append(elem)
self.conditions_seen.add(elem_str)
def visit_BoolOp(self, node: ast.BoolOp) -> ast.AST:
self.add_conditions(node, 'values')
return super().generic_visit(node)
def visit_UnaryOp(self, node: ast.UnaryOp) -> ast.AST:
if isinstance(node.op, ast.Not):
self.add_conditions(node, 'operand')
return super().generic_visit(node)
def generic_visit(self, node: ast.AST) -> ast.AST:
if hasattr(node, 'test'):
self.add_conditions(node, 'test')
return super().generic_visit(node) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here are all the conditions in `remove_html_markup()`. This is some material to construct new conditions from. | [astor.to_source(cond).strip()
for cond in all_conditions(remove_html_markup_tree())] | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Mutating ConditionsHere comes our `ConditionMutator` class. We subclass from `StatementMutator` and set an attribute `self.conditions` containing all the conditions in the source. The method `choose_condition()` randomly picks a condition. | class ConditionMutator(StatementMutator):
"""Mutate conditions in an AST"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Constructor. Arguments are as with `StatementMutator` constructor."""
super().__init__(*args, **kwargs)
self.conditions = all_conditions(self.source)
if self.log:
print("Found conditions",
[astor.to_source(cond).strip()
for cond in self.conditions])
def choose_condition(self) -> ast.expr:
"""Return a random condition from source."""
return copy.deepcopy(random.choice(self.conditions)) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The actual mutation takes place in the `swap()` method. If the node to be replaced has a `test` attribute (i.e. a controlling predicate), then we pick a random condition `cond` from the source and randomly chose from:* **set**: We change `test` to `cond`.* **not**: We invert `test`.* **and**: We replace `test` by `cond and test`.* **or**: We replace `test` by `cond or test`.Over time, this might lead to operators propagating across the population. | class ConditionMutator(ConditionMutator):
def choose_bool_op(self) -> str:
return random.choice(['set', 'not', 'and', 'or'])
def swap(self, node: ast.AST) -> ast.AST:
"""Replace `node` condition by a condition from `source`"""
if not hasattr(node, 'test'):
return super().swap(node)
node = cast(ast.If, node)
cond = self.choose_condition()
new_test = None
choice = self.choose_bool_op()
if choice == 'set':
new_test = cond
elif choice == 'not':
new_test = ast.UnaryOp(op=ast.Not(), operand=node.test)
elif choice == 'and':
new_test = ast.BoolOp(op=ast.And(), values=[cond, node.test])
elif choice == 'or':
new_test = ast.BoolOp(op=ast.Or(), values=[cond, node.test])
else:
raise ValueError("Unknown boolean operand")
if new_test:
# ast.copy_location(new_test, node)
node.test = new_test
return node | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We can use the mutator just like `StatementMutator`, except that some of the mutations will also include new conditions: | mutator = ConditionMutator(source=all_statements(remove_html_markup_tree()),
log=True)
for i in range(10):
new_tree = mutator.mutate(remove_html_markup_tree()) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Let us put our new mutator to action, again in a `Repairer()`. To activate it, all we need to do is to pass it as `mutator_class` keyword argument. | condition_repairer = Repairer(html_debugger,
mutator_class=ConditionMutator,
log=2) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We might need more iterations for this one. Let us see... | best_tree, fitness = condition_repairer.repair(iterations=200)
repaired_source = astor.to_source(best_tree)
print_content(repaired_source, '.py') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Success again! We have automatically repaired `remove_html_markup()` – the resulting code passes all tests, including those that were previously failing. Again, we can present the fix as a patch: | original_source = astor.to_source(remove_html_markup_tree())
for patch in diff(original_source, repaired_source):
print_patch(patch) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
However, looking at the patch, one may come up with doubts. | quiz("Is this actually the best solution?",
[
"Yes, sure, of course. Why?",
"Err - what happened to single quotes?"
], 1 << 1) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Indeed – our solution does not seem to handle single quotes anymore. Why is that so? | quiz("Why aren't single quotes handled in the solution?",
[
"Because they're not important. I mean, who uses 'em anyway?",
"Because they are not part of our tests? "
"Let me look up how they are constructed..."
], 1 << 1) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Correct! Our test cases do not include single quotes – at least not in the interior of HTML tags – and thus, automatic repair did not care to preserve their handling. How can we fix this? An easy way is to include an appropriate test case in our set – a test case that passes with the original `remove_html_markup()`, yet fails with the "repaired" `remove_html_markup()` as whosn above. | with html_debugger:
remove_html_markup_test("<foo quote='>abc'>me</foo>", "me") | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Let us repeat the repair with the extended test set: | best_tree, fitness = condition_repairer.repair(iterations=200) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here is the final tree: | print_content(astor.to_source(best_tree), '.py') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
And here is its fitness: | fitness | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The revised candidate now passes _all_ tests (including the tricky quote test we added last). Its condition now properly checks for `tag` _and_ both quotes. (The `tag` inside the parentheses is still redundant, but so be it.) From this example, we can learn a few lessons about the possibilities and risks of automated repair:* First, automatic repair is highly dependent on the quality of the checking tests. The risk is that the repair may overspecialize towards the test.* Second, automated repair is highly dependent on the sources that program fragments are chosen from. If there is a hint of a solution somewhere in the code, there is a chance that automated repair will catch it up.* Third, automatic repair is a deeply heuristic approach. Its behavior will vary widely with any change to the parameters (and the underlying random number generators)* Fourth, automatic repair can take a long time. The examples we have in this chapter take less than a minute to compute, and neither Python nor our implementation is exactly fast. But as the search space grows, automated repair will take much longer.On the other hand, even an incomplete automated repair candidate can be much better than nothing at all – it may provide all the essential ingredients (such as the location or the involved variables) for a successful fix. When users of automated repair techniques are aware of its limitations and its assumptions, there is lots of potential in automated repair. Enjoy! Limitations The `Repairer` class is hardly tested. Things that do not work include* Functions with inner functions are not repaired. Synopsis This chapter provides tools and techniques for automated repair of program code. The `Repairer()` class takes a `RankingDebugger` debugger as input (such as `OchiaiDebugger` from [the chapter on statistical debugging](StatisticalDebugger.ipynb). A typical setup looks like this:```pythonfrom debuggingbook.StatisticalDebugger import OchiaiDebuggerdebugger = OchiaiDebugger()for inputs in TESTCASES: with debugger: test_foo(inputs)...repairer = Repairer(debugger)```Here, `test_foo()` is a function that raises an exception if the tested function `foo()` fails. If `foo()` passes, `test_foo()` should not raise an exception. The `repair()` method of a `Repairer` searches for a repair of the code covered in the debugger (except for methods starting or ending in `test`, such that `foo()`, not `test_foo()` is repaired). `repair()` returns the best fix candidate as a pair `(tree, fitness)` where `tree` is a [Python abstract syntax tree](http://docs.python.org/3/library/ast) (AST) of the fix candidate, and `fitness` is the fitness of the candidate (a value between 0 and 1). A `fitness` of 1.0 means that the candidate passed all tests. A typical usage looks like this:```pythonimport astortree, fitness = repairer.repair()print(astor.to_source(tree), fitness)``` Here is a complete example for the `middle()` program. This is the original source code of `middle()`: | # ignore
print_content(middle_source, '.py') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
We set up a function `middle_test()` that tests it. The `middle_debugger` collects testcases and outcomes: | middle_debugger = OchiaiDebugger()
for x, y, z in MIDDLE_PASSING_TESTCASES + MIDDLE_FAILING_TESTCASES:
with middle_debugger:
middle_test(x, y, z) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
The repairer attempts to repair the invoked function (`middle()`). The returned AST `tree` can be output via `astor.to_source()`: | middle_repairer = Repairer(middle_debugger)
tree, fitness = middle_repairer.repair()
print(astor.to_source(tree), fitness) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Here are the classes defined in this chapter. A `Repairer` repairs a program, using a `StatementMutator` and a `CrossoverOperator` to evolve a population of candidates. | # ignore
from ClassDiagram import display_class_hierarchy
# ignore
display_class_hierarchy([Repairer, ConditionMutator, CrossoverOperator],
abstract_classes=[
NodeVisitor,
NodeTransformer
],
public_methods=[
Repairer.__init__,
Repairer.repair,
StatementMutator.__init__,
StatementMutator.mutate,
ConditionMutator.__init__,
CrossoverOperator.__init__,
CrossoverOperator.crossover,
],
project='debuggingbook') | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Lessons Learned* Automated repair based on genetic optimization uses five ingredients: 1. A _test suite_ to determine passing and failing tests 2. _Defect localization_ (typically obtained from [statistical debugging](StatisticalDebugger.ipynb) with the test suite) to determine potential locations to be fixed 3. _Random code mutations_ and _crossover operations_ to create and evolve a population of inputs 4. A _fitness function_ and a _selection strategy_ to determine the part of the population that should be evolved further 5. A _reducer_ such as [delta debugging](DeltaDebugger.ipynb) to simplify the final candidate with the highest fitness.* The result of automated repair is a _fix candidate_ with the highest fitness for the given tests.* A _fix candidate_ is not guaranteed to be correct or optimal, but gives important hints on how to fix the program.* All of the above ingredients offer plenty of settings and alternatives to experiment with. BackgroundThe seminal work in automated repair is [GenProg](https://squareslab.github.io/genprog-code/) \cite{LeGoues2012}, which heavily inspired our `Repairer` implementation. Major differences between GenProg and `Repairer` include:* GenProg includes its own defect localization (which is also dynamically updated), whereas `Repairer` builds on earlier statistical debugging.* GenProg can apply multiple mutations on programs (or none at all), whereas `Repairer` applies exactly one mutation.* The `StatementMutator` used by `Repairer` includes various special cases for program structures (`if`, `for`, `while`...), whereas GenProg operates on statements only.* GenProg has been tested on large production programs.While GenProg is _the_ seminal work in the area (and arguably the most important software engineering research contribution of the 2010s), there have been a number of important extensions of automated repair. These include:* *AutoFix* \cite{Pei2014} leverages _program contracts_ (pre- and postconditions) to generate tests and assertions automatically. Not only do such [assertions](Assertions.ipynb) help in fault localization, they also allow for much better validation of fix candidates.* *SemFix* \cite{Nguyen2013} presents automated program repair based on _symbolic analysis_ rather than genetic optimization. This allows to leverage program semantics, which GenProg does not consider.To learn more about automated program repair, see [program-repair.org](http://program-repair.org), the community page dedicated to research in program repair. Exercises Exercise 1: Automated Repair ParametersAutomated Repair is influenced by a large number of design choices – the size of the population, the number of iterations, the genetic optimization strategy, and more. How do changes to these design choices affect its effectiveness? * Consider the constants defined in this chapter (such as `POPULATION_SIZE` or `WEIGHT_PASSING` vs. `WEIGHT_FAILING`). How do changes affect the effectiveness of automated repair?* As an effectiveness metric, consider the number of iterations it takes to produce a fix candidate.* Since genetic optimization is a random algorithm, you need to determine effectiveness averages over a large number of runs (say, 100). Exercise 2: Elitism[_Elitism_](https://en.wikipedia.org/wiki/Genetic_algorithmElitism) (also known as _elitist selection_) is a variant of genetic selection in which a small fraction of the fittest candidates of the last population are included unchanged in the offspring.* Implement elitist selection by subclassing the `evolve()` method. Experiment with various fractions (5%, 10%, 25%) of "elites" and see how this improves results. Exercise 3: Evolving ValuesFollowing the steps of `ConditionMutator`, implement a `ValueMutator` class that replaces one constant value by another one found in the source (say, `0` by `1` or `True` by `False`).For validation, consider the following failure in the `square_root()` function from [the chapter on assertions](Assertions.ipynb): | from Assertions import square_root # minor dependency
with ExpectError():
square_root_of_zero = square_root(0) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
Can your `ValueMutator` automatically fix this failure? **Solution.** Your solution will be effective if it also includes named constants such as `None`. | import math
def square_root_fixed(x): # type: ignore
assert x >= 0 # precondition
approx = 0 # <-- FIX: Change `None` to 0
guess = x / 2
while approx != guess:
approx = guess
guess = (approx + x / approx) / 2
assert math.isclose(approx * approx, x)
return approx
square_root_fixed(0) | _____no_output_____ | MIT | notebooks/Repairer.ipynb | bjrnmath/debuggingbook |
BLERSSI Seldon serving Clone Cisco Kubeflow Starter pack repository | BRANCH_NAME="master" #Provide git branch name "master" or "dev"
! git clone -b $BRANCH_NAME https://github.com/CiscoAI/cisco-kubeflow-starter-pack.git | Cloning into 'cisco-kubeflow-starter-pack'...
remote: Enumerating objects: 63, done.[K
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remote: Compressing objects: 100% (44/44), done.[K
remote: Total 4630 (delta 16), reused 44 (delta 11), pack-reused 4567[K
Receiving objects: 100% (4630/4630), 17.61 MiB | 48.72 MiB/s, done.
Resolving deltas: 100% (1745/1745), done.
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
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Successfully built sklearn dill Flask-OpenTracing opentracing jaeger-client threadloop thrift wasabi
Installing collected packages: pandas, joblib, threadpoolctl, scikit-learn, sklearn, click, itsdangerous, Flask, opentracing, Flask-OpenTracing, configparser, minio, grpcio-opentracing, azure-common, azure-storage-common, azure-storage-blob, redis, flatbuffers, threadloop, thrift, jaeger-client, Flask-cors, gunicorn, seldon-core, dill, soupsieve, beautifulsoup4, Pillow, PyWavelets, networkx, imageio, tifffile, scikit-image, catalogue, srsly, plac, cymem, murmurhash, preshed, blis, wasabi, tqdm, thinc, spacy, alibi
[33m WARNING: The script flask is installed in '/home/jovyan/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.[0m
[33m WARNING: The script gunicorn is installed in '/home/jovyan/.local/bin' which is not on PATH.
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[33m WARNING: The scripts seldon-batch-processor, seldon-core-api-tester, seldon-core-microservice, seldon-core-microservice-tester and seldon-core-tester are installed in '/home/jovyan/.local/bin' which is not on PATH.
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[33m WARNING: The scripts imageio_download_bin and imageio_remove_bin are installed in '/home/jovyan/.local/bin' which is not on PATH.
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[33m WARNING: The scripts lsm2bin and tifffile are installed in '/home/jovyan/.local/bin' which is not on PATH.
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Successfully installed Flask-1.1.2 Flask-OpenTracing-1.1.0 Flask-cors-3.0.8 Pillow-7.2.0 PyWavelets-1.1.1 alibi-0.3.2 azure-common-1.1.25 azure-storage-blob-2.1.0 azure-storage-common-2.1.0 beautifulsoup4-4.9.1 blis-0.4.1 catalogue-1.0.0 click-7.1.2 configparser-5.0.0 cymem-2.0.3 dill-0.3.2 flatbuffers-1.12 grpcio-opentracing-1.1.4 gunicorn-20.0.4 imageio-2.9.0 itsdangerous-1.1.0 jaeger-client-4.1.0 joblib-0.16.0 minio-5.0.10 murmurhash-1.0.2 networkx-2.4 opentracing-2.2.0 pandas-1.0.5 plac-1.1.3 preshed-3.0.2 redis-3.5.3 scikit-image-0.17.2 scikit-learn-0.23.1 seldon-core-1.2.1 sklearn-0.0 soupsieve-2.0.1 spacy-2.3.2 srsly-1.0.2 thinc-7.4.1 threadloop-1.0.2 threadpoolctl-2.1.0 thrift-0.13.0 tifffile-2020.7.22 tqdm-4.48.0 wasabi-0.7.1
[33mWARNING: You are using pip version 20.0.2; however, version 20.1.1 is available.
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| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Restart Notebook kernel | from IPython.display import display_html
display_html("<script>Jupyter.notebook.kernel.restart()</script>",raw=True) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Import Libraries | from __future__ import division
from __future__ import print_function
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
import yaml
import random
import re
import os
import dill
import logging
import requests
import json
from time import sleep
from sklearn.preprocessing import OneHotEncoder
from alibi.explainers import AnchorTabular
from kubernetes import client as k8s_client
from kubernetes import config as k8s_config
from kubernetes.client.rest import ApiException
k8s_config.load_incluster_config()
api_client = k8s_client.CoreV1Api()
custom_api=k8s_client.CustomObjectsApi() | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Get NamespaceGet current k8s namespace | def is_running_in_k8s():
return os.path.isdir('/var/run/secrets/kubernetes.io/')
def get_current_k8s_namespace():
with open('/var/run/secrets/kubernetes.io/serviceaccount/namespace', 'r') as f:
return f.readline()
def get_default_target_namespace():
if not is_running_in_k8s():
return 'default'
return get_current_k8s_namespace()
namespace = get_default_target_namespace()
print(namespace) | anonymous
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Check GPUs availability | gpus = len(tf.config.experimental.list_physical_devices('GPU'))
if gpus == 0:
print("Model will be trained using CPU")
elif gpus >= 0:
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
tf.config.experimental.list_physical_devices('GPU')
print("Model will be trained using GPU") | Model will be trained using CPU
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Declare Variables | path="cisco-kubeflow-starter-pack/apps/networking/ble-localization/onprem"
BLE_RSSI = pd.read_csv(os.path.join(path, "data/iBeacon_RSSI_Labeled.csv")) #Labeled dataset
# Configure model options
TF_DATA_DIR = os.getenv("TF_DATA_DIR", "/tmp/data/")
TF_MODEL_DIR = os.getenv("TF_MODEL_DIR", "blerssi/")
TF_EXPORT_DIR = os.getenv("TF_EXPORT_DIR", "blerssi/")
TF_MODEL_TYPE = os.getenv("TF_MODEL_TYPE", "DNN")
TF_TRAIN_STEPS = int(os.getenv("TF_TRAIN_STEPS", 5000))
TF_BATCH_SIZE = int(os.getenv("TF_BATCH_SIZE", 128))
TF_LEARNING_RATE = float(os.getenv("TF_LEARNING_RATE", 0.001))
# Feature columns
COLUMNS = list(BLE_RSSI.columns)
FEATURES = COLUMNS[2:]
def make_feature_cols():
input_columns = [tf.feature_column.numeric_column(k) for k in FEATURES]
return input_columns | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
BLERSSI Input Dataset Attribute Informationlocation: The location of receiving RSSIs from ibeacons b3001 to b3013; symbolic values showing the column and row of the location on the map (e.g., A01 stands for column A, row 1).date: Datetime in the format of ‘d-m-yyyy hh:mm:ss’b3001 - b3013: RSSI readings corresponding to the iBeacons; numeric, integers only. | BLE_RSSI.head(10) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Definition of Serving Input Receiver Function | feature_columns = make_feature_cols()
inputs = {}
for feat in feature_columns:
inputs[feat.name] = tf.placeholder(shape=[None], dtype=feat.dtype)
serving_input_receiver_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(inputs) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Train and Save BLE RSSI Model | # Feature columns
COLUMNS = list(BLE_RSSI.columns)
FEATURES = COLUMNS[2:]
LABEL = [COLUMNS[0]]
b3001 = tf.feature_column.numeric_column(key='b3001',dtype=tf.float64)
b3002 = tf.feature_column.numeric_column(key='b3002',dtype=tf.float64)
b3003 = tf.feature_column.numeric_column(key='b3003',dtype=tf.float64)
b3004 = tf.feature_column.numeric_column(key='b3004',dtype=tf.float64)
b3005 = tf.feature_column.numeric_column(key='b3005',dtype=tf.float64)
b3006 = tf.feature_column.numeric_column(key='b3006',dtype=tf.float64)
b3007 = tf.feature_column.numeric_column(key='b3007',dtype=tf.float64)
b3008 = tf.feature_column.numeric_column(key='b3008',dtype=tf.float64)
b3009 = tf.feature_column.numeric_column(key='b3009',dtype=tf.float64)
b3010 = tf.feature_column.numeric_column(key='b3010',dtype=tf.float64)
b3011 = tf.feature_column.numeric_column(key='b3011',dtype=tf.float64)
b3012 = tf.feature_column.numeric_column(key='b3012',dtype=tf.float64)
b3013 = tf.feature_column.numeric_column(key='b3013',dtype=tf.float64)
feature_columns = [b3001, b3002, b3003, b3004, b3005, b3006, b3007, b3008, b3009, b3010, b3011, b3012, b3013]
df_full = pd.read_csv(os.path.join(path, "data/iBeacon_RSSI_Labeled.csv")) #Labeled dataset
# Input Data Preprocessing
df_full = df_full.drop(['date'],axis = 1)
df_full[FEATURES] = (df_full[FEATURES])/(-200)
#Output Data Preprocessing
dict = {'O02': 0,'P01': 1,'P02': 2,'R01': 3,'R02': 4,'S01': 5,'S02': 6,'T01': 7,'U02': 8,'U01': 9,'J03': 10,'K03': 11,'L03': 12,'M03': 13,'N03': 14,'O03': 15,'P03': 16,'Q03': 17,'R03': 18,'S03': 19,'T03': 20,'U03': 21,'U04': 22,'T04': 23,'S04': 24,'R04': 25,'Q04': 26,'P04': 27,'O04': 28,'N04': 29,'M04': 30,'L04': 31,'K04': 32,'J04': 33,'I04': 34,'I05': 35,'J05': 36,'K05': 37,'L05': 38,'M05': 39,'N05': 40,'O05': 41,'P05': 42,'Q05': 43,'R05': 44,'S05': 45,'T05': 46,'U05': 47,'S06': 48,'R06': 49,'Q06': 50,'P06': 51,'O06': 52,'N06': 53,'M06': 54,'L06': 55,'K06': 56,'J06': 57,'I06': 58,'F08': 59,'J02': 60,'J07': 61,'I07': 62,'I10': 63,'J10': 64,'D15': 65,'E15': 66,'G15': 67,'J15': 68,'L15': 69,'R15': 70,'T15': 71,'W15': 72,'I08': 73,'I03': 74,'J08': 75,'I01': 76,'I02': 77,'J01': 78,'K01': 79,'K02': 80,'L01': 81,'L02': 82,'M01': 83,'M02': 84,'N01': 85,'N02': 86,'O01': 87,'I09': 88,'D14': 89,'D13': 90,'K07': 91,'K08': 92,'N15': 93,'P15': 94,'I15': 95,'S15': 96,'U15': 97,'V15': 98,'S07': 99,'S08': 100,'L09': 101,'L08': 102,'Q02': 103,'Q01': 104}
df_full['location'] = df_full['location'].map(dict)
df_train=df_full.sample(frac=0.8,random_state=200)
df_valid=df_full.drop(df_train.index)
location_counts = BLE_RSSI.location.value_counts()
x1 = np.asarray(df_train[FEATURES])
y1 = np.asarray(df_train['location'])
x2 = np.asarray(df_valid[FEATURES])
y2 = np.asarray(df_valid['location'])
def formatFeatures(features):
formattedFeatures = {}
numColumns = features.shape[1]
for i in range(0, numColumns):
formattedFeatures["b"+str(3001+i)] = features[:, i]
return formattedFeatures
trainingFeatures = formatFeatures(x1)
trainingCategories = y1
testFeatures = formatFeatures(x2)
testCategories = y2
# Train Input Function
def train_input_fn():
dataset = tf.data.Dataset.from_tensor_slices((trainingFeatures, y1))
dataset = dataset.repeat(1000).batch(TF_BATCH_SIZE)
return dataset
# Test Input Function
def eval_input_fn():
dataset = tf.data.Dataset.from_tensor_slices((testFeatures, y2))
return dataset.repeat(1000).batch(TF_BATCH_SIZE)
# Provide list of GPUs should be used to train the model
distribution=tf.distribute.experimental.ParameterServerStrategy()
print('Number of devices: {}'.format(distribution.num_replicas_in_sync))
# Configuration of training model
config = tf.estimator.RunConfig(train_distribute=distribution, model_dir=TF_MODEL_DIR, save_summary_steps=100, save_checkpoints_steps=100)
# Build 3 layer DNN classifier
model = tf.estimator.DNNClassifier(hidden_units = [13,65,110],
feature_columns = feature_columns,
model_dir = TF_MODEL_DIR,
n_classes=105, config=config
)
export_final = tf.estimator.FinalExporter(TF_EXPORT_DIR, serving_input_receiver_fn=serving_input_receiver_fn)
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,
max_steps=TF_TRAIN_STEPS)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn,
steps=100,
exporters=export_final,
throttle_secs=1,
start_delay_secs=1)
# Train and Evaluate the model
tf.estimator.train_and_evaluate(model, train_spec, eval_spec) | INFO:tensorflow:ParameterServerStrategy with compute_devices = ('/device:CPU:0',), variable_device = '/device:CPU:0'
Number of devices: 1
INFO:tensorflow:Initializing RunConfig with distribution strategies.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Using config: {'_model_dir': 'blerssi/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 100, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': <tensorflow.python.distribute.parameter_server_strategy.ParameterServerStrategyV1 object at 0x7f93ec2959b0>, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f93ec295c50>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100 or save_checkpoints_secs None.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/canned/head.py:437: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/adagrad.py:76: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into blerssi/model.ckpt.
INFO:tensorflow:loss = 594.5514, step = 0
INFO:tensorflow:Saving checkpoints for 100 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:16Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
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INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:16
INFO:tensorflow:Saving dict for global step 100: accuracy = 0.1478125, average_loss = 3.0438955, global_step = 100, loss = 389.61862
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100: blerssi/model.ckpt-100
INFO:tensorflow:global_step/sec: 74.5088
INFO:tensorflow:loss = 371.56686, step = 100 (1.342 sec)
INFO:tensorflow:Saving checkpoints for 200 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:17Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-200
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
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INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:17
INFO:tensorflow:Saving dict for global step 200: accuracy = 0.16195312, average_loss = 2.8027809, global_step = 200, loss = 358.75595
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 200: blerssi/model.ckpt-200
INFO:tensorflow:global_step/sec: 79.6341
INFO:tensorflow:loss = 342.82297, step = 200 (1.257 sec)
INFO:tensorflow:Saving checkpoints for 300 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:18Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-300
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
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INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:19
INFO:tensorflow:Saving dict for global step 300: accuracy = 0.14773437, average_loss = 2.9204443, global_step = 300, loss = 373.81686
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 300: blerssi/model.ckpt-300
INFO:tensorflow:global_step/sec: 82.1163
INFO:tensorflow:loss = 350.08023, step = 300 (1.217 sec)
INFO:tensorflow:Saving checkpoints for 400 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:19Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-400
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
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INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:20
INFO:tensorflow:Saving dict for global step 400: accuracy = 0.17234375, average_loss = 2.7928438, global_step = 400, loss = 357.484
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 400: blerssi/model.ckpt-400
INFO:tensorflow:global_step/sec: 81.5196
INFO:tensorflow:loss = 330.38446, step = 400 (1.226 sec)
INFO:tensorflow:Saving checkpoints for 500 into blerssi/model.ckpt.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:20Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
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INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:21
INFO:tensorflow:Saving dict for global step 500: accuracy = 0.16554688, average_loss = 2.8326373, global_step = 500, loss = 362.57758
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: blerssi/model.ckpt-500
INFO:tensorflow:global_step/sec: 72.9833
INFO:tensorflow:loss = 309.4389, step = 500 (1.370 sec)
INFO:tensorflow:Saving checkpoints for 600 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:22Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-600
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
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INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:22
INFO:tensorflow:Saving dict for global step 600: accuracy = 0.16882813, average_loss = 2.8005483, global_step = 600, loss = 358.47018
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 600: blerssi/model.ckpt-600
INFO:tensorflow:global_step/sec: 82.3567
INFO:tensorflow:loss = 317.98203, step = 600 (1.214 sec)
INFO:tensorflow:Saving checkpoints for 700 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:23Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-700
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:24
INFO:tensorflow:Saving dict for global step 700: accuracy = 0.18289062, average_loss = 2.8308408, global_step = 700, loss = 362.34763
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 700: blerssi/model.ckpt-700
INFO:tensorflow:global_step/sec: 81.834
INFO:tensorflow:loss = 326.9156, step = 700 (1.222 sec)
INFO:tensorflow:Saving checkpoints for 800 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:24Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-800
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:25
INFO:tensorflow:Saving dict for global step 800: accuracy = 0.15484375, average_loss = 2.9795644, global_step = 800, loss = 381.38425
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 800: blerssi/model.ckpt-800
INFO:tensorflow:global_step/sec: 82.8517
INFO:tensorflow:loss = 326.4861, step = 800 (1.206 sec)
INFO:tensorflow:Saving checkpoints for 900 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:25Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-900
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:26
INFO:tensorflow:Saving dict for global step 900: accuracy = 0.19359376, average_loss = 2.8420234, global_step = 900, loss = 363.779
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 900: blerssi/model.ckpt-900
INFO:tensorflow:global_step/sec: 81.6693
INFO:tensorflow:loss = 288.16187, step = 900 (1.227 sec)
INFO:tensorflow:Saving checkpoints for 1000 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:27Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
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INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:27
INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.18296875, average_loss = 2.8350174, global_step = 1000, loss = 362.88223
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: blerssi/model.ckpt-1000
INFO:tensorflow:global_step/sec: 80.4219
INFO:tensorflow:loss = 305.5712, step = 1000 (1.243 sec)
INFO:tensorflow:Saving checkpoints for 1100 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:28Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:29
INFO:tensorflow:Saving dict for global step 1100: accuracy = 0.18640625, average_loss = 2.840476, global_step = 1100, loss = 363.58093
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1100: blerssi/model.ckpt-1100
INFO:tensorflow:global_step/sec: 83.3038
INFO:tensorflow:loss = 309.84857, step = 1100 (1.199 sec)
INFO:tensorflow:Saving checkpoints for 1200 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:29Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1200
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
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INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:30
INFO:tensorflow:Saving dict for global step 1200: accuracy = 0.20078126, average_loss = 2.8759577, global_step = 1200, loss = 368.1226
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1200: blerssi/model.ckpt-1200
INFO:tensorflow:global_step/sec: 80.6584
INFO:tensorflow:loss = 304.45615, step = 1200 (1.241 sec)
INFO:tensorflow:Saving checkpoints for 1300 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:30Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1300
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
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INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:31
INFO:tensorflow:Saving dict for global step 1300: accuracy = 0.19710937, average_loss = 2.8712735, global_step = 1300, loss = 367.523
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1300: blerssi/model.ckpt-1300
INFO:tensorflow:global_step/sec: 68.2263
INFO:tensorflow:loss = 305.92072, step = 1300 (1.466 sec)
INFO:tensorflow:Saving checkpoints for 1400 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:32Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1400
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:32
INFO:tensorflow:Saving dict for global step 1400: accuracy = 0.15476562, average_loss = 2.8461099, global_step = 1400, loss = 364.30206
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1400: blerssi/model.ckpt-1400
INFO:tensorflow:global_step/sec: 83.3139
INFO:tensorflow:loss = 295.6084, step = 1400 (1.200 sec)
INFO:tensorflow:Saving checkpoints for 1500 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:33Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:34
INFO:tensorflow:Saving dict for global step 1500: accuracy = 0.21453124, average_loss = 2.8982835, global_step = 1500, loss = 370.9803
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1500: blerssi/model.ckpt-1500
INFO:tensorflow:global_step/sec: 81.5076
INFO:tensorflow:loss = 307.38174, step = 1500 (1.227 sec)
INFO:tensorflow:Saving checkpoints for 1600 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:34Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1600
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:35
INFO:tensorflow:Saving dict for global step 1600: accuracy = 0.200625, average_loss = 2.9850085, global_step = 1600, loss = 382.0811
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1600: blerssi/model.ckpt-1600
INFO:tensorflow:global_step/sec: 80.8971
INFO:tensorflow:loss = 290.9291, step = 1600 (1.236 sec)
INFO:tensorflow:Saving checkpoints for 1700 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:36Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1700
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:36
INFO:tensorflow:Saving dict for global step 1700: accuracy = 0.19359376, average_loss = 2.8472593, global_step = 1700, loss = 364.4492
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1700: blerssi/model.ckpt-1700
INFO:tensorflow:global_step/sec: 77.9863
INFO:tensorflow:loss = 302.74707, step = 1700 (1.282 sec)
INFO:tensorflow:Saving checkpoints for 1800 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:37Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1800
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:37
INFO:tensorflow:Saving dict for global step 1800: accuracy = 0.18640625, average_loss = 2.8873806, global_step = 1800, loss = 369.58472
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1800: blerssi/model.ckpt-1800
INFO:tensorflow:global_step/sec: 81.522
INFO:tensorflow:loss = 311.20178, step = 1800 (1.227 sec)
INFO:tensorflow:Saving checkpoints for 1900 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:38Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-1900
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:39
INFO:tensorflow:Saving dict for global step 1900: accuracy = 0.1934375, average_loss = 2.8413296, global_step = 1900, loss = 363.6902
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1900: blerssi/model.ckpt-1900
INFO:tensorflow:global_step/sec: 82.8489
INFO:tensorflow:loss = 294.47797, step = 1900 (1.206 sec)
INFO:tensorflow:Saving checkpoints for 2000 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:39Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:40
INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.21117188, average_loss = 2.896705, global_step = 2000, loss = 370.77823
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: blerssi/model.ckpt-2000
INFO:tensorflow:global_step/sec: 81.545
INFO:tensorflow:loss = 300.31937, step = 2000 (1.226 sec)
INFO:tensorflow:Saving checkpoints for 2100 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:40Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:41
INFO:tensorflow:Saving dict for global step 2100: accuracy = 0.21125, average_loss = 2.9106176, global_step = 2100, loss = 372.55905
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2100: blerssi/model.ckpt-2100
INFO:tensorflow:global_step/sec: 72.1539
INFO:tensorflow:loss = 285.34515, step = 2100 (1.387 sec)
INFO:tensorflow:Saving checkpoints for 2200 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:42Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2200
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:42
INFO:tensorflow:Saving dict for global step 2200: accuracy = 0.17585938, average_loss = 2.9142356, global_step = 2200, loss = 373.02216
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2200: blerssi/model.ckpt-2200
INFO:tensorflow:global_step/sec: 82.1222
INFO:tensorflow:loss = 301.6997, step = 2200 (1.217 sec)
INFO:tensorflow:Saving checkpoints for 2300 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:43Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2300
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:44
INFO:tensorflow:Saving dict for global step 2300: accuracy = 0.218125, average_loss = 2.878163, global_step = 2300, loss = 368.40488
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2300: blerssi/model.ckpt-2300
INFO:tensorflow:global_step/sec: 82.9431
INFO:tensorflow:loss = 308.0114, step = 2300 (1.205 sec)
INFO:tensorflow:Saving checkpoints for 2400 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:44Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2400
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:45
INFO:tensorflow:Saving dict for global step 2400: accuracy = 0.21820313, average_loss = 2.900616, global_step = 2400, loss = 371.27884
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2400: blerssi/model.ckpt-2400
INFO:tensorflow:global_step/sec: 83.301
INFO:tensorflow:loss = 288.26395, step = 2400 (1.201 sec)
INFO:tensorflow:Saving checkpoints for 2500 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:45Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:46
INFO:tensorflow:Saving dict for global step 2500: accuracy = 0.20414062, average_loss = 3.027789, global_step = 2500, loss = 387.557
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2500: blerssi/model.ckpt-2500
INFO:tensorflow:global_step/sec: 82.5175
INFO:tensorflow:loss = 289.87027, step = 2500 (1.212 sec)
INFO:tensorflow:Saving checkpoints for 2600 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:47Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2600
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
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INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:47
INFO:tensorflow:Saving dict for global step 2600: accuracy = 0.19703124, average_loss = 2.8862774, global_step = 2600, loss = 369.4435
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2600: blerssi/model.ckpt-2600
INFO:tensorflow:global_step/sec: 85.1553
INFO:tensorflow:loss = 304.54187, step = 2600 (1.175 sec)
INFO:tensorflow:Saving checkpoints for 2700 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:48Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2700
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
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INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:48
INFO:tensorflow:Saving dict for global step 2700: accuracy = 0.22179687, average_loss = 2.8361683, global_step = 2700, loss = 363.02954
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2700: blerssi/model.ckpt-2700
INFO:tensorflow:global_step/sec: 82.4599
INFO:tensorflow:loss = 286.2304, step = 2700 (1.212 sec)
INFO:tensorflow:Saving checkpoints for 2800 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:49Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2800
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
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INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:50
INFO:tensorflow:Saving dict for global step 2800: accuracy = 0.22179687, average_loss = 2.822359, global_step = 2800, loss = 361.26196
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2800: blerssi/model.ckpt-2800
INFO:tensorflow:global_step/sec: 82.122
INFO:tensorflow:loss = 292.93854, step = 2800 (1.218 sec)
INFO:tensorflow:Saving checkpoints for 2900 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:51Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-2900
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:51
INFO:tensorflow:Saving dict for global step 2900: accuracy = 0.2075, average_loss = 2.9061038, global_step = 2900, loss = 371.9813
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2900: blerssi/model.ckpt-2900
INFO:tensorflow:global_step/sec: 70.3029
INFO:tensorflow:loss = 291.2099, step = 2900 (1.422 sec)
INFO:tensorflow:Saving checkpoints for 3000 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:52Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:52
INFO:tensorflow:Saving dict for global step 3000: accuracy = 0.20398438, average_loss = 2.9259422, global_step = 3000, loss = 374.5206
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3000: blerssi/model.ckpt-3000
INFO:tensorflow:global_step/sec: 80.3511
INFO:tensorflow:loss = 291.8711, step = 3000 (1.244 sec)
INFO:tensorflow:Saving checkpoints for 3100 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:53Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
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INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:54
INFO:tensorflow:Saving dict for global step 3100: accuracy = 0.22523437, average_loss = 2.8671799, global_step = 3100, loss = 366.99902
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3100: blerssi/model.ckpt-3100
INFO:tensorflow:global_step/sec: 82.5228
INFO:tensorflow:loss = 270.925, step = 3100 (1.212 sec)
INFO:tensorflow:Saving checkpoints for 3200 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:54Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3200
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
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INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:55
INFO:tensorflow:Saving dict for global step 3200: accuracy = 0.22523437, average_loss = 2.894812, global_step = 3200, loss = 370.53595
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3200: blerssi/model.ckpt-3200
INFO:tensorflow:global_step/sec: 79.6329
INFO:tensorflow:loss = 294.95923, step = 3200 (1.256 sec)
INFO:tensorflow:Saving checkpoints for 3300 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:55Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3300
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:56
INFO:tensorflow:Saving dict for global step 3300: accuracy = 0.2215625, average_loss = 2.9023647, global_step = 3300, loss = 371.5027
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3300: blerssi/model.ckpt-3300
INFO:tensorflow:global_step/sec: 82.8779
INFO:tensorflow:loss = 299.6723, step = 3300 (1.207 sec)
INFO:tensorflow:Saving checkpoints for 3400 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:57Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3400
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:57
INFO:tensorflow:Saving dict for global step 3400: accuracy = 0.2075, average_loss = 2.8652325, global_step = 3400, loss = 366.74976
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3400: blerssi/model.ckpt-3400
INFO:tensorflow:global_step/sec: 85.4935
INFO:tensorflow:loss = 278.42737, step = 3400 (1.170 sec)
INFO:tensorflow:Saving checkpoints for 3500 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:58Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:04:58
INFO:tensorflow:Saving dict for global step 3500: accuracy = 0.23226562, average_loss = 2.896808, global_step = 3500, loss = 370.7914
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3500: blerssi/model.ckpt-3500
INFO:tensorflow:global_step/sec: 83.4493
INFO:tensorflow:loss = 278.02283, step = 3500 (1.198 sec)
INFO:tensorflow:Saving checkpoints for 3600 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:04:59Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3600
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:00
INFO:tensorflow:Saving dict for global step 3600: accuracy = 0.21460937, average_loss = 2.924043, global_step = 3600, loss = 374.2775
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3600: blerssi/model.ckpt-3600
INFO:tensorflow:global_step/sec: 81.4535
INFO:tensorflow:loss = 279.70343, step = 3600 (1.227 sec)
INFO:tensorflow:Saving checkpoints for 3700 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:00Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3700
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:01
INFO:tensorflow:Saving dict for global step 3700: accuracy = 0.1934375, average_loss = 2.9265563, global_step = 3700, loss = 374.5992
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3700: blerssi/model.ckpt-3700
INFO:tensorflow:global_step/sec: 69.8044
INFO:tensorflow:loss = 289.2801, step = 3700 (1.432 sec)
INFO:tensorflow:Saving checkpoints for 3800 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:02Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3800
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:02
INFO:tensorflow:Saving dict for global step 3800: accuracy = 0.2146875, average_loss = 2.8516412, global_step = 3800, loss = 365.01007
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3800: blerssi/model.ckpt-3800
INFO:tensorflow:global_step/sec: 86.0293
INFO:tensorflow:loss = 288.4405, step = 3800 (1.162 sec)
INFO:tensorflow:Saving checkpoints for 3900 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:03Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-3900
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:03
INFO:tensorflow:Saving dict for global step 3900: accuracy = 0.23226562, average_loss = 2.8733413, global_step = 3900, loss = 367.7877
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3900: blerssi/model.ckpt-3900
INFO:tensorflow:global_step/sec: 81.837
INFO:tensorflow:loss = 274.29977, step = 3900 (1.225 sec)
INFO:tensorflow:Saving checkpoints for 4000 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:04Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:05
INFO:tensorflow:Saving dict for global step 4000: accuracy = 0.23929687, average_loss = 2.8829916, global_step = 4000, loss = 369.02292
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4000: blerssi/model.ckpt-4000
INFO:tensorflow:global_step/sec: 79.7979
INFO:tensorflow:loss = 280.6007, step = 4000 (1.251 sec)
INFO:tensorflow:Saving checkpoints for 4100 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:05Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:06
INFO:tensorflow:Saving dict for global step 4100: accuracy = 0.20398438, average_loss = 2.924492, global_step = 4100, loss = 374.33496
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4100: blerssi/model.ckpt-4100
INFO:tensorflow:global_step/sec: 82.9037
INFO:tensorflow:loss = 292.06015, step = 4100 (1.207 sec)
INFO:tensorflow:Saving checkpoints for 4200 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:07Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4200
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:07
INFO:tensorflow:Saving dict for global step 4200: accuracy = 0.23578125, average_loss = 2.846016, global_step = 4200, loss = 364.29004
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4200: blerssi/model.ckpt-4200
INFO:tensorflow:global_step/sec: 83.7255
INFO:tensorflow:loss = 272.29013, step = 4200 (1.194 sec)
INFO:tensorflow:Saving checkpoints for 4300 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:08Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4300
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
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INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:08
INFO:tensorflow:Saving dict for global step 4300: accuracy = 0.239375, average_loss = 2.8495471, global_step = 4300, loss = 364.74203
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4300: blerssi/model.ckpt-4300
INFO:tensorflow:global_step/sec: 83.2944
INFO:tensorflow:loss = 300.6521, step = 4300 (1.200 sec)
INFO:tensorflow:Saving checkpoints for 4400 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:09Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4400
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:10
INFO:tensorflow:Saving dict for global step 4400: accuracy = 0.22507812, average_loss = 2.8738248, global_step = 4400, loss = 367.84958
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4400: blerssi/model.ckpt-4400
INFO:tensorflow:global_step/sec: 82.7437
INFO:tensorflow:loss = 297.72165, step = 4400 (1.209 sec)
INFO:tensorflow:Saving checkpoints for 4500 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:10Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:11
INFO:tensorflow:Saving dict for global step 4500: accuracy = 0.21453124, average_loss = 3.0025408, global_step = 4500, loss = 384.32523
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4500: blerssi/model.ckpt-4500
INFO:tensorflow:global_step/sec: 67.978
INFO:tensorflow:loss = 287.15585, step = 4500 (1.471 sec)
INFO:tensorflow:Saving checkpoints for 4600 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:12Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4600
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:12
INFO:tensorflow:Saving dict for global step 4600: accuracy = 0.23585938, average_loss = 2.8682337, global_step = 4600, loss = 367.1339
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4600: blerssi/model.ckpt-4600
INFO:tensorflow:global_step/sec: 84.4059
INFO:tensorflow:loss = 273.37143, step = 4600 (1.185 sec)
INFO:tensorflow:Saving checkpoints for 4700 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:13Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4700
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:13
INFO:tensorflow:Saving dict for global step 4700: accuracy = 0.26742187, average_loss = 2.9070816, global_step = 4700, loss = 372.10645
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4700: blerssi/model.ckpt-4700
INFO:tensorflow:global_step/sec: 82.6464
INFO:tensorflow:loss = 280.7273, step = 4700 (1.210 sec)
INFO:tensorflow:Saving checkpoints for 4800 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:14Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4800
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:15
INFO:tensorflow:Saving dict for global step 4800: accuracy = 0.22867188, average_loss = 2.9323123, global_step = 4800, loss = 375.33597
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4800: blerssi/model.ckpt-4800
INFO:tensorflow:global_step/sec: 84.4072
INFO:tensorflow:loss = 282.58746, step = 4800 (1.185 sec)
INFO:tensorflow:Saving checkpoints for 4900 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:15Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-4900
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:16
INFO:tensorflow:Saving dict for global step 4900: accuracy = 0.22859375, average_loss = 2.9506714, global_step = 4900, loss = 377.68594
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4900: blerssi/model.ckpt-4900
INFO:tensorflow:global_step/sec: 83.8655
INFO:tensorflow:loss = 276.4771, step = 4900 (1.192 sec)
INFO:tensorflow:Saving checkpoints for 5000 into blerssi/model.ckpt.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-07-27T12:05:16Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-5000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2020-07-27-12:05:17
INFO:tensorflow:Saving dict for global step 5000: accuracy = 0.24632813, average_loss = 2.9123015, global_step = 5000, loss = 372.7746
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5000: blerssi/model.ckpt-5000
INFO:tensorflow:Performing the final export in the end of training.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Signatures EXCLUDED from export because they cannot be be served via TensorFlow Serving APIs:
INFO:tensorflow:'serving_default' : Classification input must be a single string Tensor; got {'b3001': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=float32>, 'b3002': <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=float32>, 'b3003': <tf.Tensor 'Placeholder_2:0' shape=(?,) dtype=float32>, 'b3004': <tf.Tensor 'Placeholder_3:0' shape=(?,) dtype=float32>, 'b3005': <tf.Tensor 'Placeholder_4:0' shape=(?,) dtype=float32>, 'b3006': <tf.Tensor 'Placeholder_5:0' shape=(?,) dtype=float32>, 'b3007': <tf.Tensor 'Placeholder_6:0' shape=(?,) dtype=float32>, 'b3008': <tf.Tensor 'Placeholder_7:0' shape=(?,) dtype=float32>, 'b3009': <tf.Tensor 'Placeholder_8:0' shape=(?,) dtype=float32>, 'b3010': <tf.Tensor 'Placeholder_9:0' shape=(?,) dtype=float32>, 'b3011': <tf.Tensor 'Placeholder_10:0' shape=(?,) dtype=float32>, 'b3012': <tf.Tensor 'Placeholder_11:0' shape=(?,) dtype=float32>, 'b3013': <tf.Tensor 'Placeholder_12:0' shape=(?,) dtype=float32>}
INFO:tensorflow:'classification' : Classification input must be a single string Tensor; got {'b3001': <tf.Tensor 'Placeholder:0' shape=(?,) dtype=float32>, 'b3002': <tf.Tensor 'Placeholder_1:0' shape=(?,) dtype=float32>, 'b3003': <tf.Tensor 'Placeholder_2:0' shape=(?,) dtype=float32>, 'b3004': <tf.Tensor 'Placeholder_3:0' shape=(?,) dtype=float32>, 'b3005': <tf.Tensor 'Placeholder_4:0' shape=(?,) dtype=float32>, 'b3006': <tf.Tensor 'Placeholder_5:0' shape=(?,) dtype=float32>, 'b3007': <tf.Tensor 'Placeholder_6:0' shape=(?,) dtype=float32>, 'b3008': <tf.Tensor 'Placeholder_7:0' shape=(?,) dtype=float32>, 'b3009': <tf.Tensor 'Placeholder_8:0' shape=(?,) dtype=float32>, 'b3010': <tf.Tensor 'Placeholder_9:0' shape=(?,) dtype=float32>, 'b3011': <tf.Tensor 'Placeholder_10:0' shape=(?,) dtype=float32>, 'b3012': <tf.Tensor 'Placeholder_11:0' shape=(?,) dtype=float32>, 'b3013': <tf.Tensor 'Placeholder_12:0' shape=(?,) dtype=float32>}
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from blerssi/model.ckpt-5000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: blerssi/export/blerssi/temp-b'1595851517'/saved_model.pb
INFO:tensorflow:Loss for final step: 260.41766.
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Define predict function | MODEL_EXPORT_PATH= os.path.join(TF_MODEL_DIR, "export", TF_EXPORT_DIR)
def predict(request):
"""
Define custom predict function to be used by local prediction
and explainer. Set anchor_tabular predict function so it always returns predicted class
"""
# Get model exporter path
for dir in os.listdir(MODEL_EXPORT_PATH):
if re.match('[0-9]',dir):
exported_path=os.path.join(MODEL_EXPORT_PATH,dir)
break
else:
raise Exception("Model path not found")
# Prepare model input data
feature_cols=["b3001", "b3002","b3003","b3004","b3005","b3006","b3007","b3008","b3009","b3010","b3011","b3012","b3013"]
input={'b3001': [], 'b3002': [], 'b3003': [], 'b3004': [], 'b3005': [], 'b3006': [], 'b3007': [], 'b3008': [], 'b3009': [], 'b3010': [], 'b3011': [], 'b3012': [], 'b3013': []}
X=request
if np.ndim(X) != 2:
for i in range(len(X)):
input[feature_cols[i]].append(X[i])
else:
for i in range(len(X)):
for j in range(len(X[i])):
input[feature_cols[j]].append(X[i][j])
# Open a Session to predict
with tf.Session() as sess:
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], exported_path)
predictor= tf.contrib.predictor.from_saved_model(exported_path,signature_def_key='predict')
output_dict= predictor(input)
sess.close()
output={}
output["predictions"]={"probabilities":output_dict["probabilities"].tolist()}
return np.asarray(output['predictions']["probabilities"]) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Initialize and fitTo initialize the explainer, we provide a predict function, a list with the feature names to make the anchors easy to understand. | feature_cols=["b3001", "b3002", "b3003", "b3004", "b3005", "b3006", "b3007", "b3008", "b3009", "b3010", "b3011", "b3012", "b3013"]
explainer = AnchorTabular(predict, feature_cols) | WARNING:tensorflow:From <ipython-input-8-69054218b064>:31: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
INFO:tensorflow:Restoring parameters from blerssi/export/blerssi/1595851517/variables/variables
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
* https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.
INFO:tensorflow:Restoring parameters from blerssi/export/blerssi/1595851517/variables/variables
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Discretize the ordinal features into quartiles. disc_perc is a list with percentiles used for binning | explainer.fit(x1, disc_perc=(25, 50, 75)) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Save Explainer fileSave explainer file with .dill extension. It will be used when creating the InferenceService | EXPLAINER_PATH="explainer"
if not os.path.exists(EXPLAINER_PATH):
os.mkdir(EXPLAINER_PATH)
with open("%s/explainer.dill"%EXPLAINER_PATH, 'wb') as f:
dill.dump(explainer,f) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Create a gatewayCreate a gateway called kubeflow-gateway in namespace anonymous. | gateway=f"""apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
name: kubeflow-gateway
namespace: {namespace}
spec:
selector:
istio: ingressgateway
servers:
- hosts:
- '*'
port:
name: http
number: 80
protocol: HTTP
"""
gateway_spec=yaml.safe_load(gateway)
custom_api.create_namespaced_custom_object(group="networking.istio.io", version="v1alpha3", namespace=namespace, plural="gateways", body=gateway_spec) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Adding a new inference server The list of available inference servers in Seldon Core is maintained in the **seldon-config** configmap, which lives in the same namespace as your Seldon Core operator. In particular, the **predictor_servers** key holds the JSON config for each inference server.[Refer to for more information](https://docs.seldon.io/projects/seldon-core/en/v1.1.0/servers/custom.html) | api_client.patch_namespaced_config_map(name="seldon-config", namespace="kubeflow",pretty=True, body={"data":{"predictor_servers":'{"MLFLOW_SERVER":{"grpc":{"defaultImageVersion":"1.2.1","image":"seldonio/mlflowserver_grpc"},"rest":{"defaultImageVersion":"1.2.1","image":"seldonio/mlflowserver_rest"}},"SKLEARN_SERVER":{"grpc":{"defaultImageVersion":"1.2.1","image":"seldonio/sklearnserver_grpc"},"rest":{"defaultImageVersion":"1.2.1","image":"seldonio/sklearnserver_rest"}},"TENSORFLOW_SERVER":{"grpc":{"defaultImageVersion":"1.2.1","image":"seldonio/tfserving-proxy_grpc"},"rest":{"defaultImageVersion":"1.2.1","image":"seldonio/tfserving-proxy_rest"},"tensorflow":true,"tfImage":"tensorflow/serving:2.1.0"},"XGBOOST_SERVER":{"grpc":{"defaultImageVersion":"1.2.1","image":"seldonio/xgboostserver_grpc"},"rest":{"defaultImageVersion":"1.2.1","image":"seldonio/xgboostserver_rest"}}, "CUSTOM_INFERENCE_SERVER":{"rest":{"defaultImageVersion":"1.0","image":"samba07/blerssi-seldon"}}}'}}) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Seldon Serving DeploymentCreate an **SeldonDeployment** with a blerssi model | pvcname = !(echo $HOSTNAME | sed 's/.\{2\}$//')
pvc = "workspace-"+pvcname[0]
seldon_deploy=f"""apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
name: blerssi
namespace: {namespace}
spec:
name: blerssi
predictors:
- graph:
children: []
implementation: CUSTOM_INFERENCE_SERVER
modelUri: pvc://{pvc}/{MODEL_EXPORT_PATH}
name: blerssi
explainer:
containerSpec:
image: seldonio/alibiexplainer:1.2.2-dev
name: explainer
type: AnchorTabular
modelUri: pvc://{pvc}/{EXPLAINER_PATH}
name: default
replicas: 1
"""
seldon_deploy_spec=yaml.safe_load(seldon_deploy)
custom_api.create_namespaced_custom_object(group="machinelearning.seldon.io", version="v1alpha2", namespace=namespace, plural="seldondeployments", body=seldon_deploy_spec) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Wait for state to become available | status=False
while True:
seldon_status=custom_api.get_namespaced_custom_object_status(group="machinelearning.seldon.io", version="v1alpha2", namespace=namespace, plural="seldondeployments", name=seldon_deploy_spec["metadata"]["name"])
if seldon_status["status"]["state"] == "Available":
status=True
print("Status: %s"%seldon_status["status"]["state"])
if status:
break
print("Status: %s"%seldon_status["status"]["state"])
sleep(30) | Status: Creating
Status: Creating
Status: Available
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Run a Prediction | CLUSTER='ucs' #where your cluster running 'gcp' or 'ucs'
%%bash -s "$CLUSTER" --out NODE_IP
if [ $1 = "ucs" ]
then
echo "$(kubectl get node -o=jsonpath='{.items[0].status.addresses[0].address}')"
else
echo "$(kubectl get node -o=jsonpath='{.items[0].status.addresses[1].address}')"
fi
%%bash --out INGRESS_PORT
INGRESS_GATEWAY="istio-ingressgateway"
echo "$(kubectl -n istio-system get service $INGRESS_GATEWAY -o jsonpath='{.spec.ports[1].nodePort}')" | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Data for prediction | df_full = pd.read_csv(os.path.join(path,'data/iBeacon_RSSI_Unlabeled_truncated.csv')) #Labeled dataset
# Input Data Preprocessing
df_full = df_full.drop(['date'],axis = 1)
df_full = df_full.drop(['location'],axis = 1)
df_full[FEATURES] = (df_full[FEATURES])/(-200)
input_data=df_full.to_numpy()[:1]
input_data
headers={"Content-Type": "application/json"}
def inference_predict(X):
data={"data":{"ndarray":X.tolist()}}
url = f"http://{NODE_IP.strip()}:{INGRESS_PORT.strip()}/seldon/{namespace}/%s/api/v1.0/predictions"%seldon_deploy_spec["metadata"]["name"]
response=requests.post(url, data=json.dumps(data), headers=headers)
probabilities=response.json()['data']['ndarray']
for prob in probabilities:
cls_id=np.argmax(prob)
print("Probability: %s"%prob[cls_id])
print("Class-id: %s"%cls_id)
def explain(X):
if np.ndim(X)==2:
data={"data":{"ndarray":X.tolist()}}
else:
data={"data":{"ndarray":[X.tolist()]}}
url = f"http://{NODE_IP.strip()}:{INGRESS_PORT.strip()}/seldon/{namespace}/%s-explainer/default/api/v1.0/explain"%seldon_deploy_spec["metadata"]["name"]
response=requests.post(url, data=json.dumps(data), headers=headers)
print('Anchor: %s' % (' AND '.join(response.json()['names'])))
print('Coverage: %.2f' % response.json()['coverage'])
inference_predict(input_data) | Probability: 0.6692667603492737
Class-id: 14
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Prediction of the model and explain | explain(input_data) | Anchor: b3009 <= 1.00 AND 0.40 < b3004 <= 1.00 AND 0.39 < b3002 <= 1.00 AND b3012 <= 1.00 AND b3011 <= 1.00 AND b3013 <= 1.00 AND b3006 <= 1.00 AND b3003 <= 1.00 AND b3010 <= 1.00 AND b3005 <= 1.00 AND b3001 <= 1.00 AND b3007 <= 1.00 AND b3008 <= 1.00
Coverage: 0.48
| Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Clean Up Delete a gateway | custom_api.delete_namespaced_custom_object(group="networking.istio.io", version="v1alpha3", namespace=namespace, plural="gateways", name=gateway_spec["metadata"]["name"],body=k8s_client.V1DeleteOptions()) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Delete Seldon Serving Deployment | custom_api.delete_namespaced_custom_object(group="machinelearning.seldon.io", version="v1alpha2", namespace=namespace, plural="seldondeployments", name=seldon_deploy_spec["metadata"]["name"], body=k8s_client.V1DeleteOptions()) | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
Delete model and explainer folders from notebook | !rm -rf $EXPLAINER_PATH
!rm -rf $TF_MODEL_DIR | _____no_output_____ | Apache-2.0 | apps/networking/ble-localization/onprem/seldon/blerssi-seldon.ipynb | Karthik-Git-Sudo786/cisco-kubeflow-starter-pack |
MobileCoin Example WalletThis is an example python client that interacts with `mobilecoind` to manage a MobileCoin wallet.You must start the `mobilecoind` daemon in order to run a wallet. See the mobilecoind README for more information.To run this notebook, make sure you have the requirements installed, and that you have compiled the grpc protos.```cd mobilecoind/clients/python/jupyter./install.shjupyter notebook``` | from mobilecoin import Client | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Start the Mob ClientThe client talks to your local mobilecoind. See the mobilecoind/README.md for information on how to set it up. | client = Client("localhost:4444", ssl=False) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Input Root Entropy for AccountNote: The root entropy is sensitive material. It is used as the seed to create your account keys. Anyone with your root entropy can steal your MobileCoin. | entropy = "4ec2c081e764f4189afba528956c05804a448f55f24cc3d04c9ef7e807a93bcd"
credentials_response = client.get_account_key(bytes.fromhex(entropy)) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Monitor your AccountMonitoring an account means that mobilecoind will persist the transactions that belong to you to a local database. This allows you to retrieve your funds and calculate your balance, as well as to construct and submit transactions.Note: MobileCoin uses accounts and subaddresses for managing funds. You can optionally specify a range of subaddresses to monitor. See mob_client.py for more information. | monitor_id_response = client.add_monitor(credentials_response.account_key) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Check BalanceYou will need to provide a subaddress index. Most people will only use one subaddress, and can default to 0. Exchanges or users who want to generate lots of new public addresses may use multiple subaddresses. | subaddress_index = 0
client.get_balance(monitor_id_response.monitor_id, subaddress_index) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Send a TransactionMobileCoin uses "request codes" to wrap public addresses. See below for how to generate request codes. | address_code = "2nTy8m2VE5UMtfqRf12gEjZmFHKNTDEtNufQZNvE713ytYvdu2kqpbcncHJUSLwmgTCkB56Li9fsGwJF9LRYEQvoQCDzqVQEJETDNQKLzqHCzd"
target_address_response = client.parse_request_code(address_code)
# Construct the transaction
txo_list_response = client.get_unspent_tx_output_list(monitor_id_response.monitor_id, subaddress_index)
outlays = [{
'value': 10,
'receiver': target_address_response.receiver
}]
tx_proposal_response = client.generate_tx(
monitor_id_response.monitor_id,
subaddress_index,
txo_list_response.output_list,
outlays
)
# Send the transaction to consensus validators
client.submit_tx(tx_proposal_response.tx_proposal) | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Public Address (Request Code) | public_address_response = client.get_public_address(monitor_id_response.monitor_id, subaddress_index)
request_code_response = client.create_request_code(public_address_response.public_address)
print(f"Request code = {request_code_response}") | _____no_output_____ | Apache-2.0 | mobilecoind/clients/python/jupyter/wallet.ipynb | MCrank/mobilecoin |
Show me the first lines of the original file | df = pd.read_excel('/tmp/gonzalo_test/aseg.xls')
df.head() | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
Show me the region names containing 'Vent' or 'WM' or 'Hippo' | names = set([each for each in df['StructName'].tolist() \
if 'WM' in each
or 'Vent' in each
or 'Hippo' in each])
names | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
Reshape the table and show me the first lines | df = pd.DataFrame(df[df['StructName'].isin(names)], columns=['subject', 'StructName', 'Volume_mm3'])
df = df.pivot(index='subject', columns='StructName', values='Volume_mm3')
df.head() | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
Save it and success ! | df.to_excel('/tmp/gonzalo_test/aseg_pivot.xls')
from IPython.display import Image
Image(url='http://s2.quickmeme.com/img/c3/c37a6cc5f88867e5387b8787aaf67afc350b3f37f357ed0a3088241488063bce.jpg') | _____no_output_____ | MIT | notebooks/Miscellaneous/Reshaping an Excel table.ipynb | xgrg/alfa |
The effect of temperature and reaction time affects the %yield. Develop a model for %yield in terms of temperature and time | import pandas as mypanda
import numpy as np
from scipy import stats as mystats
import matplotlib.pyplot as myplot
from pandas.plotting import scatter_matrix
from statsmodels.formula.api import ols as myols
from statsmodels.stats.anova import anova_lm
myData=mypanda.read_csv('datasets/Mult_Reg_Yield.csv')
myData
tmp=myData.Temperature
yld =myData.Yield
time=myData.Time | _____no_output_____ | Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
check for relationship now | scatter_matrix(myData)
myplot.show() | C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.
"""Entry point for launching an IPython kernel.
| Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
correlation between xs and y should be high | np.corrcoef(tmp,yld)
np.corrcoef(time,yld)
np.corrcoef(time,tmp)
mymodel=myols("yld ~ time + tmp",myData)
mymodel=mymodel.fit()
mymodel.summary() | C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
"anyway, n=%i" % int(n))
| Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
check p value ==> only time is related to yield | mymodel=myols("yld ~ time ",myData).fit()
mymodel.summary()
pred=mymodel.predict()
res=yld-pred
res
#print(yld, res)
myplot.scatter(yld,pred)
myplot.show()
mystats.probplot(res,plot=myplot)
myplot.show()
mystats.normaltest(res) | C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
"anyway, n=%i" % int(n))
| Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
Implies it is normal | myplot.scatter(time,res)
myplot.show()
myplot.scatter(pred,res)
myplot.show() | _____no_output_____ | Apache-2.0 | Regression_Analysis_Chemical_Process.ipynb | mohan-mj/Regression_Analysis |
**Análise de Dados com Python e Pandas** | # Monta o drive no ambiente virtual permitindo acesso aos arquivos do drive
from google.colab import drive
drive.mount('/content/drive')
# Permite escolher um arquivo da máquina para upload no colab
from google.colab import files
arq = files.upload()
from google.colab import drive
drive.mount('/content/drive') | Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
| MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Importando a biblioteca Pandas* | #importando a biblioteca Pandas
import pandas as pd | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Lendo arquivos* | #Lendo CSV
df = pd.read_csv("/content/drive/MyDrive/Datasets/Gapminder.csv", error_bad_lines=False, sep=";")
#Visualizando as 5 primeiras linhas
df.head() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Renomeando Colunas* | df = df.rename(columns={'country':'Country', 'continent':'Continent', 'year':'Year', 'lifeExp':'LifeExp', 'pop':'Population', 'gdpPercap':'PIB'})
df.head() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
*Trabalhando com Linhas e Colunas do arquivo* | #Quantidade de linhas e colunas dentro do arquivo
df.shape
#Nome das colunas
df.columns | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
#Tipo de dado em ccada coluna
df.dtypes
#Últimas cindo linhas por padrao do arquivo (df.tail(10) → Últimas 10 linhas...)
df.tail()
#Média entre os dados das respectivas linhas e colunas
df.describe() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
|
*Trabalhando com Filtros* | df['Continent'].unique()
Oceania = df.loc[df['Continent'] == 'Oceania']
Oceania.head()
Oceania['Continent'].unique()
df.groupby('Continent')['Country'].nunique()
df.groupby('Year')['LifeExp'].mean()
df['PIB'].mean()
df['PIB'].sum() | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
**Trabalhando com Planilhas de Excel** *Leitura dos Arquivos* | df1 = pd.read_excel("/content/drive/MyDrive/Datasets/Aracaju.xlsx")
df2 = pd.read_excel("/content/drive/MyDrive/Datasets/Fortaleza.xlsx")
df3 = pd.read_excel("/content/drive/MyDrive/Datasets/Natal.xlsx")
df4 = pd.read_excel("/content/drive/MyDrive/Datasets/Recife.xlsx")
df5 = pd.read_excel("/content/drive/MyDrive/Datasets/Salvador.xlsx")
#Juntado todos os arquivos
df = pd.concat([df1, df2, df3, df4, df5])
#Exibindo as 5 primeiras linhas
df.head()
#Exibindo as 5 últimas linhas
df.tail()
df.sample(5)
#Verifincado o tipo de dado de cada coluna
df.dtypes
#Alterando o tipo de dado da coluna LojaID [int64 → object]
df['LojaID'] = df['LojaID'].astype('object')
df.dtypes | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Tratando valores faltantes*** | #Consultando linhas com valores faltantes
df.isnull().sum()
#Apagando as linhas com valores nulos
df.dropna(inplace=True)
#Apagando as linhas com valores nulos com base apenas em 1 coluna
df.dropna(subset=['Vendas'], inplace=True)
#Removendo linhas que estejam com valores faltantes em todas as colunas
df.dropna(how='all', inplace=True) | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Criando novas colunas*** | #Criando a coluna de receita
df['Receita'] = df['Vendas'].mul(df['Qtde'])
df.head()
df.tail()
df['Receita/Venda'] = df['Receita'] / df['Vendas']
df.head()
#Retornando maior receita
df['Receita'].max()
#Retornando a menor receita
df['Receita'].min()
#nlargest
df.nlargest(3,'Receita')
#nsmallest
df.nsmallest(3, 'Receita')
#Agrupamento por cidade
df.groupby('Cidade')['Receita'].sum()
#Ordenando o conjunto de dados
df.sort_values('Receita', ascending=False).head(8) | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Trabalhando com datas*** | #Transfomando a coluna de dataa em tipo inteiro
df['Data'] = df['Data'].astype('int64')
#Verificando o tipo de dado de cada coluna
df.dtypes
#Transformando a coluna de Data em Data
df['Data'] = pd.to_datetime(df['Data'])
df.dtypes
#Agrupamento por ano
df.groupby(df['Data'].dt.year)['Receita'].sum()
#Criado uma nova coluna com o ano
df['Ano_Venda'] = df['Data'].dt.year
df.sample(5)
#Extraindo o mes e o dia
df['mes_venda'], df['dia_venda'] = (df['Data'].dt.month, df['Data'].dt.day)
df.sample(5)
#Retornando a data mais antiga
df['Data'].min()
#Retornanoa data mais nova
df['Data'].max()
#Calculando a diferenca de dias
df['Diferenca_dias'] = df['Data'] - df['Data'].min()
df.sample(5)
#Criando a coluna de trimestre
df['Trimestre'] = df['Data'].dt.quarter
df.sample(5)
#Filtrando as vendas de 2019 do mes de janeiro
vendas_jan_19 = df.loc[(df['Data'].dt.year == 2019) & (df['Data'].dt.month == 1)]
vendas_jan_19 | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
**Visualizacao de Dados** | df['LojaID'].value_counts(ascending=False) | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
***Gráficos*** | #Gráfico de barras
df['LojaID'].value_counts(ascending=False).plot.bar();
#Gráfico de barras horizontais
df['LojaID'].value_counts().plot.barh();
#Gráfco de barras horizonatal
df['LojaID'].value_counts(ascending=True).plot.barh();
#Gráfico de Pizza
df.groupby(df['Data'].dt.year)['Receita'].sum().plot.pie();
#Total de vendas por cidade
df['Cidade'].value_counts()
#Adicionando um título e alterando o nome dos eixos
import matplotlib.pyplot as plt
df['Cidade'].value_counts().plot.bar(title='Total de vendas por Cidade')
plt.xlabel('Cidade')
plt.ylabel('Total de vendas');
#Alterando a cor do gráfico
import matplotlib.pyplot as plt
df['Cidade'].value_counts().plot.bar(title='Total de vendas por Cidade', color='green')
plt.xlabel('Cidade')
plt.ylabel('Total de vendas');
#Editando o Estilo
plt.style.use('ggplot')
df.groupby(df['mes_venda'])['Qtde'].sum().plot(title = 'Total de Vendas')
plt.xlabel('Mes')
plt.ylabel('Venda')
plt.legend();
df.groupby(df['mes_venda'])['Qtde'].sum()
#Selecionando apenas as vendas de 2019
df_2019 = df[df['Ano_Venda'] == 2019]
df_2019
#Total vendidos por mes
df_2019.groupby(df_2019['mes_venda'])['Qtde'].sum().plot(marker = 'v')
plt.xlabel('Mes')
plt.ylabel('Total de Produtos Vendidos')
plt.legend();
#Histograma
plt.hist(df['Qtde'], color='darkturquoise');
plt.scatter(x=df_2019['dia_venda'], y = df_2019['Receita']);
#Salvando em png
df_2019.groupby(df_2019['mes_venda'])['Qtde'].sum().plot(marker = 'v')
plt.title('Quantidade de produtos vendidos x mes')
plt.xlabel('Mes')
plt.ylabel('Total de Produtos Vendidos')
plt.legend()
plt.savefig('grafico Qtde x mes.png'); | _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
**Análise Exploratória** | plt.style.use('seaborn')
#Upload de arquivo
from google.colab import files
arq = files.upload()
#Criando nosso DataFrame
df = pd.read_excel("/content/drive/MyDrive/Datasets/AdventureWorks.xlsx")
df.head()
#Quantidade de linhas e colunas
df.shape
#Verificando os tipos de dados
df.dtypes
#Qual a Receita total?
df['Valor Venda'].sum()
#Qual o Custo Total?
df['Custo'] = df['Custo Unitário'].mul(df['Quantidade']) #Criando a coluna de custo
df.head(1)
#Qual o custo Total?
round(df['Custo'].sum(), 2)
#Agora que temos a receita, custo e total, podemos achar o Lucro Toal
#Vamos criar uma coluna de Lucro que será Receia -Custo
df['Lucro'] = df['Valor Venda'] - df['Custo']
df.head(1)
#Total Lucro
round(df['Lucro'].sum(), 2)
#Criando uma coluna com o total de dias para enviar o produto
df['Tempo_envio'] = df['Data Envio'] - df['Data Venda']
df.head(1)
#extraindo apenas os dias
df['Tempo_envio'] = (df['Data Envio'] - df['Data Venda']).dt.days
df.head(1)
#Verificando o tipo de coluna Tempo_envio
df['Tempo_envio'].dtype
#Média de tempo de envio por Marca
df.groupby('Marca')['Tempo_envio'].mean()
#Vaerificando se temos dados faltantes
df.isnull().sum()
#Agrupar por ano e Marca
df.groupby([df['Data Venda'].dt.year, 'Marca'])['Lucro'].sum()
#resetando o index
lucro_ano = df.groupby([df['Data Venda'].dt.year, 'Marca'])['Lucro'].sum().reset_index()
lucro_ano
#Qual o total de produtos vendidos
df.groupby('Produto')['Quantidade'].sum().sort_values(ascending=False)
#Gráfico Total de Produtos vendidos
df.groupby('Produto')['Quantidade'].sum().sort_values(ascending=True).plot.barh(title='Total Produtos Vendidos')
plt.xlabel('Total')
plt.ylabel('Produto');
#Selecionando apenas as vendas de 2009
df_2009 = df[df['Data Venda'].dt.year == 2009]
df_2009.head()
df_2009.groupby(df_2009["Data Venda"].dt.month)["Lucro"].sum().plot(title="Lucro x Mês")
plt.xlabel("Mês")
plt.ylabel("Lucro");
df_2009.groupby("Marca")["Lucro"].sum().plot.bar(title="Lucro x Marca")
plt.xlabel("Marca")
plt.ylabel("Lucro")
plt.xticks(rotation='horizontal');
df["Tempo_envio"].describe()
#Gráfico de Boxplot
plt.boxplot(df["Tempo_envio"]);
#Histograma
plt.hist(df["Tempo_envio"]);
#Tempo mínimo de envio
df["Tempo_envio"].min()
#Tempo máximo de envio
df['Tempo_envio'].max()
#Identificando o Outlier
df[df["Tempo_envio"] == 20]
df.to_csv('Project Python_Pandas.csv', index=False)
| _____no_output_____ | MIT | pandasProjectCognizant/project_python_Pandas.ipynb | luizpavanello/cognizant_bootcamp_DIO |
Aerospike Java Client – Advanced Collection Data Types*Last updated: June 22, 2021*The goal of this tutorial is to highlight the power of working with [collection data types (CDTs)]("https://docs.aerospike.com/docs/guide/cdt.html") in Aerospike. It covers the following topics:1. Setting [contexts (CTXs)]("https://docs.aerospike.com/docs/guide/cdt-context.html") to apply operations to nested Maps and Lists.2. Showing the return type options provided by CDT get/read operations.3. Highlighting how policies shape application transactions.This [Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html) requires the Aerospike Database running locally with Java kernel and Aerospike Java Client. To create a Docker container that satisfies the requirements and holds a copy of these notebooks, visit the [Aerospike Notebooks Repo](https://github.com/aerospike-examples/interactive-notebooks). PrerequisitesThis Notebook builds on the material in the following notebooks:1. [Working with Lists]("./java-working_with_lists.ipynb") 2. [Working with Maps]("./java-working_with_lists.ipynb")3. [Introduction to Transactions]("./java-intro_to_transactions.ipynb")It uses examples based on those from [Modeling Using Lists](./java-modeling_using_lists.ipynb) and Working with Maps. If any of the following is confusing, please refer to a relevant notebook. Notebook Setup Import Jupyter Java Integration Make it easier to work with Java in Jupyter. | import io.github.spencerpark.ijava.IJava;
import io.github.spencerpark.jupyter.kernel.magic.common.Shell;
IJava.getKernelInstance().getMagics().registerMagics(Shell.class); | _____no_output_____ | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Start AerospikeEnsure Aerospike Database is running locally. | %sh asd | _____no_output_____ | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Download the Aerospike Java ClientAsk Maven to download and install the project object model (POM) of the Aerospike Java Client. | %%loadFromPOM
<dependencies>
<dependency>
<groupId>com.aerospike</groupId>
<artifactId>aerospike-client</artifactId>
<version>5.0.0</version>
</dependency>
</dependencies> | _____no_output_____ | MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Start the Aerospike Java Client and ConnectCreate an instance of the Aerospike Java Client, and connect to the demo cluster.The default cluster location for the Docker container is *localhost* port *3000*. If your cluster is not running on your local machine, modify *localhost* and *3000* to the values for your Aerospike cluster. | import com.aerospike.client.AerospikeClient;
AerospikeClient client = new AerospikeClient("localhost", 3000);
System.out.println("Initialized the client and connected to the cluster."); | Initialized the client and connected to the cluster.
| MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Create CDT Data, Put into Aerospike, and Print It | import com.aerospike.client.Key;
import com.aerospike.client.Bin;
import com.aerospike.client.policy.ClientPolicy;
import com.aerospike.client.Record;
import com.aerospike.client.Operation;
import com.aerospike.client.Value;
import com.aerospike.client.cdt.ListOperation;
import com.aerospike.client.cdt.ListPolicy;
import com.aerospike.client.cdt.ListOrder;
import com.aerospike.client.cdt.ListWriteFlags;
import com.aerospike.client.cdt.MapOperation;
import com.aerospike.client.cdt.MapPolicy;
import com.aerospike.client.cdt.MapOrder;
import com.aerospike.client.cdt.MapWriteFlags;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
// Create whale migration list of tuples.
ArrayList<Value> whaleMigration0 = new ArrayList<Value>();
whaleMigration0.add(Value.get(1420));
whaleMigration0.add(Value.get("beluga whale"));
whaleMigration0.add(Value.get("Beaufort Sea"));
whaleMigration0.add(Value.get("Bering Sea"));
ArrayList<Value> whaleMigration1 = new ArrayList<Value>();
whaleMigration1.add(Value.get(13988));
whaleMigration1.add(Value.get("gray whale"));
whaleMigration1.add(Value.get("Baja California"));
whaleMigration1.add(Value.get("Chukchi Sea"));
ArrayList<Value> whaleMigration2 = new ArrayList<Value>();
whaleMigration2.add(Value.get(1278));
whaleMigration2.add(Value.get("north pacific right whale"));
whaleMigration2.add(Value.get("Japan"));
whaleMigration2.add(Value.get("Sea of Okhotsk"));
ArrayList<Value> whaleMigration3 = new ArrayList<Value>();
whaleMigration3.add(Value.get(5100));
whaleMigration3.add(Value.get("humpback whale"));
whaleMigration3.add(Value.get("Columbia"));
whaleMigration3.add(Value.get("Antarctic Peninsula"));
ArrayList<Value> whaleMigration4 = new ArrayList<Value>();
whaleMigration4.add(Value.get(3100));
whaleMigration4.add(Value.get("southern hemisphere blue whale"));
whaleMigration4.add(Value.get("Corcovado Gulf"));
whaleMigration4.add(Value.get("The Galapagos"));
ArrayList<Value> whaleMigration = new ArrayList<Value>();
whaleMigration.add(Value.get(whaleMigration0));
whaleMigration.add(Value.get(whaleMigration1));
whaleMigration.add(Value.get(whaleMigration2));
whaleMigration.add(Value.get(whaleMigration3));
whaleMigration.add(Value.get(whaleMigration4));
// Create Map of Whale Observations
HashMap <Value, Value> mapObs = new HashMap <Value, Value>();
HashMap <String, Integer> mapCoords0 = new HashMap <String, Integer>();
mapCoords0.put("lat", -85);
mapCoords0.put("long", -130);
HashMap <String, Integer> mapCoords1 = new HashMap <String, Integer>();
mapCoords1.put("lat", -25);
mapCoords1.put("long", -50);
HashMap <String, Integer> mapCoords2 = new HashMap <String, Integer>();
mapCoords2.put("lat", 35);
mapCoords2.put("long", 30);
mapObs.put(Value.get(13456), Value.get(mapCoords1));
mapObs.put(Value.get(14567), Value.get(mapCoords2));
mapObs.put(Value.get(12345), Value.get(mapCoords0));
// Put data in Aerospike, get the data, and print it
String nestedCDTSetName = "nestedset1";
String nestedCDTNamespaceName = "test";
Integer whaleMigrationWriteFlags = ListWriteFlags.ADD_UNIQUE
| ListWriteFlags.NO_FAIL
| ListWriteFlags.PARTIAL;
ListPolicy whaleMigrationPolicy = new ListPolicy(ListOrder.UNORDERED, whaleMigrationWriteFlags);
MapPolicy mapObsPolicy = new MapPolicy(MapOrder.KEY_ORDERED, MapWriteFlags.DEFAULT);
Integer whaleKeyName = 2;
String listWhaleBinName = "listwhalebin";
String mapObsBinName = "mapobsbin";
Bin bin1 = new Bin(listWhaleBinName, whaleMigration);
Key whaleKey = new Key(nestedCDTNamespaceName, nestedCDTSetName, whaleKeyName);
Record putDataIn = client.operate(client.writePolicyDefault, whaleKey,
Operation.put(bin1),
MapOperation.putItems(mapObsPolicy, mapObsBinName, mapObs)
);
System.out.println(listWhaleBinName + ": " + whaleMigration + "\n\n" +
mapObsBinName + ": " + mapObs ); | listwhalebin: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]
mapobsbin: {13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 12345={lat=-85, long=-130}}
| MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Using Contexts (CTXs) to work with Nested CDTsWhat are Nested CDTs and CTXs? What is a Nested CDT?The primary use case of Key-Value Stores, like Aerospike Database, is to store document-oriented data, like a JSON map. As document-oriented data grows organically, it is common for one CDT (list or map) to contain another CDT. Does the application need a list in a map in a list in a map? Aerospike fully supports nesting CDTs, so that’s no problem. What is a Context?A Context (CTX) is a reference to a nested CDT, a List or Map that is stored in a List or Map somewhere in an Aerospike Bin. All [List](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/ListOperation.html) and [Map Operations](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/MapOperation.html) accept an optional CTX argument. Any CTX argument must refer to data of the type supported by the operation. The most common ways to access a CTX are to look up a Map CTX directly by its key within the Bin and to drill down within a List or Map by index, rank or value. A CTX can also be created within a List or Map. For more details, see the [CTX APIs](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/CDT.html). Look up a Map CTX in a Bin by MapkeyUse the `mapKey` method to look up a CTX in a Map directly by mapkey. This works for a Map anywhere in a Bin.The following is an example of finding a Map CTX in a Bin by Mapkey: | import com.aerospike.client.cdt.CTX;
import com.aerospike.client.cdt.MapReturnType;
Integer lookupMapKey = 14567;
String latKeyName = "lat";
Record whaleSightings = client.operate(client.writePolicyDefault, whaleKey,
MapOperation.getByKey(mapObsBinName, Value.get(latKeyName), MapReturnType.VALUE, CTX.mapKey(Value.get(lookupMapKey)))
);
System.out.println(mapObsBinName + ": " + mapObs );
System.out.println("The " + latKeyName + " of sighting at timestamp " + lookupMapKey + ": " + whaleSightings.getValue(mapObsBinName)); | mapobsbin: {13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 12345={lat=-85, long=-130}}
The lat of sighting at timestamp 14567: 35
| MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
Drill down into a List or MapHere are the options to drill down into a CDT.Drilling down to a CTX in a List:* `listIndex`: Lookup list by index offset.* `listRank`: Lookup list by rank.* `listValue`: Lookup list by value.Drilling down to a CTX in a Map: * `mapIndex`: Lookup map by index offset.* `mapRank`: Lookup map by rank.* `mapValue`: Lookup map by value.The following is an example of drilling down within a List and Map CTX: | import com.aerospike.client.cdt.ListReturnType;
// CDT Drilldown Values
Integer drilldownIndex = 2;
Integer drilldownRank = 1;
Value listDrilldownValue = Value.get(whaleMigration1);
Value mapDrilldownValue = Value.get(mapCoords0);
// Variables to access parts of the selected CDT.
Integer getIndex = 1;
Record theRecord = client.get(null, whaleKey);
Record drilldown = client.operate(client.writePolicyDefault, whaleKey,
ListOperation.getByIndex(listWhaleBinName, getIndex, MapReturnType.VALUE, CTX.listIndex(drilldownIndex)),
ListOperation.getByIndex(listWhaleBinName, getIndex, MapReturnType.VALUE, CTX.listRank(drilldownRank)),
ListOperation.getByIndex(listWhaleBinName, getIndex, MapReturnType.VALUE, CTX.listValue(listDrilldownValue)),
MapOperation.getByIndex(mapObsBinName, getIndex, MapReturnType.VALUE, CTX.mapIndex(drilldownIndex)),
MapOperation.getByIndex(mapObsBinName, getIndex, MapReturnType.VALUE, CTX.mapRank(drilldownRank)),
MapOperation.getByIndex(mapObsBinName, getIndex, MapReturnType.VALUE, CTX.mapValue(mapDrilldownValue))
);
List<?> returnWhaleList = drilldown.getList(listWhaleBinName);
List<?> returnObsList = drilldown.getList(mapObsBinName);
System.out.println("The whale migration list is: " + theRecord.getValue(listWhaleBinName) + "\n");
System.out.println("The whale name from the CTX selected by index " + drilldownIndex + ": " + returnWhaleList.get(0));
System.out.println("The whale name from the CTX selected by rank " + drilldownRank + ": " + returnWhaleList.get(1));
System.out.println("The whale name from the CTX selected by value " + listDrilldownValue + ": " + returnWhaleList.get(2) + "\n\n");
System.out.println("The observation map is: " + theRecord.getValue(mapObsBinName) + "\n");
System.out.println("The longitude of the observation from the CTX selected by index " + drilldownIndex + ": " + returnObsList.get(0));
System.out.println("The longitude of the observation from the CTX selected by rank " + drilldownRank + ": " + returnObsList.get(1));
System.out.println("The longitude of the observation from the CTX selected by value " + mapDrilldownValue + ": " + returnObsList.get(2));
| The whale migration list is: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]
The whale name from the CTX selected by index 2: north pacific right whale
The whale name from the CTX selected by rank 1: beluga whale
The whale name from the CTX selected by value [13988, gray whale, Baja California, Chukchi Sea]: gray whale
The observation map is: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}}
The longitude of the observation from the CTX selected by index 2: 30
The longitude of the observation from the CTX selected by rank 1: -50
The longitude of the observation from the CTX selected by value {lat=-85, long=-130}: -130
| MIT | notebooks/java/java-advanced_collection_data_types.ipynb | markprincely/interactive-notebooks |
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