hartikainen

const assert = require('assert');
const fs = require('fs');
const stream = require('stream');

const _ = require('lodash');
const fastCsv = require('fast-csv');

const { metrohash64 } = require('metrohash');
const {
  Bool, Utf8, Int32, Int64, Float32, Float64, Struct, Dictionary,

hartikainen

import argparse
import datetime
import itertools
import os

import numpy as np
import ray
from ray import tune
import tensorflow as tf
import tree

hartikainen

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


from experiments import load_results


TASKS = [

hartikainen

# NOTE(hartikainen): Might want to check that the working tree is clean.
commit_sha="$(cat ./name_rev.txt | awk -F' ' '{print $1}')"
git checkout "${commit_sha}"

echo "Applying patch: $(cat ./patch.diff)"
git apply patch.diff

hartikainen

import random
import time

import tensorflow as tf
from ray import tune


class ExperimentRunner(tune.Trainable):
    def _setup(self, variant):
        self.timestep = 0

hartikainen

import tensorflow as tf


class TestModule(tf.Module):
    def __init__(self, value):
        self.variable = tf.Variable(value)


module_1 = TestModule(value=9000)
tf.saved_model.save(module_1, "./foo")

hartikainen

import tensorflow as tf
import tensorflow_probability as tfp

from softlearning.utils.tensorflow import nest


@tf.function(experimental_relax_shapes=True)
def another_test_function(inputs):
    outputs = {'old': inputs, 'new': inputs + 100}
    return outputs

hartikainen

import gym
import tensorflow as tf
from sklearn import preprocessing

from softlearning.utils.tensorflow import cast_and_concat


feature_fn = preprocessing.PolynomialFeatures(2)

hartikainen

import tensorflow as tf


class TestModule(tf.Module):
    def __init__(self, model):
        self.model = model


x = tf.keras.layers.Input((3, ))
y = tf.keras.layers.Dense(5)(x)

hartikainen

import tensorflow as tf


class TestModule(tf.Module):
    def __init__(self, value, model):
        self.value = value
        self.model = model


def main():