diff --git a/README.md b/README.md index 9720d15..2b8cfe9 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,58 @@ -# diffGrad +# [diffGrad: An Optimization Method for Convolutional Neural Networks](https://ieeexplore.ieee.org/document/8939562) -diffGrad: An Optimization Method for Convolutional Neural Networks + The PyTorch implementation of diffGrad can be found in [torch-optimizer](https://pypi.org/project/torch-optimizer/#diffgrad) and can easily be used by following. + +## How to use -IEEE Transactions on Neural Networks and Learning Systems, 2019 +
pip install torch-optimizer
 
-Abstract—Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which function has the steepest rate of change. The main problem with basic SGD is to change by equal sized steps for all parameters, irrespective of gradient behavior. Hence, an efficient way of deep network optimization is to make adaptive step sizes for each parameter. Recently, several attempts have been made to improve gradient descent methods such as AdaGrad, AdaDelta, RMSProp and Adam. These methods rely on the square roots of exponential moving averages of squared past gradients. Thus, these methods do not take the advantage of local change in gradients. In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i.e., diffGrad). In the proposed diffGrad optimization technique, the step size is adjusted for each parameter in such a way that it should have a larger step size for faster gradient changing parameters and lower step size for lower gradient changing parameters. The convergence analysis is done using the regret bound approach of online learning framework. Rigorous analysis is made in this paper over three synthetic complex non-convex functions. The image categorization experiments are also conducted over the CIFAR10 and CIFAR100 datasets to observe the performance of diffGrad with respect to the state-of-the-art optimizers such as SGDM, AdaGrad, AdaDelta, RMSProp, AMSGrad, and Adam. The residual unit (ResNet) based Convolutional Neural Networks (CNN) architecture is used in the experiments. The experiments show that diffGrad outperforms the other optimizers. Also, we showed that diffGrad performs uniformly well on network using different activation functions.
+import torch_optimizer as optimizer
 
-IEEE TNNLS version: https://ieeexplore.ieee.org/document/8939562
+# model = ...
+optimizer = optimizer.DiffGrad(
+    model.parameters(),
+    lr= 1e-3,
+    betas=(0.9, 0.999),
+    eps=1e-8,
+    weight_decay=0,
+)
+optimizer.step()
+
-arXiv version: https://arxiv.org/abs/1909.11015 +## Issues + It is recommended to use diffGrad_v2.py which fixes [an issue](https://github.com/shivram1987/diffGrad/issues/2) in diffGrad.py. + + It is also recommended to refer [arXiv version](https://arxiv.org/abs/1909.11015) for the updated results. + +## Abstract + +Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which function has the steepest rate of change. The main problem with basic SGD is to change by equal sized steps for all parameters, irrespective of gradient behavior. Hence, an efficient way of deep network optimization is to make adaptive step sizes for each parameter. Recently, several attempts have been made to improve gradient descent methods such as AdaGrad, AdaDelta, RMSProp and Adam. These methods rely on the square roots of exponential moving averages of squared past gradients. Thus, these methods do not take the advantage of local change in gradients. In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i.e., diffGrad). In the proposed diffGrad optimization technique, the step size is adjusted for each parameter in such a way that it should have a larger step size for faster gradient changing parameters and lower step size for lower gradient changing parameters. The convergence analysis is done using the regret bound approach of online learning framework. Rigorous analysis is made in this paper over three synthetic complex non-convex functions. The image categorization experiments are also conducted over the CIFAR10 and CIFAR100 datasets to observe the performance of diffGrad with respect to the state-of-the-art optimizers such as SGDM, AdaGrad, AdaDelta, RMSProp, AMSGrad, and Adam. The residual unit (ResNet) based Convolutional Neural Networks (CNN) architecture is used in the experiments. The experiments show that diffGrad outperforms the other optimizers. Also, we showed that diffGrad performs uniformly well on network using different activation functions. + +## Citation -All experiments are perfomed using following framework: https://github.com/kuangliu/pytorch-cifar -Please cite this work as: S.R. Dubey, S. Chakraborty, S.K. Roy, S. Mukherjee, S.K. Singh, and B.B. Chaudhuri. diffGrad: An Optimization Method for Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 2019. +If you use this code in your research, please cite as: + + @article{dubey2019diffgrad, + title={diffGrad: An Optimization Method for Convolutional Neural Networks}, + author={Dubey, Shiv Ram and Chakraborty, Soumendu and Roy, Swalpa Kumar and Mukherjee, Snehasis and Singh, Satish Kumar and Chaudhuri, Bidyut Baran}, + journal={IEEE Transactions on Neural Networks and Learning Systems}, + volume={31}, + no={11}, + pp.={4500 - 4511}, + year={2020}, + publisher={IEEE} + } + +## Acknowledgement + +All experiments are perfomed using following framework: https://github.com/kuangliu/pytorch-cifar +## License -Copyright (©2019): Shiv Ram Dubey, Indian Institute of Information Technology, Sri City, Chittoor, A.P., India +Copyright (©2019): Shiv Ram Dubey, Indian Institute of Information Technology, Sri City, Chittoor, A.P., India. Released under the MIT License. See [LICENSE](LICENSE) for details. diff --git a/diffGrad.py b/diffGrad.py index f2649ad..4408b4a 100644 --- a/diffGrad.py +++ b/diffGrad.py @@ -1,3 +1,4 @@ +# This diffGrad implementation has a bug. Use diffGrad_v2.py. import math import torch from torch.optim.optimizer import Optimizer @@ -96,9 +97,9 @@ def step(self, closure=None): # compute diffgrad coefficient (dfc) diff = abs(previous_grad - grad) dfc = 1. / (1. + torch.exp(-diff)) - state['previous_grad'] = grad - - # update momentum with dfc + state['previous_grad'] = grad # used in paper but has the bug that previous grad is overwritten with grad and diff becomes always zero. Fixed in the next line. + + # update momentum with dfc exp_avg1 = exp_avg * dfc step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 diff --git a/diffGrad_Regression_Loss.ipynb b/diffGrad_Regression_Loss.ipynb new file mode 100644 index 0000000..9d4590f --- /dev/null +++ b/diffGrad_Regression_Loss.ipynb @@ -0,0 +1,312 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "colab": { + "name": "diffGrad.ipynb", + "provenance": [] + } + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "35b4rc5kNcOn" + }, + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import math as mt" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HNU2Li63NcO5" + }, + "source": [ + "nb_iters = 300\n", + "lrn_rate = 0.1\n", + "beta1 = 0.95\n", + "beta2 = 0.999\n", + "eps = 0.00000001" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "o52tc0LpNcPI" + }, + "source": [ + "class Adam(object):\n", + " def __init__(self, lrn_rate, beta1, beta2, eps):\n", + " self.lrn_rate = lrn_rate\n", + " self.beta1 = beta1\n", + " self.beta2 = beta2\n", + " self.eps = eps\n", + " self.idx = 0\n", + " self.m = 0.0 # 1st order\n", + " self.v = 0.0 # 2nd order\n", + "\n", + " def update(self, x, g):\n", + " self.idx += 1\n", + " self.m = self.beta1 * self.m + (1.0 - self.beta1) * g\n", + " self.v = self.beta2 * self.v + (1.0 - self.beta2) * g ** 2\n", + " m_adj = self.m / (1.0 - np.power(self.beta1, self.idx))\n", + " v_adj = self.v / (1.0 - np.power(self.beta2, self.idx))\n", + " x_new = x - self.lrn_rate * m_adj / np.sqrt(v_adj + self.eps)\n", + " return x_new" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "wjpxTiVuNcPS" + }, + "source": [ + "class diffGrad(object):\n", + " def __init__(self, lrn_rate, beta1, beta2, eps):\n", + " self.lrn_rate = lrn_rate\n", + " self.beta1 = beta1\n", + " self.beta2 = beta2\n", + " self.eps = eps\n", + " self.idx = 0\n", + " self.m = 0.0 # 1st order\n", + " self.v = 0.0 # 2nd order\n", + " self.g_prev = 0.0\n", + "\n", + " def update(self, x, g):\n", + " self.idx += 1\n", + " self.m = self.beta1 * self.m + (1.0 - self.beta1) * g\n", + " self.v = self.beta2 * self.v + (1.0 - self.beta2) * g ** 2\n", + " m_adj = self.m / (1.0 - np.power(self.beta1, self.idx))\n", + " v_adj = self.v / (1.0 - np.power(self.beta2, self.idx))\n", + " dfc = 1.0 / (1.0 + np.exp(-np.abs(self.g_prev - g)))\n", + " x_new = x - self.lrn_rate * m_adj * dfc / (np.sqrt(v_adj) + self.eps)\n", + " self.g_prev = g\n", + " return x_new" + ], + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "H0-CjFJXNcPY", + "outputId": "3ca07bfa-3f10-44fb-c5da-ad340cddc1e4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 880 + } + }, + "source": [ + "def fun1(x):\n", + " if x <= 0:\n", + " return (x + 0.3)*(x + 0.3)\n", + " else:\n", + " return (x - 0.2)*(x - 0.2) + 0.05\n", + "\n", + "def calc_grad(x):\n", + " if x <= 0:\n", + " return 2*x + 0.6\n", + " else:\n", + " return 2*x - 0.4\n", + "\n", + "# optimize with the specified solver\n", + "def solve(x0, solver):\n", + " x = np.zeros(nb_iters)\n", + " y = np.zeros(nb_iters)\n", + " x[0] = x0\n", + " for idx_iter in range(1, nb_iters):\n", + " g = calc_grad(x[idx_iter - 1])\n", + " x[idx_iter] = solver.update(x[idx_iter - 1], g)\n", + " y[idx_iter] = fun1(x[idx_iter])\n", + " return x, y\n", + "\n", + "# Adam & diffGrad\n", + "x = {}\n", + "y = {}\n", + "x0 = -1.0\n", + "solver = Adam(lrn_rate, beta1, beta2, eps)\n", + "x['adam'], y['adam'] = solve(x0, solver)\n", + "solver = diffGrad(lrn_rate, beta1, beta2, eps)\n", + "x['diffGrad'], y['diffGrad'] = solve(x0, solver)\n", + "# visualization\n", + "plt.rcParams['figure.dpi']= 300\n", + "plt.rcParams['figure.figsize'] = [6.0, 4.0]\n", + "#plt.plot(np.arange(nb_iters) + 1, x['adam'], label='Adam')\n", + "#plt.plot(np.arange(nb_iters) + 1, x['diffGrad'], label='diffGrad')\n", + "plt.plot(np.arange(nb_iters) + 1, y['adam'], label='Adam')\n", + "plt.plot(np.arange(nb_iters) + 1, y['diffGrad'], label='diffGrad')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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FqIokhTGmDlicN3ywxMPk77+m/IgKHqfUuIafk3ucNRSfpHgT8B5gZv6EMeYZ4NfAXdba7WXEJX4rcDW3rHJPhVZSlNI82zVJ0TnysKNAkmIqviV924aFbDvUzVcf3Oc6v7eznw/9YDt/95Odrhcqbw//hvNC7s9l7gVw4esmF2BDG9z+Rfjqi93nf/33cN7tY3p+VNr8GXV85+1XcOfXH5uwmfX2Iz1sP9LDJ3+5i9lNNTTXRggZQzhkOHJ6kJ546d88v3LlLD7y8vNY2R5caadSrGxv5N9et4F3XNvNJ+55ls3Pdkz8pKx0xvLkwdM8efA0n7tvD+D02pjTXMu8llpqoiFSaUvGWpJpS0evc4Hd714SbmqjzsXx9qYa6mIRIiGTLWHkJBS6B5OcHkjSNZCgt4zfazFO9A5xoneIB3efHDfXEAszszFGQyxbRikWJhYOkbaWdMZ5zRKpDD2DKXriSXrjqbJXPkyloVSGA6cGOHCquCRUTSQ0krior4lQEw4RjRii4RBhY0hlnNcnlclk753tdMa5qD6QTDGYyCYmJvxvQc5/R/bvL/+H9JkxZBMYTnKjPhamLhYhGjKEsu/h4XJcw3+fnLygGXldkunhezuyPfxa5c6lMqMJiGQ6E+jqpUhouJxYaOQ+Gh4dC4cMYeP8zJHQ6L0zBpFQaHRuJOFhyVhnRZGF0cfWWdFpLWSGt7NjmezY8L4Za8lkRvfLZMdszn75c6PPzfk9Zn+Xo1vO9vCQ89htfDR5M/wwf9/R45vRo3uNezx/+Fy5+xSKi5x984+TSafp7g6PjH27tjlzAAAgAElEQVTl4FNEwuFx+445gsvD/P3Gxuo9l/vzuB1v7FjpybFSFnlX8QLASVNasTiTTcCmUik6O0f/3+juju1EIlVxacJXej8VTzn9Yrm/UKlUcsxn6nud24lEzu7ecnpPFUcv03ipVIqOjtHP09NmHx98eX6BHamEavmXQBtjPxtJ4ITHvl4O520XKMZfkvzjHCrjGIcZm6QoJbalBebOyd7eYYy5G3ibtbbw1ckyGGPaGdvUvBgrcjf6+vro6RnfZ+BM0N8/2vqkULmnwcF4ya9BqsBqic7TPcyrn/h/E+tqWnH750mi6zDxbDz7j7svymmqjZAY7Ccx8ReJffeeqxfw1IFTPHHI+zVzS1BESPH+yP94Pqf/6g+S7vNebVC0medTd86tRJ/5/vi5gU6GfvlRhq75i8mfpwQG+LdXr+WvfvQsP9/ZOeH+4CSoCiWpJrJkZh3/94ZlXLtyJsZkJv05z/08uW1P1uImw6duP4enDs3nyw8fYvOu8RfWi5HKWA6fHhxZvVNprfVR1s1tZFV7A6vbG1g5u54FLbU01oSLvlDQG09xsGuQA11xDpwaZNeJfnYe7+Ngl3uZMD/0J9L0n5qCPyBVbiiVGUnunK2sdRJpxZaRm65S2SQTVHbljwQl5+9t75n571qR4OR8geu07/8LK3IWyvlMdekzJTI5o5+n8L6uM/Z6Zr4+P66VTUK1JCnyv3o7UEbPhvwrWX59nTf/OOVcMatUbMMM8CpgozHmFmttqaWyJvJO4MOTOcCWLVs4duyYT+FUr0KXCrdt28aeA6W9Bs63Fd0/pg9t2Upny8Qfkxd0J1niMt6xfwePbd4MwMNHDWO+bZtVb5Jszu4zFW6bA3uOh+lOFp/fvym0lfnG/R9lR1ou4tHn4/C8Pz9TXfgabjA/IWzHN62ObP0CvxlYyUBNqfm9yXtRC8xeafj+/hC9Jbx2pagLW164MMPVc3uxh7Zxbznp2yJs2bKlMgcGbp0Fl10Ivz4S4rFOQ9pWz/dImqKWlc2jtzl1KYwZBDqgA452QHGFq8arAVYBq1rhJa0wkILD/YYDfbCvz7Cv19BTofeNiIiIiIiIyHTR29s7pdfFgnTgQNGdCSqiWpMU5XytM/+rmpVKUgQV2y7gx8B9wHaclSWDQCuwGrgJeBuQ2wF3MfBTY8yl1trjZcQpk1SwpncZ6w1DBqLGknS5eJoo8kuZQ5Fm1/Ga1GgmuCfhHltzdGoX9DfH4C1r0nzumTADqeJevzeGf+E6njFhti94jZ/hMRhr4/n2W1hz/Ifj5sI2xboj/81jy97t6zmLYQxcPNuyrjXNTw+G+O0xg/VpIWdj1HL1nAxXzbU0ngEriOfWw+tWZnjJYtjSYXi0I8TxweAv0DcPJyVanPv22uCWKNdHYFWLZVULkC0V05WAfb2GfX2G/b2Gg/1UVRJHRERERERERM4c1ZKkyO/MmyjjGPk1E+rKjCVf0LHtAzZZa+/1mO/I3h4wxvwD8C84yYphS4C7gNvLiFMmyRSs0lveBb5YGJIuJdqLrZAxFGlyHa9J5iQpxi8EAKCpCi5CL22CD1yQ5of7Qzx10pAp8DquNIe4Iuy+kOhg6xUM1MzxPb7n5ryUJSfvozbVPW5uwekt7Ol7llONfrXIKU19BF61LMOls+F7+8Ls7i3/IvPcOsum+RkuarNES2+vUvVaYnDTAsuN89Mc6IdHO0I8cdLQV6EVBS3R0YTEymbL7ACTEhMxBmbWwMway4Y2529aKgOH+kdXWuzvM5waqo6AQ1iaYjAjBi0xO3LfEHE+A/URqItYakIQGb4ZJwns1N136qunMjCUgXgaEmnDQAr6UtCbgN6koS/p3PcmoTcJg+nq+PlFREREREREprtqSVLkr06IlXGM/O6+fhXZDjQ2a+0+nETFhKy1ceDtxqkB8r6cqduMMRdbax8rMU4vdwHfKfE5K4AfDG9s3LiRtWvX+hROdenv7x8pSVOoJ8X69etZuGRlycdv2r6F/p7xdctXrFnLpgsmvugeeaYbDv/nuPEGM8imTZsA+Naxp6Hj9Lh9zluxiE2blpcccyXcDhzrGeK/tx7hO08cG9fc+bz5Tfxd9BHwWEM065Y/Y9OcyjQ7snO74Z4PuM5dHr+XgZe9vSLnLcUdwKGuQe597hT3PneSrQe6SRfIqUXDhgvmN3HxkhlctnQGGxY1T7pJYjFyP0/g/O1oaGio+HnzvRmnieiezgEePdDN1gPdPHagm5P9Hhm9AmbURVgzp5Hz5jWybl4T581rZG5zTSCvZyV19iV4rqOf3R0D7OkcYHfnAPtPDXJqoPTXyEt9LEx7Y4z2phjtTTXMyd63N8WYk72f1RAjHAr+tUykMnT2J+joS9DRm6CzL8GJvux2X4KO3iFO9ifpiaeyPQmCZYC6aIgIaWIhJ+E9q6WRxtroSMPqumiIuqhzHwkXzj6mMk6T8yGXWzyZHnk8mMwwmEgzmHLup+JnFxERERER8UNTUxObNq2f6jACsXPnzik9f7UkKfI7c+SvXihG/uoEv7p9VHNswz4AvBTIvQL+BsCXJIW19gQlNjLPv/jW2NhIc7N72aEzSoFrMQ1lvgYNNRHGL8YBwtHijte22HU4FD9Nc0MdhKN0Dbovy1gwq6mqfm/NzfBXC2fz/91yHvfsOMau470snlnPdWvamVOThE+8zv2JCy6mcdVVlQvssj+AbV+HY0+Pm4ocfIDmnl2w8OLKnb9I65qbWbdkDu+8EboHkmzZd4qO3iHS1pLJWNIZSzRsWNnexPrFM6iNju9TErSGhoYpfQ+ub2lh/Yp5gJO0OD2Q5Ej3IEdPxznaPcjxniEslnAoRCRkCIcMjTURFrbWsbC1ngWtdTTWVMt/av3V3AzL57eNG0+kMhzviXO8J86xnjh98ZTTQHsoRf9QinTGEgoZQsYQDkEkFKK5LkpTbYTm2ijNtRHam2uY01xLU20VLOcqoG0mnDPBPtZa4skM3YNJ19tQKk0ilSGZzpBIZUhnnCRhOGSy76kQkTHbxkksxMI0xCJOsiEWpqEmMpJ8aKiJUBMJjaufumnThsA/T8l0hsFk2klcZBtlD28PJFKjc0lnLp2xZIb/JlnrrHbJjqUzzpcBouEQ4ZAhmvP6OK9ZaOS1i4acfSJhQyQ7Hg2HRrZjEec+Mjwecu6H9wlNkER04rEk0xlSaUsqkyGZtiOPU4XmcsaGjzNyy/7sqczofe4YQMgYDM6qp5AxYMaPOY/NyHbIONvDj0N5c6GQyc5PvL9zSoPNloeD0X/+5LaUs3b0yxvW4rrvyN55+w7vN/o471zZjdFjjZ7bjjvG2H+cjYvLZTw3jmHxeJw9e/aMbC9btpyamppxcYz+SOOPZfMGck+R+7OOP5b7PrjuM/r8UnPhpSTPp3ea3Z1SqsUpvXXkeEOJBAcPHBzZXrR4ETWxcr4LWL18eJnOGnqpilPoPZVIDHHw0GiTwEULFxKL5X9X9uxR6MujMkp/p9wlEgkO5XyeLlvXXlXXxSqpsdHvFsqlqZYrJ/kX7euNMabE5tn5X7WtVJKinK/0Vio2AKy1KWPMp4FP5wzf7Oc5pDjGejeKCJnyauTUxdwvFA8UW++poUDj5v5OaJ7HiV6XJAjQ3lyd/7Cpi4V5xQsWjB189JuQ6HV/wsY7KxtQKAw3/z18/eXu8/f/M7x2/GqWqdRSH+Wmdf6XvzqTGWNobYjR2hDj3PktUx1O1YpFQiyaWc+imfVTHUpVMMZQl00kzG0p53sO09vwhf/mKk84iUykp6eHzYndI9ubrlp81vwPq4jfenp62Lx5/8j2pk3L9XkSmQTnMzXa8HbTphX6TImUadznaeOCAnuLn6qlsngnYxPoUaC9xGPkv2tK+uZ/AfnHWVjGMSoVW65f5W2vMtO9lsg0VKiXdbm/jjqPb7MPJv1IUnSQTGc41e/eamV24zS5oGYtbPmi+1z9LFh3a+VjWH4tLLnSfe7Zn8CJqV02JyIiIiIiIiIiUo2qIklhrR0EDuQNu9eo8Za//zPlRzTGs3nbi8o4Rv5z/Iot18G87QjQWoHzSAGFGmebUHmlc+pi7gueBotdSVE3E89F+f0ddPa5r6KA6l1JMc7+B6HDIwmw/o0QDSjZctUfe8/99l+CiUFERERERERERGQaqYokRVb+hft1JT4/vyuzX4mA/cBgznaDMWZJsU/O7ptb86Kf8QkFP7h1KlVthaAVqlBW9koK949p0SspwhFnNYGb/g46PEo9AcxunCZJike/4DFh4OK3BBfHyhtg7vnuc09/B7r2u8+JiIiIiIiIiIicpaopSfFk3vYVxT7RGDMPWJozlAR2+BAT2b4Y2/KGi44NyK//sq3EXhvFmpu3bYGTFTiPFFCoQVO55Z7qJ7uSArxLPvV3cKLHPUkRDRtm1E+DPFfvcdj5I/e51S+E1qJzipNnDFz1fvc5m4YH/zW4WERERERERERERKaBakpS/Dhv+8YSeirkN4nebK31szl1fmw3lfDc/H09rqZO2lV520ettakKnUs8mALpp1CovI9brUdPioFiV1IANLS5j/d30OFR7ml2Y03ZiZVAbf8eZDze6pdUuGG2m3W3wszl7nNPfAP6KtGSRkREREREREREZHqqpiTFgzgNtIctB64r8rl/kLf9Az8CyvHDvO1XG2MaJ3qSMaYJeHXesN+xDct/DfIbaUsArC3QOtvnxtlxX1ZSdHqupJjdPE2aZm//rvt46zJYcX2wsQCEwnDle93nUnF4+LPBxiMiIiIiIiIiIlLFqiZJYZ2ru1/NG/7wRKspjDE3AFfnDPUC3/Y5tm3AozlDjcCfFPHUPwEacrYfttb6UoYqlzHmDYxP6Hzf7/PI5IRMeR+3+pjHSopSkhSN7e7jfSfo6Iu7Tk2LfhTdh+DgI+5zF74Wyly9MmkXvhaa5rnPPfolSPQHG4+IiIiIiIiIiEiVqpokRdbHgNwyTdcCf+q1szFmAfDFvOFPWWs73fbPeZ7Nu11XRGwfytv+M2PMNQXO4Rb7ByeI6zXGmNtKKHOFMea1jH8NngS+V+wxxE+FelKU93Gr80xSlFDNy2slRe9R75UUTdMgSbG9QC7u3FcGF0e+SA1c/i73uaFu2OZrHlVERERERERERGTaqqokRTa58P/nDX/UGHOXMWb+8IAxJmSMuRWnRNTSnH2PAJ+oUGw/B+7JGYoC/2uMea8xpj4ntgZjzPuAn2f3GfZTa+1EJZjOAe4Gdhlj/t4Yc7kxpiF/J2NMzBhzvTHmB8B/ArlXk+PAOyrUnFsmUuhlD5VX7qmp1r1xdk+8hCRF8wL38Z7Dnj0p2qdFksKj1NOc82D26mBjyXfR70Nti/vcli8Ufq+IiIiIiIiIiIicJaoqSZH1McY3qn4HcMAYs9sY8zhwEmelwOKcfQaB37PWnq5gbHcAe3O2a4F/ATqNMb8zxmzH6avxyezcsN3Am0s4z0rgL3CSMD3GmP3GmKeMMQ8bY3YC3Tg9J16e97wk8Hpr7cMlnEt85X3hudxyTy11Udfx7sFkCQfxSFIMdtHd3eM6VfUrKbr2w+Gt7nNTuYpiWE0jrH+j+9yJ7bD/wWDjERERERERERERqUJVl6TI9qZ4NfCtvKkwTjPt9cCMvLmTwIuttQ9UOLbjwCbgqbypOuBcYB1jkxPglF7aZK3tKPO0IZxkzAXApTirLdw6Gu8CLrfWeny1XIJgCyQpym2c3VwgSVH0ghmvlRRAtP+I63jVr6TYXqCiWTUkKQAueSvg8Xvf8vlAQxEREREREREREalGVZekALDWxq21rwVehXOR30s/cBewzlp7b0Cx7Qc24vSbcL+66ziC0zj7UmvtwSIP/23go8BDOCtDJpIC7gdeB5xnrfX4WrkExWSCW0mRzlj6i22e3Tzfc2pWxr2FS9WvpPAq9TTvQpi1IthYvMxcBqtf6D6380fQfTjYeERERERERERERKqMe7H7KmGtvRu42xizEmcVwQIgBpwGdgIPWGvjZRy3vK+0jz4/AXzcGPNPwEXAhUB7dvoETmLl8eyqkFKOuwOnzBPGmDCwBmf1yEKgGedn7wO6cMpOPWqtLSaZIQEptK7BlNmTwitJAdAzmKSxpoiPcbQO6mfBwMlxU/PN+DGA9ma3BTtV4uRuOJq/oCmrWlZRDNt4J+z6+fhxm4atX4HrPxh8TCIiIiIiIiIiIlWiqpMUw6y1zwPPT3Uc+bJJiEezN7+PnQZ2ZG8ybXinKUyZC5cKJSm6B5PMn1FX3IGa57smKebhnqRoa4wVd9ypMB1KPQ1bfj3MXAGndo+f2/pVuOYDEKnyVSsiIiIiIiIiIiIVUpXlnkSmK1MoSRGuTJKiaM0LXYfnmVPjxmbUR6mJhIs/dtC2f999fP4GaF0aaCgTCoWc1RRu+jtgxw+CjUdERERERERERKSKKEkh4qNCjaxNmY2zo+EQ9TH3hEFJSYoW9+bZ81zKPc1urOJv9nc+B8efdp8777ZgYynWC14H0Qb3uUc+F2wsIiIiIiIiIiIiVURJChFf+d84G6C51n01RWkrKdybZ7utpGhvruIkhdcqCoB1twYXRylqW+DC17jPHX4MjjwZbDwiIiIiIiIiIiJVQkkKET8V6JxtQuV/3LxKPvX4Uu5pmq2kcGtCDbBwI8xYFGwspfAq+QTw+NeDi0NERERERERERKSKKEkh4qtC5Z78T1L4Ue6pxQxQT3zMWHtzbfHHDVJfBxze6j637hXBxlKq9rWw9Gr3uae/A4n+YOMRERERERERERGpAkpSiPipAj0pAJr9SFJ4lHuC8aspqnYlxfO/wDMRtPpFgYZSlove7D4+1KMG2iIiIiIiIiIiclZSkkLEVwWSFKHykxT+lHtyX0kB4/tSVG1PCq9STzNXQNvKYGMpxzkvhbpW97mtXws2FhERERERERERkSqgJIWInzxWUmSsITSJlRS+lHuK1EDDbNepabGSIpWA3Zvd51a/MNhYyhWthQs8GmgffBg6ng02HhERERERERERkSmmJIWIr9yTFBYoP0XhU5ICPEs+zWdskqIqV1IceMgpi+RmuiQpADbc4T2nBtoiIiIiIiIiInKWUZJCJAAZQpPqSdFSF3EdLz1JsdB1eG5euafZjVXYOPu5e9zHY02w+IpgY5mMOetg4SXuc0/9F6SGgo1HRERERERERERkCilJIeInj3JPFphESwpa6r1WUqRKPJB7X4r5OeWeYpEQzR5JkSnl1Y9ixSaIxIKNZbI2vMl9fOAkPPOTYGMRERERERERERGZQkpSiATAYia1kqK51rtxtvVIjLgfyL3cU25PitmNNZOKtSJO7oaTz7vPrX5RsLH44dxXQqzRfU4ln0RERERERERE5CyiJIWIr7xWUkzuor9XT4pEOkM8mSn+QEWUe6rKfhS7/td7btVNwcXhl5pGOP9V7nN7NkPXvkDDERERERERERERmSpKUoj4ybPcU2WSFFBiXwqPck/NZpBGBgBnJUXV8Sr1tOAiaGwPNha/eJV8Anjim8HFISIiIiIiIiIiMoWUpBDxVfBJip54CUkKj3JPAPOyqymqbiVFvAf2P+g+t+qFwcbip/nrYc757nNPfBPSJfYbERERERERERERmYaUpBDxlXuSIjPJJEWzXyspmuaDRyzDfSlmN9aWElrl7dkMGY+fcfU0TlIYAxd5rKboPQrP/zLYeERERERERERERKaAkhQiPjKeTawnl6SojYapibh/XLsHSkhSRGKe5ZGqdiXFrnvcxxvnwrwLg43Fb+e/CiIeSaHHvxZsLCIiIiIiIiIiIlNASQoRH1nPnhST51XyqaSVFOBZ8mn+yEqKKkpSWOu9omD1zc5qhOmsrhXW3eo+t+t/oedosPGIiIiIiIiIiIgETEkKkQBkfPio+ZekcG+ePZcqXElxYif0HXOfW3VzsLFUyoY73MdtGp78j2BjERERERERERERCZiSFCK+8mPNhDuvvhQlJylaFroOj/SkaKqiJMXuX7uPmzAsuzbYWCplyRUwa5X73BPfgEwm2HhEREREREREREQCpCSFiJ88yz1NvixRUOWe2qqp3JNXkmLRRqhtDjaWSjHGezVF1z7Y95tAwxEREREREREREQmSkhQiPjIeKykqmaTo8anc0zxzkraGGNFwlfxZSMZh/wPucyuuDzaWSrvwtRBy//2yVQ20RURERERERETkzFUlVyNFzhTuSYqMDw2ePZMUcX/KPTWYIVY2p0sNq3IOPASpuPvcmZakaJwN57zYfe6ZH0P/yWDjERERERERERERCYiSFCJ+8ij3hA8rKXzrSeFR7glgTX1PaceqJK9ST7UtMH99sLEEwavkUzoB274VbCwiIiIiIiIiIiIBUZJCJAB+tNP2rSdF0zwyHkmTFTXdpYZVObs3u48vvw5C4SAjCcby66Flsfvc418vkAATERERERERERGZvpSkEPGVV0+KyX/UfEtShKOcMq2uU4vCXaWGVRm9x+H40+5zZ1qpp2GhEKx/g/tcxzNwcEuw8YiIiIiIiIiIiARASQoRHxmPb7tXsnF2yUkK4Kid6To+z3SWfKyK2HOv99zyTYGFEbj1bwDj8Wf58a8HG4uIiIiIiIiIiEgAlKQQ8ZH1XEkxeV5Jingyw1Cq+IbX8WSag2n3JMXMdJUkKbz6UcxaCa1Lgo0lSC0LYOWN7nPbvwvxKuoZIiIiIiIiIiIi4gMlKUQC4Ee5p+a6iOdcKaspjnXHOWpnuc41DR0vOS7fWeudpDhTSz3l2vAm9/HkAPzuf4KNRUREREREREREpMKUpBDxkbEZ1/FKrqQA6CkhSXG0O+5Z7qlm4FjJcfnu+HboP+E+dzYkKVa/EBra3ee2fi3YWERERERERERERCpMSQqRAFSyJwVA92Cq6OMc6xn0XEkR6jnsrGSYSl6rKEIRWHpVsLFMhXAU1r/efe7ok3D0qWDjERERERERERERqSAlKUR85dWTYvIftbpomGjYPdlR6kqKw7bNfTI1CD1HygnPP15JikWXQk1TsLFMlfVv9J5TA20RERERERERETmDKEkh4iPjS2Enj2Mb47maotSeFHvsXO8dTj5Xamj+SQ7C/gfd51ZsCjaWqTRrBSy92n1u23cgMRBsPCIiIiIiIiIiIhWiJIWIj6xHqSRrJl/uCaDZhyTF0e44PTTSYZvdd+icwiTF/gchPeQ+dzb0o8h10Zvdx4e6YccPAg1FRERERERERESkUpSkEPGVV7knf5IUfqykON4TB2CPne++w1QmKbxKPdW1wrwXBBvLVDvnpc7P7eZxNdAWEREREREREZEzg5IUIj4yXispqihJcbQ7m6TIzHPfYSrLPe3e7D6+/DoIhYOMZOpFa+GC17jPHXgIju8INh4REREREREREZEKUJJCxFeVTVI0104uSZFIZejsc8op7a62lRS9x+DEdve5s63U07ANd3jPPfbl4OIQERERERERERGpECUpRAJQLSspTvTGGV7sscd6rKToPjg1jZm9VlEALD+LmmbnmrMOFl3qPvfUt2CoL9h4REREREREREREfKYkhYiPjMdKCnxqnO2VpOgpMklxLFvqCQokKQBO7S4pLl949aNoWw0zFgUbSzW5+A/cxxO98PR3go1FRERERERERETEZ0pSiPipyntSHM1JUhy07SSsR5+HoEs+ZTKwx2Mlxdla6mnYuldA3Uz3uce+5PmeExERERERERERmQ6UpBDx1dQkKcpZSZEmzH47133HoJMUx38H/R3uc2d7kiJaC+vf4D537Gk49Fiw8YiIiIiIiIiIiPhISQoRH3mVe/KtcbaPKymgQMmnkwEnKbxKPYWisOTKYGOpRhf/vvfcY18KLg4RERERERERERGfKUkh4ifP0juVXUnRn0iTTGcmfP6xnsEx27vtfPcdg15J4ZWkWHwZ1DQGG0s1mrkcVtzgPve778LAqWDjERERERERERER8YmSFCIBqHS5Jyiu5FPxKymeD67XQWIADjzkPrdiUzAxTAeXeDTQTg/Bk/8RbCwiIiIiIiIiIiI+UZJCxFfuqxms8avcU8RzrpiST8fykxQZjyRFog96j5YUW9n2PwjphPvc2d6PIteqF0LzAve5x77sNB8XERERERERERGZZpSkEPGTx+KDIFZSTJSkSKUznOgdGjPmWe4Jgiv55FXqqW4mzL0wmBimg3AELnqz+9ypPfD8LwMNR0RERERERERExA9KUoj4yKtxtl89KRprIoRD7seaKEnR2ZcgnRkbXzeNdNpmjyfsKivGknklKVZsgpD+RI2x4Q4Ieaymefjfgo1FRERERERERETEB7oCKBIAv8o9GWNornW/SN0TTxV87tHuQdfxgn0pKq3nCHTsdJ9TqafxmubC2pe5z+25F45vDzQcERERERERERGRyVKSQsRHxnr0pPBpJQV4l3yaaCXF8Z646/g+PPocBFHuqVCJouVqmu3qsnd6zz10V3BxiIiIiIiIiIiI+EBJCpFAVD5J0TNBkuJot3uS4mTtYvcnBJGkeO4e9/HZ50CLR/LkbLdoIyy42H3u6W9D34lg4xEREREREREREZkEJSlEfOXek8LPlRTNZa6kOOaRpOhtXOr+hO6DkHQvEeWLVAJ23+s+t+qmyp33THD5u9zH0wl49EvBxiIiIiIiIiIiIjIJSlKI+MizcbZPPSmgQLmngfJWUiRbV3k8w8LJ3aWEVpoDD0Gi131u1c2VO++ZYO3LoWWR+9yjX4Sk++9aRERERERERESk2ihJIeInW/mVFOX2pPBaSRFtWwoh92NysoIln7xKPcWaYPHllTvvmSAcgY1/6D430OmUfRIREREREREREZkGlKQQ8VX1lns62uNeumnujEaYucz9SZXsS+GVpFixCcIeSRMZteEOiDa4zz10l2fCTEREREREREREpOFzuDoAACAASURBVJooSSHiI89yT1O8kiKTsRzvHnKdm9tcC22r3Z9YqSTFqb3Quct9TqWeilM3A9a/wX2uYyc8/6tg4xERERERERERESmDkhQifvLIUdgAelL0xL2TFKcGEiTSGde5eS11MGul+xMrVe7p+V96z6lpdvEuezueCbDffFyrKUREREREREREpOopSSHiK69yT/591MpZSeHVjwJgbksttHk0z+58rjIXunf9r/v4vAuhaa7/5ztTzVwO57zEfe7gI7D3vmDjERERERERERERKZGSFCI+8i735B+vJEVvPEXSY7XEUa+m2WHDrIaYd7mnRB/0HisrTk+JAdh3v/ucSj2V7sr3ec/d9/Hg4hARERERERERESmDkhQivqp8T4q2xhrPuYOnBlzHj3W7N81ub6olFDLe5Z4Ajj5VUnwT2vdbSHms7Fj1Qn/PdTZYdAmsuN59bv8DsNcjISQTsxYy6amOQkRERERERETkjBaZ6gBEziTGsyeFf/nAJbPqMca9CtOejn6Wz24cN77/pHvyYm5LrfOgfiY0zYfeI+N3OvAQrHnRZEIe6zmPUk/1s2DBBv/Ocza59k9h96/d5+77GCy7Oth4ppN4Dxx5HA49CkeehL4TMNgFg6dg8DTYNNQ0Q12r8zmpn+WsPFpwkfN+bV0GPvacERERERERERE52yhJIeKryq+kqI2GWTCjjkNd41dH7O3sd33OrhN9ruPL2hpGNxZfBtu/O36ng4+UFacra+G5e9znVt4IobB/5zqbLL4Mll0De38zfm7f/bD/QVhyRfBxVSNr4djTsO2/YfdmOLED789t1lCPczu939nObfxe1wqLLnV6g5zzUieRISIiIiIiIiIiRVO5JxEfefWksD5/0XpMciHHHq8kxbFe1/E1c5pGNxZf7n6yw1sh6d14uySdu+D0Afc59aOYnGv/zHtOvSmg5yg88Cn47JXwuavhoc/Aie1MmKCYyGAX7Po5/PA98E+r4BuvhK1fg3i3L2GLiIiIiIiIiJzplKQQ8ZVHksLnj9oKl5JOAHs6xq+Y6B5IcqzHPcmwem5ukuIy95OlE3D0yZJjdPXsz9zHTci7r4IUZ+mVsOQq97k9m+GAjytippOTu+F7b4dPngu/+FA2MVEhmZRTdutHfwT/vA5++gHn/CIiIiIiIiIi4klJChEfea2k8LPcE3ivpHAr97TrhPsqCshbSTHnXIg1ue944KGS4vPkVk4KYOFGlcnxw7V/4j33iw+5NzI5U3U+B9/9Q/jMxfDUfzm9JYKU6IMtn4d/3QD/8WrYc+/Z9fqLiIiIiIiIiBRJSQoRP3ldhPS5sa5XkuJE7xB9Q6kxY896lHpqro0wp7lmdCAUhkUb3U944OGy4hzj5G44+pT7nJ+Nuc9my67xLtt18GHvJNGZpP8k/ODd8G8bnb4TNjPVETl9WL7+CvjKi2HfA1MdjYiIiIiIiIhIVVHjbJEAWJ9XUiyf7Z6kANjb0c/5C1tGtncd9+hHMbcJk588WXwZ7P7V+J0PPAyZDIQmkdf8XYEL5OfeVv5xZZQxzmqKb7zSff4XH4Y1L4ZoXbBxBSGTgSe+Ab/8sNMnolT1s2DhJTD3AmiY7TTErm+FUNQ53uApGDgFp/bA4ceh4xlK7mdx4EH46oth+Sa4/oOw8OLS4xQREREREREROcMoSSHiI4PHt7Z9Xkkxv6WOmkiIodT48+3p7BuTpPBaSbF6jktpJ6++FPHT0PkstK8tK17A+1v8Cy6G1iXlH1fGWr4JVtzgnmzqPug0jL7mA8HHVUnHfgc/fj8c2lLCkwwsuxrOfzUsvQpal5X2OR3qhSNPwHO/gB3f924I72bPZue27la46SPQurSEuEVEREREREREziwq9yTiJ48vVvu9kiIUMp4ln/Z0jPalsNYWXEkxzoKLIOSRu5xMX4rjO+DEDve5824v/7gynjHwwr8HE3afv/+T0HM02JgqJZ2C+/4RPn9t8QmKmcvhxr+G92+HN/0INtzhjJWaSKxpcspr3fy38N5tcOdmuOKPnFUYxdrxffjMRvjlR5ykh4iIiIiIiIjIWUhJChEfBdU4G4prnt3RN0TXQNJ1P9eVFLEGmHeh+wkPPFJyjCM8eyEYOPfW8o8r7trXwsVvcZ9L9sOv/ibYeCrh1B74yi2w+e8gk5p4/1kr4bYvwLsfg6veDy0L/IvFGFiwwUlYvO938Iq7YO75xT03PQS//Wf49AZ44ptO2SoRERERERERkbOIkhQiAbA+l3uC4pIUzx3v83y+a5ICvBsvl7uSwlrvfhRLroDm+eUdVwq77s+htsV97qn/dPoqTEfWwtavwWevKm71ROsyuO2L8K4tcMHvOQ3iKylaC+tfD2+7H978E1h+XXHP6z8BP3gXfPlmOPJkJSMUEREREREREakqSlKI+MizJ0UFVlIsn93oOr6now9rnRUdXv0o2hprmNkQcz+wV1+K0/uh50jJcXL0KTi1233uPDXMrpiGWU6iwsuP3gupRHDx+CHeDd95M/zoj5wVIYWEa+C6v4B3PgwXvLryyYl8xji9Lu74Abzpx97Jv3yHHoXPXwc//mOnUbeIiIiIiIiIyBlOSQqRIAS4kqI/kaajdwigQD8K9wQHAIs8khQABx4uOr4Rv7vbfdyEYe0rSj+eFO+St8KsVe5zx7bBfR8LNp7JOPoUfO5ap4/DRFbcAO98CK77U2dlw1RbdjX8/s/gDXfDnGLKQFl47EvwmYudVSMqASUiIiIiIiIiZzAlKUR85NWTwlbgo7bcI0kBsCdb8ulZjySFZ6kngMbZTv1+N6UmKayF7d9zn1t2jXMuqZxw1Gmi7eW3/zy5XiNBsBYe/RJ88Sbo2lt435oWp7TTG+6GWSuCia9YxsDKG+Ft98HLPl1cg+2Bk86qkS/dOH3Lc4mIiIiIiIiITCAy1QEUwxizAtgILARiQBfwDPCgtTY+hXEZYAPwAqA9O3wceAp43A7X3PHnXLOAK4EVQAPQD+wGHrDWnvTrPDI5xutXXoGVFK0NMVrro66Nsfd09HPpspns8ij3tKZQkgKc1RQnnx8/XmpfikOPQvdB97nzbi/tWFKeVTfD6hfBrp+Pn7MZ+N7b4O2/hZoCq2umSCQ9SN1P3wPP/mDinZddA7d+FloWVj6wyQiF4aI3wbmvhPs/AQ/fBekJym4d3gpfuB4uejPc8CGonxlIqCIiIiIiIiIiQajqJIUx5lbgr3ASAW76jDFfBT5ire0MMK4o8F7gfcACj90OGWP+Bfi0tXb8VeTiz3Uh8DfAS3Ff+ZI2xvwE+Ctr7bZyzyN+8cpL+Z+kAKfkU9eB0+PG93b2cfj0IP2JtOvzVs+dIEmx+DJ48pvjx4//DoZ6oWaC5w/b9m338VAU1r60uGPI5BjjfHP/rstg0KXHQddeuOcv4WWfCj62ApoHD3DJ3s8QHTpWeMdwDdz4Ybj0HRCaRosDa5vhpo/AhjvgZ38Kz/9igidY2PoVp9zVDR92nhd0nw0RERERERERkQqoyis6xpgaY8w3ge/hnaAAaATeDewwxlwTUGyLgEeAf8Q7QQHOqo9/Ah4yxhTar9C53gs8Brwc799VODu/1RjznnLOI37yKvdUmSSFd/Psfs9+FACr2if41rxXk1+bKb48UH8nPOGS6ABYeQPUtRZ3HJm8pjmFkxBbvwrPuqy0mArWsrjzXq559iM0TpSgmLkC7vwVXP6u6ZWgyDVrBbz+O/Ca/4SWxRPvP9gFP34ffPEGOLS18vGJiIiIiIiIiFRY1V3VMcaEgP8GXp83lQb2Ak8C3Xlzs4GfGWM8rqz6Fls7sBlYnzc1CGwHdgL55acuAjYbY9pKPNcfA//C+NUuR4Gt2ftcEeDTxpg/KuU84i+vnhSVKPcE3s2z93b28+yxPte5BTPqaKqNFj7wrBVQ7/GWfeLrxQX38GchNeg+p1JPwVv3crjwdd7z3/1DOL49uHjcDPVS+/P3s/7glwlPtADtvNud/g5zi2lEXeWMgXNeAu96BK75E2d1yESOPOEkKr77NjjtUVJNRERERERERGQaqLokBfAB4BV5Y/8OLLbWLrfWrgdmArcBB3L2qQe+bYxpqWBsX8XpCTEsjlPyqc1ae561dh3QBvwxY5MVq4AvF3sSY8wVwMfzhu8FLrLWzrfWXmytnQ9cAtyXt98njDEbiz2X+CvInhTg3Tz7wKkBth/Jz+U51kxU6gmceJde5T6380fQtb/w8+PdsOUL7nMN7bD2ZRPHIP675R+gZZH73FA3fPNV0H0o2JiGHX4cPncNsZ13F94vHIOXfAJu/1LxZcemi1g9XP+X8K6HYdULi3iChW3fgn+9CH7xIRgcX/pNRERERERERKTaVVWSItsc+i/zhv/cWvsOa+2R4QFrbcZa+z3gCmBfzr4LcRIElYjtZuCWnKEk8EJr7aestQM5sfVbaz8JvCi7z7CXGWM2FXm6f8Qp4zTsR9lzPZ67k7X2MeBm4Cc5w5Hs86WK2Ap91LzKPaUyll8/c8J1bvVETbOHrX+j+7jNwJbPF37uli84F73dXPFuiNYVF4P4q7YFXvnvePZI6T0C37wdBlx6V1RKJgMPfAq+dBOc2lN439al8Ae/gEveWrHEX1WYuRxe/2147bdgxpKJ908POa/hp18Av/0XGHJfRSVZ1kJy0EmmDnY57/f+Tug/6bx26dRURygiIiIiIiJyVqm2xtl/AuReQf0N8DGvna21h40xbwV+mTP8fmPMp621J32O7W/ztv/BWvubArHdZ4z5GPDBnOG/A64sdBJjzC04yZdhJ4E/sNYmPM6TMMa8BdgBzMoOX2OMuclaO1EnVvGZZ7mnCvWkWDKrHmOca275BjyaZq+ZO0E/imErroe21dC5a/zc41+H6/7M/ZvsiX54+C73Y9bOgIvfUtz5pTKWXgVXvAce/LT7fMcz8K3XwRu/V/lk0ukD8MM/gj2bJ9537cvhFZ9xEi1nizW3wPLrnATEbz8JqfxqgnkGu+CXH3b2v+LdcMmdToPus4G1MHDSWeXVtddZEdR3AvpPQN9xJwkR74FEr5OIsO5/H0eEIhCpg7oZ2Vsr1M2EhtnQPA+a5kPTXGhZ6CSSIrFgfk4RERERERGRM1DVJCmyvSh+P2/4r631qp/jsNb+yhhzP3B1dqgJ+D3gsz7Gdj6QW0Kpn+JWK3wceD8wXJPnCmPMWmvtzgLPeWve9r9ZazsKncRae8IYcxfwV3nHUZIiYEH3pKiNhpnfUsfh0x69H1ysai9yJUUoBJe9A378/vFzQz3wxH/AZW8fP7f1a87FQjeXvv3MK9EzHd3wYeh8Dnb9zH3+wENOouJVX65Mg/NM2lmN86u/hWR/wV1tKIp54d/Dxj88s1dPeInWOQnBC18DP/9zePanEz9n8BT86m/ggU87q04ufgu0LKh8rEGwFnqOwImdcGI7HN8BJ3Y4q3ASPq4gyaSchEaiF7on6PlhQk6yYuby8bfWpVo5JiIiIiIiIjKBqklS4KwemJ2zvQenD0MxvsRokgLgVnxMUjC+R8a3rbW9Ez3JWttrjPkO8Oa82FyTFMaYGiC/EHmxvSy+zNgkxS3GmJjXCgyplGCTFADLZzcUnaQIGVjZXuRKCoALXuNc7BzsGj/3yGdh450QyqlMlhry/oZ+rBEufVvx55bKCUecBMTXXwGHtrjvs/vX8PlN8H++CXPP8+/cx7fDD98Dh7dOuGt/bDb29i/RuOZa/84/XbUuhdf+F+y6B+75S/cVTvnip+H+f3JWYax9qZPoWXLl9En2JAac98uxp5ykxHBCIl5lvTdsxlkVdPoA7Ll3/HzLImdVWttqmL0a2tY4jxvaps/vQkRERERERKSCqilJ8ZK87V9MtIoid9+87euMMQ3W2sJf0S1efmz3lPDcXzA2SfFS4KMe+17H6KoLgGettRN0KHZYa/cZY57DadINzoqSa9FqikAFXe4JnObZ9z/XWdS+S2c1UBsNT7zjsFi98y3s+z8xfq5rHzz7M+fi57CtX4Peo+7HuvgtUD+z+HNLZcXq4XX/DV+6GU4+575P116nV8TL/xXOf9Xkzte1D37zj/Dkf01cagc42HoF2xa9iavnrZ/cec80q292SrE98XXY/FGnnNFEbBp2/MC5zVoJ594G590O7edUPt5ixXvg2NNw9KnRW+ezTgJguus+6Nx2/2rseF3raPKibTXMziYvZiwem/wVEREREREROcNVU5LiBXnbDxb7RGvtEWPMPmBpdigGrAMenWxQxhgDXFBubMADedsXGmOMRwKm7Ncg51yrcrZfgJIUwfLKq1V0JUXxKyOKbpqd65I7nbIxmeT4uYfvGk1SPPUt+PmfuR8jXAOXv6v0c0tl1c+EN34XvngT9B1z3yc5AHf/gZOQuvr/wpx1pZ2ja7/zbf4n/9MpoTMBG23g8flv4NDMgu17zm7hiJP0O//V8OBn4MF/nbBs1oiTz8NvPu7c2tfBOS+F5dfCwksgUlPZuIcNnBqbjDj6FJzaHcy5q8lgFxx8xLnlitQ6yaS2VdlVF6ugdRnMWOT0xDjTV19YC+mk04MlNTR6n84+TiedfWwGsNn/7ubdh8IQikI46vQXCUez2xHnPlLjlOGK1ishJCIiIiIiUgWqKUmxNm97R4nP38FokmL4eJNOUgBLgPqc7X5r7YFin2yt3W+MGcg5RgOwCHA7hh+vQaHjyRSxhCp27GVtDRPvlLVmbhlJiuZ5cN5tsO2/x8/tfwC+fQfMOQ82/733MTa80WkyK9VnxmJ4w//AV18C8W7v/X73P85t9YvgyvfBoo3eF/e6D8POH8HOHzr9LYr9NvyCi+m/+Z859OS+kn+Ms1JNE2z6c6fs2kP/5vT5KKUvw4ls+aTffNy5ML7oUlh8Gcw+x0lgzFrhXNwtRzrlrKo6fcApTTV863h24h4PZ7tUHI7/zrnli9Q6/S9aFjlJi5bFzn3TPCfpWDfTuQ+6D0Y6CUO9zvtvqC973zt6PzS83ZvddhvLbqfieJZOrIRwNmERaxhNXETrndVmw49H5uud+2Ifh2NnflJJRERERETEB1WRpDDG1AGL84ZLvYqRv/+a8iMqeJxyrq4czDvOGtyTFJM9V6VeAylSCI+LsRW8SFFsksIYuHV9mc1zL3uHe5ICRsvIeAlF4Mr3lndeCcbc8+Gtv4Jvvd4psVPIrp87t0itU56mfZ1TW7+vA/qOOxemO54p7fzRBrjhQ7DxTjJ9/cC+cn+Ss1NDG9z4YbjiPfDwZ+GRf3ea25ciFYe99zm3YaGok8RqmO2co2E21DaDCTvNok3IKSUV73ESXEM9ziqJnsPO+2CqSjVF6qB1iXPhvnEONLY7t7pWpzdOTSPEmpxv04dyf5aM8zok45AadHpiDHaN3gZOQu8x52frOeI0KA9aKu6shjn5fOH9InVQP5OGWDNXxCEZaSRtItT+74+gNnvxPBxzklDhmPO7JruCIZ1wVj2lk84KunTS2U7FcxIQfdnkQnY7FQ/kx6+IdHaVRiV6nZiw856L5SQ78l/7SM3o4zG37JjJ+ZLDmH9L5P27YnjOWudzmUlBZvg+95Y/NtF2xrm3aWfOZpzH1uZtZ3K2M+PnbMaJ2Zjsfch5bEKj4yOPQ86P5zYXCjuvayj7uTXhnLGcz/OYsbBzjPyxUHZ83Jj7/jWpFOsOHwJCWBOi5oGtUFfv7OO8+KMreoZ/FyNj5c7jMj/B49xjF/2Y0efnH2vsG238e27c+ET7F3OMcsf9OjYu4xU4dqFysAX/32GiWKvxOWM3Y0NDrD62F3DeabFHfgc1Aa3oFDkDxYaGWHVsz+i2PlMiZcv/PEV3nILLfn8KIzp7VEWSAmhj7D9dkkARhbbHOJy33T6piLyPc6iMYxxmbMLAK7bJnqtSr4FUsQUz6miIhelPeNf5j4QMH37ZupJWXYwxfz0svgIOlFqBDLjuz50LnVLd2lbBnb+C77/TWQExkVR8tFTPZKy8CV76z3qP+KF+Jlz/l3DFu53ya1s+P/HF7EIySacMU7WWYmqa75Qfa18H7Wth5gqnwXhjezDfXk8OOuXMuvbCqT1jb6cPTG0/jdQg9BwmzGFm5453TVVAZymbhqFu5yaMuQBeRG+ialPD2JqqHJ+iQETOALXkLfn3aGknIsWpxal3PkKfKZGy5X+eUqEDSlIEpFqSFPlF9QdKaJo9LL8gd/GF+gvLP045zbiLjW2y56rIa2CMaYex1zmKsCJ3o6+vj56eEr/ZO03094++7F6Ns9MZW9Gf/+a1bXzvqfH/txwJGV52fjtvunQhy9vqJxVDZMOd1JeYpBi65J0MXXgnnKG/+zPSiz5DrO08an77D5gKXmTNtCwhfuUHSK15uXNBOfseyf08uW1LMQysfS2c838I77+f2JNfJbL31xX9fVaSjTaQnr2OTNsa0m3nkGk7h3TbGqid4bIz0NsbXHC182HefJiX10MlnSDUc4hQ1x5Cp54ndPJ5Ql27CZ98DqML1iIiIiIiItNCOp1h4Cy5ptXXV0L56Aqo1iRFOXUDBic4ZrmCjG2y56rUa/BO4MOTOcCWLVs4dsyjMe8ZxCtJ0dc/wObNmyt23kuisK0pzO5e59vDtWHLlXMs187L0BI7zP6nD7N/siexIS5ou55lnb8uavcd817Nc6nL4N57J3tmCdw5tK34Ey44+DWahvz9Gk5/rI1dc2/l4MwrsEcjcPTegvtv2bLF1/OflVruoObcVzD/9KPM73qEtv4JSnpNoUS4ge66JZyuX0p3/VJO1y2hv2bOaMmbLqBrAJ57YkrjLF4YWAOxNTAHaLfUpHpojB+laegIjfEjNMWP0Bg/Sn3y5FQHKyIiIiIiIjl6e3t5oILX86rJgQNFt2CuiGpJUtTmbSfKOMZQ3rZfXSODjG2y56rUayDF8lwAVNnSIw1ReM+5aY4PQjwNCxog6nevbmPYtujNHG++kLVH/oeWuHvLFIth26I3sa/tep8DkCB1Nq3j12s/yrzTj7H6+I+ZMbhvUsfrrZnH8+23cHDWVVhTLf/pOXsMRVvYO/tG9s6+kdrEKeaffoy2vu209T1LND0wJTHFI83ZRMRSuuuXcLpuKYOxtjO70bAxDEVbGIq2cLLpnDFT4XScxqFj2aTFEZqGjtIYP0LD0AnCNjlFAU8vNvvfWq8vDIiIiIiIiEh1qpYrRfkrBmJlHCO/K5BfXRyDjC0O1E/iXJV6DaRIXpfWgrhcYgzMrZ94v8k63rKe480XsuD0Fs45ejeNQ6NlptImwhOL7+TwzMsrH4hUnglxtHUjR2dcwuze7aw88VPaend4N4jP0127iKMzLubIjEvorV1wZl98nkbisZnsab+ZPe03g80wY3Afbb07mTGwh+b4YRrix4r+HRcjEa6nr3Y+vTXz6audR2/tfLrrlhCPtuo9kSMdrqU7u4JkDOusvqhLdFKfOJm976QucdK5T54kNkWJpkIyhEiFa0mF6pz7cB2pUC3J7H0q937kcS2pUA0ZEyUdio65z4SipE2UjImAMSMJidFmzDmsxZAmZNMY69yHbGr0cSZJOJPI3oYIZ4aIZIZythPZ7dGxyJj9svfpuHOvJJKIiIiIiMikVEuSIr/oVf6KgmLkrxrwq5BWkLH1MTZJUeq5KvUa3AV8p8TnrAB+MLyxceNG1q5dW2D36au/v3+kJM2p2Hx2pAYIYXEaREKIDA3zz+HyTZumNlDf3UAm8wEGdv2EyP77sbXNJM9/PatnrmD1VIcmFXA98B76koOETj1PuPMZQp3PEjr1PCY9hK1vI9MwG1vfTqZxDum5L8C0LmM+ML+Es+R+nsD529HQUGbDdymZBfpSQyO9FMxAJ6GBTszgKUx/ByY16KwYsxnnZgw21oitacbWNEOsiUxjO7ZpAZmm+WSaF0BNMxGgNXsTf8WBeDqJGerGDHZh4l2Y+GnMYBfJnuMc27uTaHoAY9O0z5pB1FhIJyGTxKQTI4/BQDgKoQg25NyPbkcgHMPGGiDaiK1pxEYbne2Yc29jjdhoA9Q0OfeR2jHJg0j2Vs4/oqpRKnsbAsikITmASQ6M3I9/3D/6eqeHMOlk9rVP5IwnMZkEpBLZ30+Ska85jFmpmffVh/xVnKEIhMLO782Ex26HwmCy96EI1oRHHg/P2dzt3H1MyDmeMdn7ENaEnPFQGAhBKDSy39i57HvBZhhpoG0zzujIWCb7swzPWyBnLJPGWOs03R7+G2TTzutvM5jh8Ux23KYhkzOe3R47lxk9XiY98ti47J9OJug+3YXJzjc3NxI2w/GbnG+qmNGf12Qfm/ykmhndNW9/67o/Y483bi7v8ci+hZ837lyuz8vyWi3s9d7MGTcTvo/dn1faebyO4cN53H6Wos/jfpqCX2Eq2JqxmLgr+JyCcRc/kUmn6e3rG3k9GxsbCYXCBeIRkUIymfSYWvL6TImUL//zVLdkPZvOuOt57nbu3Dml56/WJEW9McaU2Dw7/ypWpZIU5VwtKza2PqB9EueqyGtgrT0BnCjlOSbvW42NjY00Nzf7EU5VS659NQs3bRr3s57RF+03vtG5MX4pj5yJmmHWHFh15cS7+qChoeGs+NtRdWbOhhWXTnUUUpJZ40aSPT3sHBqtn7pp0ybq9XmqEKXgznSDPT08vHns50n/fRIpT09PD7/V50nENz09Pdyvz5SIL87mz1Njo1+tjcvjd9X6cnUy9isWUcZerC/Ggrztki6qF5B/nIVlHKPY2CZ7rkq9BiIiIiL/r707j7OmKg88/nsEXnZFcAmgiIqiICYRkbigqKATNW4BXKOISZwY45JRohkjmESNxo3JBHVGRsiConHBFVcQFeOGiAtEFkEQNYCAguDL8swfpzpWn/cudbtv3+rb/ft+Pvfzsc49Vaf6tR5O3XrqnCNJkiRJ0tStiiRFZl4PVI+XWgAAIABJREFU1EuI7zbhYer65y79jBb5j2r7zks4Rr3PsHOr21ot/waSJEmSJEmSJE3dqkhSNOoH6ntNuH+94MG0HtBfDFzf2t42Iu7SdeembnudieuAS4ZUX63/BpIkSZIkSZIkTd1qSlKcVW0/qOuOEbEzsHur6Ebge1M4J5p1Mc6uijufG1BP3H72iLU2lvxvMKSt+niSJEmSJEmSJK0aqylJ8dFq+6CoV18e7lHV9qmZOa2Fs2HTczt4gn3ruh8ZUfc0ykiLBffsOmojInYH7tEq+kVzPEmSJEmSJEmSVqXVlKQ4g7KA9oK7AQd23Pe51fbJ0zihlg9X24dGxNglzyNie+DQqnjouWXmDcCnquIjOp3hpvVOycyNHfeVJEmSJEmSJGnmVk2SIjNvAY6vio8aN5oiIh4JHNAq+gXw3imf29nA11pF2wFHdtj1SGDb1va/Z+a4aaiOq7b/NCJuP2qHiLgD8Pwxx5EkSZIkSZIkaVVZNUmKxuuB9jRNDwP+YljliNgVeGdVfExmXjGofmu/rD4Hdji3V1XbL4+Ih45oY9C5v3JcI5n5MeDfW0U7AcdFxBZD2tlASUjs1Cr+QmZ+clxbkiRJkiRJkiT1aVUlKZrkwmur4tdFxLERsctCQUTcKiKeSJkiavdW3cuAN63QuZ3C4qmYtgA+GREviohtWue2bUS8GDilqbPg45n52Y7NvQy4pbX9e8CnIuJ+7UoRsW9zTo9rFd9Mt1EekiRJkiRJkiT1alUlKRqvZ9OFqv8E+GFEXBARZwJXAh8EdmvVuR44LDOvXsFzexbwg9b2VsBbgSsi4jsR8V3Kuhpvab5bcAFweNdGMvOLwCuq4gOBb0TEjyLi6xFxGfB1ymiTtiMz89+RJEmSJEmSJGmVW3VJimZtikOB91RfbUZZTPu3gR2q764EHpOZX1rhc/sp8HDgW9VXWwN7A3uxODkBcBbw8My8fMK23gC8lDIyom0XYF9g56r8ZuAlmfnmSdqRJEmSJEmSJKkvqy5JAZCZN2Tm04BDKA/5h7kOOBbYKzNPm9G5XQw8gLLexGUjql5GmXZp/8y8ZIltvQm4P/AxFk//1HYLZeTJvpn51qW0I0mSJEmSJElSHzbv+wRGycz3A++PiD2A/YFdgQ3A1cA5wJcy84YlHDeWeV4bgTdExBspoxp+E7hD8/V/UhIrZzajQpYlM88CHhcRtwMeQhlNsi0lQXMB5d9g5ELhkiRJkiRJkiStRqs6SbEgM88Hzu/7PGpNEuJrzWel27oC+NBKtyNJkiRJkiRJ0qysyumeJEmSJEmSJEnS2meSQpIkSZIkSZIk9cIkhSRJkiRJkiRJ6oVJCkmSJEmSJEmS1AuTFJIkSZIkSZIkqRcmKSRJkiRJkiRJUi9MUkiSJEmSJEmSpF6YpJAkSZIkSZIkSb0wSSFJkiRJkiRJknphkkKSJEmSJEmSJPXCJIUkSZIkSZIkSeqFSQpJkiRJkiRJktQLkxSSJEmSJEmSJKkXJikkSZIkSZIkSVIvTFJIkiRJkiRJkqRemKSQJEmSJEmSJEm9MEkhSZIkSZIkSZJ6YZJCkiRJkiRJkiT1wiSFJEmSJEmSJEnqhUkKSZIkSZIkSZLUC5MUkiRJkiRJkiSpF5v3fQJasza0N84///y+zmPFXXvttfzwhz/8r+1zzjmH7bbbrsczkuaX8SRNj/EkTY/xJE2P8SRNlzElTc96jqcBz243DKq3UiIzZ9me1omIeDxwct/nIUmSJEmSJEmayBMy88OzaszpniRJkiRJkiRJUi9MUkiSJEmSJEmSpF443ZNWRETcBnhYq+gSYGNPp7PS7s7iqa2eAFzQ07lI8854kqbHeJKmx3iSpsd4kqbLmJKmZz3H0wbgzq3tz2fmNbNq3IWztSKai3hm85b1KSLqogsy87t9nIs074wnaXqMJ2l6jCdpeownabqMKWl6jCe+2VfDTvckSZIkSZIkSZJ6YZJCkiRJkiRJkiT1wiSFJEmSJEmSJEnqhUkKSZIkSZIkSZLUC5MUkiRJkiRJkiSpFyYpJEmSJEmSJElSL0xSSJIkSZIkSZKkXpikkCRJkiRJkiRJvTBJIUmSJEmSJEmSemGSQpIkSZIkSZIk9cIkhSRJkiRJkiRJ6sXmfZ+AtAZcDry62pa0NMaTND3GkzQ9xpM0PcaTNF3GlDQ9xlNPIjP7PgdJkiRJkiRJkrQOOd2TJEmSJEmSJEnqhUkKSZIkSZIkSZLUC5MUkiRJkiRJkiSpFyYpJEmSJEmSJElSL0xSSJIkSZIkSZKkXpikkCRJkiRJkiRJvTBJIUmSJEmSJEmSemGSQpIkSZIkSZIk9cIkhSRJkiRJkiRJ6oVJCkmSJEmSJEmS1AuTFJIkSZIkSZIkqRcmKSRJkiRJkiRJUi9MUkiSJEmSJEmSpF5s3vcJSPMuIu4OPAC4E7ABuAo4FzgjM2/o89yktSwitgIeBNwLuC2wEbgU+EpmXtjnuUl9mmVs2AdK02M8adYiIoDdgX0o190OwK8o1955wNemfe1FxPbAg4F7ArcGrgcuplznl025rb2BfYGdgc2AK4HvUPrDm6bZltRHPM2S8aRZiogNlN8yuwO7AtsDWwA/p1x7ZwPnZObNU2pvc2B/4D7ATsDNwI+Bb2Tmd6fRRqutXYEHAncBtqb8Td8HvpiZ106zrXkTmdn3OUhzKSKeCPwVcL8hVa4FjgdenZlXzOq8pFmKiKOBo5ZxiBMy8/AJ27x90+bhwLZDqn0D+JvMPHkZ5yZNRXMj+gDKje8DgPtTbrQXXJyZu0+hnZnFhn2g+rKS8RQRy/1hdNfMvGgJ7RpPmpmIuC3wROC/AY8Abjei+o3Ax4C3Zubnl9nuXYG/Bg6jJOFqCXweOCozT19GOwE8B/gLSiJkkCuBtwF/l5nXLbUtaVbxFBG7Az9Y2lkWmRmT7mM8aZYi4hDgIEoi+16Mf7H+GuDdwDGZee4S29wOeDnwJ8COQ6r9B/B64PhcxkP0iHgYcDRw4JAqG4GTgFct5X5yLTBJIU0oIrYEjgOe0XGXy4FDlnOzLa1Ws05SRMSBwPsY/QOg7Z+AP8rMjZOfmrR0EfFg4H9QHqTuMqb6spMUs4oN+0D1YVbxNOskhfGkWYuIfwT+kMFJgnH+CfizzPz5Eto9DHgXsE2H6gm8AXjFpA+DImIH4L3AwR13uRB4/LTfktX6MMt46iNJYTxp1iLiUsqoiUndCLyW8jJH534jIvYBTgbu2nGXTwJPycxrJjm5Jtn3euBlHXe5Dnh2Zr5/knbWAtekkCYQEbeiZDbrH5M3U24azqJkc9tuD3wiIh648mcorV0R8RDg42z6EPZq4JvARZRYbHsW8O7mxkCapf2AJzH+geqyzSo27APVo5nF06wYT+rJ/gx+oHozZVrAb1Cm0Bj0AOZZwKebt047i4hDKW+61gmKy4Ezm3bbD5WC8tb2mydsZ2vKA6T6gepGyjQa36Y8+Gm7G3BqROwxSVtSY+bxNCvGk1aRGyjX3NcoMXUxi/sMKNNAHQW8s+tBI2JP4HNsmqC4lhK351GSH22PptyHbdW1ncb/YtMERQKXUPrBepTstsBJEfGkCduZe65JIU3mZcATqrK3U6bOuAz+60fnE4C3Ars1dbYB3hsR95k06yrNmZcC35qgfqe5h5uh1CdR5mxccDHwIuDDC29MRMSdgFcCz2vVezLwEib8sSutoGuBqfwonXFs2AdqNZpaPFXOpozemMRPJqhrPKlvVwMnUqaf+UJm/mLhi4jYDDiAMj3TAa19HkCZeuyQLg0066y8i8UvR34LeElmntqqtyflLdgnt+q9OCK+kJkf6Pj3vLk5vwW3AK8B3pKZVzXtbACe3tS9bVPv9pSY2m9ac5trXVrxeKp8Cvj7pZ5sB8aT+nIZJY5OB74M/CAzb2lXaH7/HAK8irL+y4IjIuKLmfmuUQ0060/UI9B/Rvld9O7MvLGptyPw58Ar+HU/9kDKaL8XdvljmpGEL6iK308ZLXheq94jKbF036ZoM+CEiPjmupr6KTP9+PHT4UNZPOfnlIznwuflI+rvSnkTrl3/1X3/HX78TPNDmVOxfY0fuELtvLZq50JglxH1/7KqfzVw277/vfysnw/w4uba+zlwKuVm9hDKAmkHVtfnRctoZyaxYR/op8/PDOOpfZzTVvDvMZ789PIBvt5cS88Ftu5QfzPgHdW1l8DDO7Z3YrXfV4FbD6kbA9o6H9i8Qzv3Am6q9n3aiPp7UxYzbtd/Tt////iZr88s44myeHB7n+NX8O8ynvz08qE8oI8J6t+WMrqife1dBtxqzH5/XO3zM2CvEfWfXtW/EbhHh/PbMOD+7W3D/kbgNpQRI+36J/T9/8ssP073JHV3JIsXZjydMq/cQJn5I8oclW0viYidVuDcpDWrWQz4z6riP8rmTdMhXkeJ0QW3oYzykGblI5QfbTtk5sMz88jM/LfMvHhaDcw4NuwD1acVj6cZM57Ul6OAPTPzuMy8flzlLG9CP5/yMLatvh43ERF7A09pFW2kzLE9cA7+LE9oXkSZYmPB3SmL9o7zasoD4AX/nJnvHlY5y5z5dd93VERs0aEtacHM4mnGjCf1IjPPbvqCrvWvAp7J4umfdqYsvD1QMwLolVXxSzPzeyPaORH4l1bR5pSXNcd5LiXBuOA8ykjCgX9jlhGyz6b0lwueERH36tDWmmCSQuqgGW5f3yAfPe4/oJn5WeALraLtgcOmfHrSWvdUFk/lcXoTW0M1sfnqqvgI16bQrGTmBZn5vayGJ0/ZTGLDPlB9m1E8zYTxpD5l5scyc+P4mov2uZkyeqnt0R12PYLFzxvek5nnjGnrBuDvquKRD3CbaT/a00Ql3R4evYsyPeKCuwAHddhPAmYeTzNhPGneNP3KN6rie4/Y5dHAnVvbF1Gu33GOZnEy5NCIuM2Yfer+63VNPzdUkyw5qVW0Gd2S9WuCSQqpmwdR5ldccCFwWsd9j6u2nziNE5LWkXrO7jqmhjmVMrxywW8AvzOVM5JWh1nFhn2gND3Gk+bRF6rtnSKiXgi79vhqu2sfdRKLF+TdLyJ2GVH/sSxea/O0zLxwXCNN0rN+MGVMaRaWEk+zYjxpHl1Qbd9uYK2i/v30ri6jNzLzAuDzraItgMcMq9+sB3i/VtG1wHvHtdOo+8v6nNcskxRSN4+ttj89wTC0T1fbB0bEtlM4J2nNi4jtgIdWxZ/qsm8To5+pih83jfOS+jbj2LAPlKbHeNI8umpA2dA3SJuFsPdoFV0HnNGlocys6wabxk1b/V2nvrBRx5T3iZqFieJpxownzaOtqu2rR9Sd1TVet/Olpn/r4kvAL1vbe0bEPTruO9dMUkjd/Fa13ekmG6CZG/yiVtEGYK8pnJO0HuxNeUthwQ8y8ycT7P+laruOZWlezTI27AOl6TGeNI92HVB25Yj69XX+1cy8aYL2ZtJHUaYI+VVre5dmvSdpJU0aT7NkPGmuNFPW7lcV19M/LdS9I2UE+YJfAWdO0Nysfj/dBHx1grbWDJMUUjf1nHZDF9UZoq4/ao48aa5FxJYRce+IeEhE7B8ReyxjCLOxJw02y9gwDrVuRcTOEbFvRDw0IvaJiJ2XeUjjSfPogGr74jFz8c/kOm8W5t2jKu7cVmb+ik2nCTGmtNImjaehIuLOEbFfRBwQEXsvJylgPGlOHQG0pwQ8l00f8C+or8fzJ4y9Oh72iIjNB9b0fm9Jhv1jSmpExNbAblXxJRMepq6/59LPSFrV/hG4G5sOubwpIr4BfAI4NjMv73i8OlaWG3t3iYitxi1YJc2BmcSGfaDWsX0i4kLgrvUXEfETyrzEx2fmKV0PaDxpjh1RbX98TP1p91HDrvO7sfiZxvWZecUS2mqPSNoTOH3CY0iTmDSeBnlURFwGbJI4j4iLKGsd/Z/M/PIExzSeNFci4tnAsa2iW4AXjJhGc1l9U2ZeHhE38OtnHRso94nnTbutAfXXxf2eIymk8W5HmQt1wY3Af054jB9V23dY1hlJq9debJqggHLDuz9wNHBxRPx1RGzW4Xh1rFw64fn8FGhPL3ArYKcJjyGtRrOKDftArVc7MiBB0fgN4CnAJyLizIjYp+MxjSfNnYh4DJuugXT8mN2W20fV1/mwt8Prdur9ltKWMaUVs8R4GmRnBiQoGrsDhwNnRMRnI6JOjg9jPGlViYh7RsRBrc/vRsTTI+K1EfFdSuxsaKpvBI7IzM+OOORy+yaAy8Ycc0Hdby23H1wXseRICmm87artX06wwOGCeoGc+pjSerI18FfAARHxe5l57Yi6dax0XWwKKAsER8T1wPYjjinNo1nFhn2gNNpvA1+JiGdn5vvG1DWeNFciYkfgHVXxhzJz2FQaC5bVRw2ov0VEbNlMJzPNdgbtY0xpRSwjnpbjEcA3I+JJmTluRIPxpNXm+cCLxtRJ4BTgFZn5rTF1Z3KNNyNn6xcyl9sProtYciSFNF79H4OlTBNz/ZhjSvMsKQtB/U/gYOBOwDaUERW7Ar9HuSGvY+dA4D1jRlQYf9Jgs4oNY1DrzRWUN/OeCdyXMqJiC+C2wG8CLwDqH8FbA/8SEfXbsTXjSXMjIm4F/Avlvm7BNcALO+y+3Gu9vs4HHXMa7Qxqy5jS1C0zntouBd4GHEqZo34HSh91O8riwUcCF1b77AicHBH3GnNs40nz6H3AazokKKC/309LaWtdxpIjKaTx6qlrlrKoVf3Wz9ZLPBdptfkUcGJmfn/I95c1n49GxN8C7wEe3Pr+sZQ3JP5hyP7GnzTYrGLDGNR68kzgfUMWUby6+ZwN/GNEPA84Btiy+X4DcGJE7DFi3SPjSfPk74Hfrcqel5ld5tVe7rVeX+dgH6X5tpx4gpLQeDzwscy8ZcD3Vzafr0fEmymj1v+KX7+YvAMlmb7fiBF8xpPm0WHAYRHxBcp0T+ePqNvX76eltLUuY8mRFNJ49Q/NDQNrjbZlte2ivVoTMvOMEQmKuu6lwEFAvYDbKyNimyG7GX/SYLOKDWNQ60Zm/uuQBMWguu8Ank5ZpHHBrsCfjtjNeNJciIgXAn9eFb8hM0/qeIjlXuv1dT7omNNoZ1BbxpSmagrxRGZelZkfGZKgqOvenJlHD2hzX+DJI3Y1nrSqZOaLMzMWPpTZGu4MPA44jsWjDQ4AvhYR9x9xyL5+Py2lrXUZSyYppPHq+fIHZUXHqbOeo+bgl9as5u3SZ7F4wd47AI8asovxJw02q9gwBqUhMvMDwD9XxX8wYhfjSateRDwdeGtVfDzw8gkOs9xrfdAbo/ZRmjtTiqclycxjgM9XxfZRmluZeX1mXpqZH8vMP6RMy3lWq8oOwIciYochh+jr99NS2lqXsWSSQhqv/o/BNhEREx5j2zHHlNaNZgjmh6virkmKOpZGamJ1XXbwWvNmFRv2gdJob6q27xsRdxxS13jSqhYRjwNOANrX5QeAP5xwkfdl9VED6t80ZBq15bYzaB9jSlMxxXhajrqPekREDJv23XjSXGmeKxwMtKdN2xV42ZBdZnKNZ+b1wM3LbGtdxpJJCmm8KygLAy/YgvLm9yR2rbb/c1lnJM2/z1bbew6pV8fKnQbWGu6OLF5/6RZKTEvzblaxYR8ojZCZ32bxNR3APYdUN560akXEwykLkLb7hk8DT8vM+mHLOMvto+rr/PKO7dT7LaUtY0rLNuV4Wo7Psbjf2R7YeUhd40lzJzOvAI6qig8fUn25fRPALmOOuaDut5bbD66LWDJJIY3RZEF/WBXvNuFh6vrnLv2MpDWhXiTu9kPq/Ue1vdzYu3jEgqbSPJlJbNgHSp1cWm0P7NOMJ61WEbE/ZZRrezqKM4AndV2npTLtPmrYdX4hi6cQ3Toiht1TLrctqZMViKcly8zrgKuq4mExYjxpXn2Qxcm4XSLiLgPqLatviog7sDiuN1LiZpBZ9YNrikkKqZv6Pwh7Tbj/vcccT1pvbqy2txhSz9iTBptlbBiH0mhd+zQwnrTKRMR9gU8A27WKvwk8pnnAuRQzuc4z80bggqW2FRFbAnfr0pbUxQrF03J16qOMJ82rzLwa+FlV/BsDqtbX490jYpIFreu+6YLMvGlgTe/3lsQkhdTNWdX2g7ruGBE7A7u3im4EvjeFc5LmWX3TMGwY/3dZfGO9exNTXT242q5jWZpXs4wN+0BptK59GhhPWkUiYk/KFDS3bRWfAzw6M69ZxqHr63y/EfPgDzKTPgrYF9iytf3jzFwXU2po+lYwnpZzTpsDO1XFK9JHYTxpdamTc2TmT4CftIq2pFy3Xc3q99PmwAMmaGvNMEkhdfPRavugCRY6rBcEPjUz18WiN9IID6m26+mfAMjMXwCnV8UHd2mgidGDquKPdDo7aZWbcWzYB0pDRMSdgHpKgYF9WsN40qrQTIXxGRavi/ID4ODMHPUQc6zMPJfFb2RvS8cHNBGxLfDA9uHYNG7a6u869YVD6nqfqCVZyXhapt9h8boYN7H4IW3NeNLciYjtgR2r4p8Oqf6xanulrvG6nQc1/VsXDwa2aW1/PzO/33HfuWaSQurmDBYvKHo34MCO+z632j55GickzauI2AH4/aq4Xki77cPVdh1TwzwcuGtr+6fAVzruK82DWcWGfaA0XH2NX5KZ542obzypd82onM+yeCHPHwGPzMwfTamZpfZRT2HxVDlfz8zLRtT/OIvn0T8wIuopZzbRJAcPr4qNKU1sRvG0VHXcfTkzfzmivvGkefRYoP3Cx+XAj4fUrfum53R5WSQi7g48rFV0IyVeBsrMSyhTvS3YDjhsXDuNdXu/Z5JC6iAzbwGOr4qPGvcfs4h4JHBAq+gXwHune3bS3HkjsENreyNl7tZh3gO053B9aEQ8YlQDTWweVRW/q4llaa2YSWzYB0qDRcS9gf9RFX9o1D7Gk/oWETtSpqS5e6v4csob3z+YYlP/j8ULmT61iZlR57YV8PKq+LhR+2Tmz1gcdwEc3eH8jmDx9GkXU96ElzqbYTxNLCIOBP6gKh7XRxlPmisRsTXw6qr4oyN+23wSuLS1vTvwnA5NHc3iRMj7O0zjVvdfL2/6uaGafvIpraJB941rlkkKqbvXA+0h9Q8D/mJY5YjYFXhnVXxMZl4xqL40byLi5RHReQ7HiNg8It7Epm8GvD0zh73pQDOX6f+uit8ZEbuMaO4VwENb29cAf9/1XKV5MOPYsA/UmhURvxURL4mIbcbX/vU+wCnA9q3i64G/67C78aReNFNinALs3Sq+GnhUZp4zzbYy8zssTqRtAE6IiFsPObcA3grco1V8ISXZMc5RlAc5C/4gIp42rHJE7EV5aabtbzJzY4e2JGB28RQRB0fEcyZZ16V5aeUDwGat4h8Db++wu/GkmYuIN0TEfhPusyNlZMQ9W8U3A28Ztk9m/gp4TVX8xuY6HtbO04FnVm3UL30N8n+BH7a27wm8ZdiLKU3/+E+U/nLBiZm5btYfi8wcX0sSABHxCuC1VfHbgL9dGIYcEbcCHg8cA+zWqncZsHdmXj2Lc5VWWkScRnmwcgblR+hngXMz86aq3m2AxwBHAr9VHeYCYP/MvHJMWztSFgpuL056MfBC4CPZdGbN3OCvBJ5XHeLIzDRJoZmKiAcDWw/46jdZ/GPupyy+8W27bNSN6Sxjwz5QfVrJeGreNj0VuJLyUOeDwNfqJEDzo/I+wB8Bf8ziBUIBXpyZx4z9YzCe1I+IOJVNpxd7FfDlJRzuG5l51Zj29gC+xeK5tb9FiZXTWvXuCbwOeHJ1iMMy831dTiYi3kGJywW3UB5EvWXhPCNiC+AZwJtZvLjx2cC+9T2sNMqs4ikiDgfeRZlC6n2Uh7Jn1m9xR8RmwP2B51P6wfZLybcAh2TmB7ucjPGkWYuIsyj3dF8FTgI+B3w3M2+s6gWwJ3Ao5ffO7apDvTEzXzamrS0oUzG1E4w/A15CSQrc1NTbsSn7SxbH07GZ+acd/66nASdWxf8G/GV7etAmsfgW4L6tetcC9+17VNYsmaSQJtD8WDwZeFz11c2Uh0LXUOb53qH6/nrKkM8vrfhJSjPSSlK0/YoyfPIaSlzsRBlCOWjk3k+Ah46Zu7vd3kMpwzPrIZJXUxam24HyEGez6vuTgSelHZ5mLCIuYtMFdSd1QmYePqadmcSGfaD6tJLx1EpS1H5KWT/iF5S5hHdl8YOYtjdl5ku7nojxpD5ExDTvhR7eTjSMaPOplAc09Zujl1PeML0DZS7/+vt/yMwXdj2ZZiTU5ykPads2UvrCX1HWgNmu+v4K4MHrZVFSTc+s4qmVpKj9iPJg9Trg1pR7vfr6hjLt2osy8x+6nozxpFlrJSnaNlKu86ub/709cGcWj2JtOwE4ossUz820Sl9k0wW3r6W8SLk15T5si+r7rwIHZub149potXUs8CdVcQKXUPrCu7BpsuUW4CmZ+W9d21kLOg8Xk1TmEY6IQyk3CU9tfbUZpZMe5ErKWwv+mNR6sCWL52Qd5uPAc5rpajrJzNMj4rGUN4jaNxM7AL89ZLcTKTcqJii0Zs0qNuwDtQ7dsfmM8nPg+Zn5r5Mc2HjSepGZ72nefD2OxaOhbt98BnkjZQTuJO38MiIeTekL2+szbaC8dTvIRcDjfaCqObVr8xnlx8CzM/PTkxzYeNIqsYGSKBjn55T1jN7e9bdNZp7TjF44mcUvwWzHpsmSBZ8BDp0kQdF4AXADZVTGgqAkFncbUP+XlGcl6ypBAa5JIU0sM2/IzKcBhwBnjah6HXAssFeXt4ykOfQayrym36W8+TnOtZQb3Ydl5mMnSVAsyMzPAXtRpsT45Yiq3wR+PzOf0cw7Ka1ps4oN+0CtUd+mrAlxCuWt1C7OpTxE3X3SBMUC40nrRWa+mzJV2onAjSOqnk55Q/VlS3nBpFn092DKNDXnj6j6M8p0a/tk5rcnbUdkUQfqAAADJUlEQVSasc9R5r8/jTKyb5xbgDOB/w7sMWmCYoHxpBl7GuVe7DOUpMM4SZla7GWU6/xtk/YbmfktYB/KdIOjpi88jzLV56OWMtVmZt6SmX9OSfh9YUTVjcC/AvfJzPeOqLdmOd2TtEzNXKv7U95i2EAZinYO8KXMvKHPc5NmpRkSvBdlaqedKW8g3IoSD1cB3wO+nZldkhld29waeBBwb8ob4wvDQb+SmaNupKU1bZaxYR+otSgi7kJZvHc3yvROW1PegLuK8lbqV8atpbTEdo0nrXnNwqAPocTY9pTY+iHlOv/RlNvaB7gf5d50M8popO9QYnhUskRalZpRSXcH9qBMe7MDZbrP6yh91CXAVzOzy0PeSds2njQTzZSY96Bc57tRpjPbgpKku4YyaufMaV7nzToV+1MS6jtRXsL8cdPOVJNvzVqBD6L8bVtR/q7zgC+uROzOE5MUkiRJkiRJkiSpF073JEmSJEmSJEmSemGSQpIkSZIkSZIk9cIkhSRJkiRJkiRJ6oVJCkmSJEmSJEmS1AuTFJIkSZIkSZIkqRcmKSRJkiRJkiRJUi9MUkiSJEmSJEmSpF6YpJAkSZIkSZIkSb0wSSFJkiRJkiRJknphkkKSJEmSJEmSJPXCJIUkSZIkSZIkSeqFSQpJkiRJkiRJktQLkxSSJEmSJEmSJKkXJikkSZIkSZIkSVIvTFJIkiRJkiRJkqRemKSQJEmSJEmSJEm9MEkhSZIkSZIkSZJ6YZJCkiRJkiRJkiT1wiSFJEmSJEmSJEnqhUkKSZIkSZIkSZLUC5MUkiRJkiRJkiSpFyYpJEmSJEmSJElSL0xSSJIkSZIkSZKkXpikkCRJkiRJkiRJvTBJIUmSJEmSJEmSemGSQpIkSZIkSZIk9cIkhSRJkiRJkiRJ6oVJCkmSJEmSJEmS1AuTFJIkSZIkSZIkqRcmKSRJkiRJkiRJUi9MUkiSJEmSJEmSpF6YpJAkSZIkSZIkSb0wSSFJkiRJkiRJknphkkKSJEmSJEmSJPXCJIUkSZIkSZIkSeqFSQpJkiRJkiRJktQLkxSSJEmSJEmSJKkX/x8E/WzGAYY3UQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "T_0ZbkB2NcPf", + "outputId": "94a91728-9728-4ff8-8ee2-db6904c54cb7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 868 + } + }, + "source": [ + "def calc_grad(x):\n", + " if x <= -0.9:\n", + " return -40\n", + " else:\n", + " return x * mt.cos(8 * x) + mt.sin(8 * x) + 3 * x * x\n", + "\n", + "# optimize with the specified solver\n", + "def solve(x0, solver):\n", + " x = np.zeros(nb_iters)\n", + " x[0] = x0\n", + " for idx_iter in range(1, nb_iters):\n", + " g = calc_grad(x[idx_iter - 1])\n", + " x[idx_iter] = solver.update(x[idx_iter - 1], g)\n", + " return x\n", + "\n", + "# Adam & diffGrad\n", + "x = {}\n", + "x0 = -1.0\n", + "solver = Adam(lrn_rate, beta1, beta2, eps)\n", + "x['adam'] = solve(x0, solver)\n", + "solver = diffGrad(lrn_rate, beta1, beta2, eps)\n", + "x['diffGrad'] = solve(x0, solver)\n", + "# visualization\n", + "plt.rcParams['figure.dpi']= 300\n", + "plt.rcParams['figure.figsize'] = [6.0, 4.0]\n", + "plt.plot(np.arange(nb_iters) + 1, x['adam'], label='Adam')\n", + "plt.plot(np.arange(nb_iters) + 1, x['diffGrad'], label='diffGrad')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5s0WgNckNcPj", + "outputId": "820e033a-80c4-4989-b381-8dc6b0794d43", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 868 + } + }, + "source": [ + "def calc_grad(x):\n", + " if x <= -0.5:\n", + " return 2 * x\n", + " elif x <= -0.4:\n", + " return 1.0\n", + " elif x <= 0.0:\n", + " return -7/8\n", + " elif x <= 0.4:\n", + " return 7/8\n", + " elif x <= 0.5:\n", + " return -1.0\n", + " else:\n", + " return 2 * x\n", + "\n", + "# optimize with the specified solver\n", + "def solve(x0, solver):\n", + " x = np.zeros(nb_iters)\n", + " x[0] = x0\n", + " for idx_iter in range(1, nb_iters):\n", + " g = calc_grad(x[idx_iter - 1])\n", + " x[idx_iter] = solver.update(x[idx_iter - 1], g)\n", + " return x\n", + "\n", + "# Adam & diffGrad\n", + "x = {}\n", + "x0 = -1.0\n", + "solver = Adam(lrn_rate, beta1, beta2, eps)\n", + "x['adam'] = solve(x0, solver)\n", + "solver = diffGrad(lrn_rate, beta1, beta2, eps)\n", + "x['diffGrad'] = solve(x0, solver)\n", + "# visualization\n", + "plt.rcParams['figure.dpi']= 300\n", + "plt.rcParams['figure.figsize'] = [6.0, 4.0]\n", + "plt.plot(np.arange(nb_iters) + 1, x['adam'], label='Adam')\n", + "plt.plot(np.arange(nb_iters) + 1, x['diffGrad'], label='diffGrad')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + } + ] +} \ No newline at end of file diff --git a/diffGrad_v2.py b/diffGrad_v2.py new file mode 100644 index 0000000..3268ef4 --- /dev/null +++ b/diffGrad_v2.py @@ -0,0 +1,106 @@ +# Fixes a bug in original diffGrad code +import math +import torch +from torch.optim.optimizer import Optimizer +import numpy as np +import torch.nn as nn +#import torch.optim as Optimizer + +class diffgrad(Optimizer): + r"""Implements diffGrad algorithm. It is modified from the pytorch implementation of Adam. + It has been proposed in `diffGrad: An Optimization Method for Convolutional Neural Networks`_. + Arguments: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + amsgrad (boolean, optional): whether to use the AMSGrad variant of this + algorithm from the paper `On the Convergence of Adam and Beyond`_ + (default: False) + .. _diffGrad: An Optimization Method for Convolutional Neural Networks: + https://arxiv.org/abs/1909.11015 + .. _Adam\: A Method for Stochastic Optimization: + https://arxiv.org/abs/1412.6980 + .. _On the Convergence of Adam and Beyond: + https://openreview.net/forum?id=ryQu7f-RZ + """ + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + super(diffgrad, self).__init__(params, defaults) + + def __setstate__(self, state): + super(diffgrad, self).__setstate__(state) + + def step(self, closure=None): + """Performs a single optimization step. + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data + if grad.is_sparse: + raise RuntimeError('diffGrad does not support sparse gradients, please consider SparseAdam instead') + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p.data) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p.data) + # Previous gradient + state['previous_grad'] = torch.zeros_like(p.data) + + exp_avg, exp_avg_sq, previous_grad = state['exp_avg'], state['exp_avg_sq'], state['previous_grad'] + beta1, beta2 = group['betas'] + + state['step'] += 1 + + if group['weight_decay'] != 0: + grad.add_(group['weight_decay'], p.data) + + # Decay the first and second moment running average coefficient + exp_avg.mul_(beta1).add_(1 - beta1, grad) + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + denom = exp_avg_sq.sqrt().add_(group['eps']) + + bias_correction1 = 1 - beta1 ** state['step'] + bias_correction2 = 1 - beta2 ** state['step'] + + # compute diffgrad coefficient (dfc) + diff = abs(previous_grad - grad) + dfc = 1. / (1. + torch.exp(-diff)) + #state['previous_grad'] = grad %used in paper but has the bug that previous grad is overwritten with grad and diff becomes always zero. Fixed in the next line. + state['previous_grad'] = grad.clone() + + # update momentum with dfc + exp_avg1 = exp_avg * dfc + + step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 + + p.data.addcdiv_(-step_size, exp_avg1, denom) + + return loss