hartikainen

import pickle

import tensorflow as tf


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

hartikainen

import pickle

import tensorflow as tf


def main():
    model_1 = tf.keras.Sequential((
        tf.keras.layers.Dense(16, activation='relu'),
        tf.keras.layers.Dense(1, activation='linear'),
    ))

hartikainen

import pickle

import tensorflow as tf


def main():
    x = tf.keras.layers.Input((3, ))
    y = tf.keras.layers.Dense(5)(x)
    model_1 = tf.keras.Model(x, y)

hartikainen

from timeit import time

import tensorflow as tf


def test_fake_data():
    matrices = [
        tf.random.uniform((6, 6), -1.0, 1.0)
        for i in range(4)
    ]

hartikainen

import tensorflow as tf

batch_size = 32

data = tf.random.uniform((1024, 4))
model = tf.keras.Sequential((
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(2, activation='linear'),
))

hartikainen

import tensorflow as tf
import tensorflow_probability as tfp


def main():
    x = tf.Variable(0.5)
    y = tfp.util.DeferredTensor(x, tf.exp)
    print(y.numpy())  # y.transform_fn(y.variables).numpy() works.

hartikainen

import random
import time

import tensorflow as tf
from ray import tune
from wandb.ray import WandbLogger


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

hartikainen

import random
import time

import tensorflow as tf
from ray import tune
from wandb.ray import WandbLogger


class MyLogger(tune.logger.Logger):
    def _init(self):

hartikainen

#!/bin/bash

pid=$1
me="$(basename $0)($$):"
if [ -z "$pid" ]
then
    echo "${me} a PID is required as an argument" >&2
    exit 2
fi

hartikainen

import numpy as np
import matplotlib
matplotlib.use('TKAgg')
from matplotlib import pyplot as plt
from matplotlib import animation

from .data import WALLS, TRAJECTORIES


# First set up the figure, the axis, and the plot element we want to animate