lemonzi

import tensorflow as tf
from tensorflow.contrib import distributions

# These are your input features, a random tensor.
# There are 128 examples per batch, 1000 time-steps, one observation.
# If your code doesn't use batches, remove the first dimension.
features = tf.random_normal([128, 1000, 1])

# Put your networks here
# policy_mu, policy_sigma = ...

lemonzi

import numpy as np
import scipy.signal
import tensorflow as tf
import tensorify


@tensorify.tensorflow_op(tf.float32)
def discount(x, gamma=1.0, axis=0):
    y = scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=axis)[::-1]
    return y.astype(np.float32)

lemonzi

"""Decorators that make TensorFlow programming a bit easier.

Quim Llimona, 2017.
"""

import functools
import tensorflow as tf

def add_name_scope(name=None):
    """Creates a name_scope that contains all ops created by the function.

lemonzi

#/bin/bash
git clone https://github.com/lemonzi/sphero-osc
cd sphero-osc
npm install

# This is for running it
node .