VikingPenguinYT

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.contrib.distributions import Bernoulli


class VariationalDense:
    """Variational Dense Layer Class"""
    def __init__(self, n_in, n_out, model_prob, model_lam):
        self.model_prob = model_prob

kdubovikov

%%cython 
import numpy as np
cimport numpy as np
cimport cython # so we can use cython decorators
from cpython cimport bool # type annotation for boolean

# disable index bounds checking and negative indexing for speedups
@cython.wraparound(False)
@cython.boundscheck(False)
cdef cython_get_sample(np.ndarray arr, arr_len, n_iter, int sample_size, 

franktoffel

# coding: utf-8

'''The following code reproduces an example of the paper:
'The Complex-Step Derivative Approximation'
by Joaquim R. R. A. MARTINS, Peter STURDZA and Juan J. Alonso published in 2003.

License: MIT
Author: FJ Navarro Brull
'''

Breta01

avloss

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

poolio

falcondai

import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_nn_ops

@ops.RegisterGradient("GuidedRelu")
def _GuidedReluGrad(op, grad):
    return tf.select(0. < grad, gen_nn_ops._relu_grad(grad, op.outputs[0]), tf.zeros(grad.get_shape()))

if __name__ == '__main__':
    with tf.Session() as sess: