StevenReitsma

import mlflow

# Log parameters (key-value pairs)
mlflow.log_param("num_dimensions", 8)
mlflow.log_param("regularization", 0.1)

# Log a metric; metrics can be updated throughout the run
mlflow.log_metric("accuracy", 0.1)
...
mlflow.log_metric("accuracy", 0.45)

StevenReitsma

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StevenReitsma

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StevenReitsma

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StevenReitsma

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StevenReitsma

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StevenReitsma

def RosenbrockOpt(optimizer,MAX_EPOCHS = 4000, MAX_STEP = 100):
   '''
   returns distance of each step*MAX_STEP w.r.t minimum (1,1)
   '''
   x1_data = tf.Variable(initial_value=tf.random_uniform([1], minval=-3, maxval=3,seed=0),name='x1')
   x2_data = tf.Variable(initial_value=tf.random_uniform([1], minval=-3, maxval=3,seed=1), name='x2')

   y = tf.add(tf.pow(tf.subtract(1.0, x1_data), 2.0),
   tf.multiply(100.0, tf.pow(tf.subtract(x2_data, tf.pow(x1_data, 2.0)), 2.0)), 'y')

StevenReitsma

class AddSign(optimizer.Optimizer):
    """Implementation of AddSign.
    See [Bello et. al., 2017](https://arxiv.org/abs/1709.07417)
    @@__init__
    """

    def __init__(self, learning_rate=1.001,alpha=0.01,beta=0.5, use_locking=False, name="AddSign"):
        super(AddSign, self).__init__(use_locking, name)
        self._lr = learning_rate
        self._alpha = alpha

StevenReitsma

class PowerSign(optimizer.Optimizer):
    """Implementation of PowerSign.
    See [Bello et. al., 2017](https://arxiv.org/abs/1709.07417)
    @@__init__
    """
    def __init__(self, learning_rate=0.001,alpha=0.01,beta=0.5, use_locking=False, name="PowerSign"):
        super(PowerSign, self).__init__(use_locking, name)
        self._lr = learning_rate
        self._alpha = alpha
        self._beta = beta

StevenReitsma

# This class defines the API to add Ops to train a model. 
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.framework import ops
from tensorflow.python.training import optimizer
import tensorflow as tf