blob: de5fae5c132bff594bb7f71e028c70c728b6dcee [file] [log] [blame]
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a multi-layer perceptron (MLP) model \n",
"\n",
"In this notebook, we are going to use PySINGA to train a MLP model for classifying 2-d points into two categories (i.e., positive and negative). We use this example to illustrate the usage of PySINGA's modules. Please refer to the [documentation page](http://singa.apache.org/en/docs/index.html) for the functions of each module."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from builtins import range\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To import PySINGA modules"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from singa import tensor\n",
"from singa import optimizer\n",
"from singa import loss\n",
"from singa import layer\n",
"#from singa.proto import model_pb2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task is to train a MLP model to classify 2-d points into the positive and negative categories.\n",
"\n",
"## Training data generation\n",
"\n",
"The following steps would be conducted to generate the training data.\n",
"1. draw a boundary line in the 2-d space \n",
"2. generate data points in the 2-dspace\n",
"3. label the data points above the boundary line as positive points, and label other points as negative points."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We draw the boundary line as $y=5x+1$"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# generate the boundary\n",
"f = lambda x: (5 * x + 1)\n",
"bd_x = np.linspace(-1., 1, 200)\n",
"bd_y = f(bd_x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We generate the datapoints by adding a random noise to the data points on the boundary line"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# generate the training data\n",
"x = np.random.uniform(-1, 1, 400)\n",
"y = f(x) + 2 * np.random.randn(len(x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We label the data points above the boundary line as positive points with label 1 and other data points with label 0 (negative)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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/GOzoM01ol5dbK1eAqLS04/5uFaVfOFEAaiWwQpEByNjZvcaVu3XQavbvoiK+iMqKWIyH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\niB4lomIiKu7Tp4+zkzXh+9hjztIYx2LAL35hfV3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"text/plain": [
"<matplotlib.figure.Figure at 0x7f986a5ac198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# convert training data to 2d space\n",
"label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)])\n",
"data = np.array([[a,b] for (a, b) in zip(x, y)], dtype=np.float32)\n",
"\n",
"plt.plot(bd_x, bd_y, 'k', label = 'boundary')\n",
"plt.plot(x[label], y[label], 'ro', ms=7)\n",
"plt.plot(x[~label], y[~label], 'bo', ms=7)\n",
"plt.legend(loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the MLP model\n",
"\n",
"1. We will create a MLP by with one dense layer (i.e. fully connected layer).\n",
"2. We use the Softmax function to get compute the probability of each category for every data point.\n",
"3. We use the cross-entropy as the loss function.\n",
"4. We initialize the weight matrix following guassian distribution (mean=0, std=0.1), and set the bias to 0.\n",
"5. We creat a SGD updater to update the model parameters.\n",
"\n",
"2 and 3 are combined by the SoftmaxCrossEntropy."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2, 2) (2,)\n"
]
}
],
"source": [
"# create layers\n",
"layer.engine = 'singacpp'\n",
"dense = layer.Dense('dense', 2, input_sample_shape=(2,))\n",
"p = dense.param_values()\n",
"print(p[0].shape, p[1].shape)\n",
"\n",
"# init parameters\n",
"p[0].gaussian(0, 0.1) # weight matrix\n",
"p[1].set_value(0) # bias\n",
"\n",
"# setup optimizer and loss func\n",
"opt = optimizer.SGD(lr=0.05)\n",
"lossfunc = loss.SoftmaxCrossEntropy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Each layer is created with a layer name and other meta data, e.g., the dimension size for the dense layer. The last argument is the shape of a single input sample of this layer.\n",
"* **param_values()** returns a list of tensors as the parameter objects of this layer\n",
"* SGD optimzier is typically created with the weight decay, and momentum specified. The learning rate could be specified at creation or passed in when the optimizer is applied."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the model\n",
"\n",
"We run 1000 iterations to train the MLP model. \n",
"1. For each iteration, we compute the gradient of the models parameters and use them to update the model parameters.\n",
"2. Periodically, we plot the prediction from the model."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training loss = 0.704490\n",
"training loss = 0.483168\n",
"training loss = 0.411447\n",
"training loss = 0.362376\n",
"training loss = 0.326843\n",
"training loss = 0.299900\n",
"training loss = 0.278710\n",
"training loss = 0.261552\n",
"training loss = 0.247332\n",
"training loss = 0.235318\n"
]
},
{
"data": {
"image/png": 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durtVX7oL1eg8Gy0tLfyXv/yF7733Xh4+vIoBWeI6vXbzUxHl5WX2f9nt4bff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\nQ4fK62ALo0w6lbo6EewlEwwlJR05qGT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"text/plain": [
"<matplotlib.figure.Figure at 0x7f986abf16a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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X3d9BhnU8q1fHd820CQzU9ymE2Qk++OArVFYeTzr9IE8+2cYxx9TFq0SWykot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\ncMLB4XA4HL1wwsHhcDgcvfj/AbxRnkNegXHGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f986abf92e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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9nnGQ6uudd4Kjeb0wUZM0N2u1k4jXUSGReJq99votnZ2/oaionZqaGq6++mom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\nLBaLxdILKxwsFovF0ov/HwP2pdP3I630AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9868545b38>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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m7jMn2wzAXAp6xclWDLopamoCp9E18FdJQP72ugIwbbkqh3yWZXQkS65mkqD7\nZurUeILcu59yXVfYRmgWKhvbW5sgqu3ZcieXNZptTF76e4YJ6Q6Bdikvb5Lq6hZZsmRr4DnMyqFD\nFB1SU94ktWyS5ZVHiSglTdUDpJaP81YOtWySpuoBndo06USYJP8IprpK3k1hWFLvQGiTBORv/ifQ\nazQ8B/wVXZH1OwHHjAW2AH/JbNNtzp2Lcuit5Kdio1gUZFLF4Bobw5PiQI+W/fefeaSbnNBMsqpq\nLm3Plju2C9XY3Bvbt2+XP/3pT3LbbbdJXd1sgbDCdXb9ZjYRtcuxJUtkLmd3te+DLGe09OMT8ZLg\nzP3RIbV8LDVsEUWb1LBFaqmX5Yzu7LCoBaBzuVmS+iPYCjDDD100PgdgMHBw5nk/4A1gv6xjxgK/\ni3vuuMqhWEo19zbFpiCTaE+U0Kuq6vr72q/m1bmlUvHbl0R2cdzvapI7UU7p3XbT1w6yZLS2tsqL\nL74od9xxh5xwwglSXV0tgAAybNh1Ul7elJPCsuortshczg49aWP1QHno6P+ScsLqI3VI/8oWWT71\nP6WRtMzlbJnJTYHKRqZMSX66l8TMIY4AMyijpDKk8z5BtxPCb4Hjs97rEeXQ1wquFYJiVZD5LocY\nZS6ZOjXe8UFC1lRALUyom4RxVZVeb8DDVknGaXvQ/9qLwPHKXZeX6+deGQmPtrY2Wblypdx5551y\n0kknSb9+/XYog/3220+mTZsmv/71r+WDDz6IFQkVNlA2ziKp7y7Es086bpwsZ4zUUr9jZpCmUdI0\nygxulqYbZ9itLzpyZPzGRxHlNE46VK0HZg6J5jkopUYABwF/Dth9uFLqFeBd4BoReT3Ja0NxlWru\nLZLI0i0E+WYrm0LDa2p0aLjt8UGYCqhF5UF4Za1bWrpn3I4cqffvv7995vOoUeYS0X7CCgn6a7eJ\n6NyQo4/uYPXqVTtWO1u6dClbtmwBYJ999mHy5Mkcc8wxjB07lkGDBnU7Z3b5bj3u686OnI6sxJL0\nxIksWpTu2pflLZRtb+jMIQijuhr23Zcxy3/Bu9v2YgGns5466ljPRJ4gXVMOm8+P7jCAf/7TfJ1c\nchhEdEntf//37pX6Ojrg1Vejk2biCDBDkoZoBZ8/SWgY0f+SGuAlYGLAvt2Amszzk4C1hvNMAVYC\nK4cPHx5DdbuZg0hhbOB+CuHLKERoeNyYf1NAic11owoFzp5t/99sajL7V0DPBIJmHWZ/RbvAEQLI\nZz/7WbnR+Ls+AAAgAElEQVTswgvlhWnTZMs111j/mN5vdeutEYURFy/r6lGvrt7R4B2zyKlvy9yK\nC80zBv9JP/7Y/GNMn2435TJ5+bPtkzbY1Bi39/rbC7CQqWhRRSsB5cAzwFWWx78FDIw6Lq5ZKemV\nsPoihVSQhfBlFCo0PPt4T6BWVGjLQ3W13Tls+zPquEmT4iltU2QW6PNl/587Ojrkhz98Wyoqwtct\nqKjYLm+88c/ozrTQ2KGnmPWyOSHCWybNRntnt8vUbhtnTVT4V7Z9Mqof4nyPqJsvFwEWYK+lWJYJ\nRefA/wK413DMpwGVeX4o8E/vtWnbmaOVChVNVCgFWQhfRqFDw72s5XHjOu3u3n+iqkrLgahz2M7E\noo6bNCme0rZRSh0dHbJ69Wp54IEH5Mwzz5Q999xT4GYxRRVVVIjMnd1iziZctsz6Jur2e2yKCCuD\nzj+9SZCXlopcconZAZT9J4iaLlZW6j9D2Nqj2SFvNsIkTniYzbQ9AQFWTMrhy9rMxSo6Q1VPAi4F\nLs0cczk6zPUV4EXgCJtz55vnkKvzs9AUWoEV4vymeyCXxeu9c5qS2mbPDlegUco1qZm+qSSGX6hH\nHWcIS49tFquo2C5nnHGxDB48WDL3ngwZMkTOOecc+eY3lxjWSs7IqHFLwjulujq/hTTmzo0enafT\n0Sagiorc/rTZf/50ujOL0Jsu2vwhbEcuttEDcabteQqwolEOhdx2xgzpnoomSlpBRt0DudzLUUlt\n6XSwgrOxiOQy089WOMuWmevueL9XVH0e/3G2Stsr6ZBKtUtpaat0xvh3CGyTCj6W2+q+Ls9efLGs\nffVV6ejoEJFof0VNqkXmlpxr7pgw4W4j4GzqXaRSejRhM+LOFtg20+3sZDHTj+N9rzgzAlt7YqFu\n7giccuij9FWnedL3QFRSm+n8Yfe6V67C5PzNVkDeTD9bcEcNMvv31zLqllvM3yNbac2e3VlQLqi0\nxj/+8Q+ZPv1JqajYJkptFW0iCk782pH1myXYjDWQ2GR2AOdbT8T2jzJ9ejwNnut02NSedFr/EEHT\n0kmT7PohypTlc8T3FE459FEKHU2UNPX1epB39NF2ZShsFVwuSWre/Wyq0ukvnmbb1rjZ1BUV2nwd\n1f5UqnN5zDDZ9rvfvS+PPvqoXHTRRVJXVyeQFqi3akeXxLEsrdxZfiNTloJPumYKh21lZeE2+Xyd\nqv5txgy7WYZSZkUSNRqJe8N5P1RYHwT1Q9CPa+vUKgBJKQe3nkMPk0TN/57i5z+Hb3xD3xF+Skv1\ncpNBhOWTZK+lsGZN/IVpoOsykLns9+PlCDzxRPQCQ35MC85nH7dhg3kFtZNPLgMuoba2kqOPPpoj\nj/wpv/lNf6ulKxuoZj2ZP0xWEstXvgIfPb2SBSfOYn3HXtS1vaHzAUy5BKA7JawTw5Iq/KTTnQkR\n/i+czT33wLe/bT4X6Jvio49yT96Jc8MF/VBBZPdDb6xJ2wM45dDDmBYYsbn3eopNm4IVA2jFUF4O\nra3d9wUpuKAkso4Ofe/EWaYRwq8bl9razkVocllBzYbqahgwYAvXXfc3GhoOBLoLi1SqhunTV3Pd\ndcMoLS2NtXRlNQ3UkdHE2Vq5qYnKCcczuTFC0PlRKlwx9O+P9YLJnrA8/3yYPz/4mLY2qK/XGYym\nTMWyMhg4MPfs1jg33OOPmzs/ne5Uftn90NNr0vYAJb3dgF0N779VW6vvC6X0o19Y9QRNTTBvnl5N\nbd687jLhlluCFYOHClnEK/t+8w/Gtm3T59y2Ta8cFlcxQDLL65aXwzHHwNq1+nt7g8ukaWzcwrRp\ng/jJT35Pa2tF4DHbt1fQ0TGC0swXi9OWMtqYyBP6RSoFr7zS+WParLfqp6JCC7ggysv1cnFxlsWr\nrITPfz58f0MDfOpT4euTQqdC2mef8E6Jmm6n03qR7Ow/rFL6fe+GW7ECLrvMvPTcySfrVPY4/dCX\nScI2VahtZ/Q5ePRmuK2Nb2/cOLMpeMwYO/+gybdQVhZtcvZMw975Z8zIzZEdZjr2h/Xnf05duhq2\nCGySL33pcvnBD34g3/veGxJW7jooO9qc4RxQaTToS02dal+gSSmRsWPNx6fT4U7VoCii5csjwqZC\nHM2e08hfCCqf5B2b8EAbp1MxR4tkgfM5hJPEYvGFJpdZaBLfy2T/9tf42Xdf87K1/ftrk3F9vR4A\n7r13cHtMJpu2tmgzUVUVXHtt5/lBm6ttbPJReN/7lFPgySe7r6Hc0aH9BrYD8JKSZo4+egVf+cog\nrr56JLuV/wgWLKB59a+5t+MaoPvsIXum5TfZ72hLqpWy5gau4i4UdNYTCvIfeF/q0UftCzRVV8N+\n+8HKleEmnuZmXcDqgQfgrLO6jrizbYbTpunOM/1I3hdPp6Pt9YGdErDIdRA2xca85yaKyebbUySh\nYQq17cwZ0nGJGyPvH8ht2tT5eto0u9XUPv7YPJCsqjK3wwvbPPTQ8OjIqMVs+vePLqOR1AzCi1ry\nz+amTxcxrx+gZwqVla1SW9vRta1ZP9jyyqOkVn0sNVVtVv/LLm2Z3SJN/QfF/1K2U6youkX+zV/U\nKddFM6qqcrshc5lu20QrRSXxmGZNRQgulLU7xVquOhe8ENJx40QuukiHTtp8r2zB6YV9ekLYJIyV\n0gXVPObMsbdM+NuxfLlevTDqM0p1/15e7aPs8tLZ+OXE6aebrxMVguuPaNy2bZs888wzcv3118uo\nUbcIfGI87/TpAXIq5I/YSFrmll8gM09/SQv8OP/HoNGBKWZXKW1a8n+mslI/hmn25cvtSl57P7ht\nQkn2Nn16jC+eJzaJRVG5EF48ch/BKYcA+mqCmZ/Gxuiia2HfK4nVz7IHdZ6SOvZYkeOOCx+MxskZ\nMCWYZa+BYIPpd6+u1u2fNCk8dL2yslVOPfVxOeKII6SsrEwAKSsrk8MOGyvpdGOo7F22LIcGQXhJ\n1Sj8I4Zp00Qeeij6D5892vamkWGj79mzzTH+/vObqgna/GF7Aht/Ra4+jWJZbjELpxwC6GsJZtlE\nlWGI+l5z5ybjrA27H2z6d+5c8+CztFQrGZPsnDEjXr/le/9DvZSUVMmXvvQluf766+Xpp5+WrVu3\n7vhN/IPvigrdx6GKIaqjshvnt/mZBEyYXTGsI+Oc27Yzs39wUzXBuH+uMPwC2Et/jyuMbWyy+ZT8\nLTL7tVMOAfTlmUOuo37/97rllvifD9qyi1N62PSvTdLruHHRJt7I+z5r1LZ8WXPgvfrCCy2ydOlS\n+f73vy8HHzxVYJPoaKI2KSnZJqlUg/z4xy/Ili1bIi/VpfKoSejapn9XVmpNEyVgTH+Ofv30iCL7\nHHPm5C68bH7EqGqCfuWVq/AMcy7lcj4bf4WtT6PI7ddFpRyAE4E1wJvA9QH7U8Djmf1/BkbYnHdX\nWs/BVNXTdiA2bZr95+KWtRex69+omYNXcy3qGKMiDwl/bLzldvn5ww3yzW/+Q/71XxfI2LEnSmVl\npQCilJKDDjpIvv3t78rVV78kN93UmJslwGbEGHeloag/a5RW9srX+k1Hph8qakZhoxxsqgnmE69t\nO1rqjRu7yEehRaMcgFJgHVCHjtV7Bdgv65hpwIOZ52cBj9uc21Y5+AeRM2YU7WwvlDh+QP+W7R+I\nM3OoqjKboMJG7501e/Rx2XXFouRi//7apxD1fUNNgAah0QGyCWQ0uoz1F77wBbniiitkwYIFUl9f\nn/8PFWfEGBXnb9qyBUxce6lJeHk/vOkGiZr5ZP/xCpG0Yzv76g1hXOT262JSDocDz/he3wDckHXM\nM8DhmedlwEcktNhP0MDFq5hZrOs5+MknGjDbcTt3bnhUU7aAfvZZcwE76B6kkT3LD7O/h0Ur1dR0\nyhTT4NR0v7c/+qi0RQjdlupq+fDttxP5fUSkU/hNmmS3uIPH9dfH/2GDBIxJUPqr+3nkYl/0KzeT\nhs8lYiAXbP02vSGM3czBWjlMAh72vT4PuC/rmNeAob7X60hgmdAiN/3taGMS5mlvMzlEo0btXojo\n4sWd0Yyma02a1PV7xOnrqPLUTU3mxci8Y9vb2+Xll1+W/3PHHXLnQQfJc6Wl0h7VSUneoLZJFUFC\nasoU+x/W1P6oHzY7HDWXGYtNpdGenIIX88yhyO3XO61yAKYAK4GVw4cPN3ZClAKfNq13o8xs7q+o\nAVJJia5sUFkZbsoJuybogaU/b2DxYnt54VcOiQ6WMhpz+dT/lNqqFqmp6fD1T4fMnfuG3HvvvXLq\nqafK7rvvLqNB6kG2KiXtaPNRbEGdC3GmdUGdEJWAYRL2QUvbRa0oZPI15NJnvVnjxdZv01vCuLeV\np4FiUg69ZlaKEqxeZm5v/G62I+0oi8FDD4Wfp3//4Mi+sHt66dJ48sJvrUjMzJp1UzVUD5RH0xfJ\nmUc+JWPG3CN77DFEQPsM6urqZOr550tz3JFwUqPJONO6ICFlWjAm+4e2ETBRa5Harq5WyD5LkiSj\nlQpBka5HXEzKoQxYD4z0OaT/JeuYb2U5pH9lc+4o5RDXJNOTgwxTzkFVVed9GDVDjUpC9ZJkbcox\nxJGxNTXxAmas5Epjo3SEfNl6kM8NGyYXXHCBzJkzR956663oC5t+6Fxi/LOxtXuHlYOYPTv6s16+\ngI2AidLQUTHChbw5CpUQlr3k5+zZRSeMi42iUQ66LZwEvJExF92UeW8GMCHzPA38OhPKuhyoszlv\nlHKIGzHYk4OjKJ+gv0yFaYZqK5+i7vG5c+1WcisrC65plKuZtaOjQ9544w2ZNWuW/Mdhh8nWkC/T\nXl0tHf/1X91PENUB/pXLkojxz+40G8UUVg6iqSn683H+lDZ21FxmDvnWDipiE8uuSFEph0JtuUQr\nRQ3SeiqwISrnwG/PFwmfocYdOIfJGpvQ9fLy4HWNPWxkQEdHh6xbt04efvhhOeecc2SvvfYSz0z0\nw5qacGdy2I+TdIy/V6LZZpRrM/qIEu7Ll4vstlv45+OM2KM0tG3xvGzFkE/toL4QFbKLsUsoh5Ej\nD7GapXr3etQaBBUVyc0couRL1MwhlbKTU3FnR96Su9nnnDs3vBqrtxlLQmQa2njL7TJ32gsy89bt\nO8791ltvyezZs+WCCy6Q4cOH71AGe+65p5x55pnywAMPyOrVq/XMIK5tKu6UJUqZxE2EsXEERwnA\nxkZ9Xf8C17mOrj0N7f2Y2eFrcctsVFXpP2uuZpoiD+vcFdkllENJySGx7qMoAVhVlcxAxmYEHWXG\nSaXC5ZSXzOfdr9nXM9VFC6vIELWgzbPP2n/h9upqaa6qkhmnnCIjR47coQwGDBggZ5xxhtx3333y\n+uuvS0dHR9fz5GqbimO2iLLLhzl0TddPSrgn5cDMzkTMbkvQdbL70Itl9iq15qqsijwhbFdkl1AO\ncEisQZpJ9hiraMbAdhbd1BSdBRy1P6wSgamkTdh96lcQYYPOsC/cHjJybgS596CD5L677pJVq1ZJ\ne3t7dAd6I3FPw5WWanvWxRdH17OxEaymkWw6Ha5ZbUa5YRVOezJeOh8zjtf+6dPDoxPimoLczKHo\n2CWVg81/LWjWXVmp74ck7mFTReOgqgdhgj8qOznqfg0aTJtKYtRUNMvcaS9I46amTvk2u0WaZv+y\nW8e899578thjj8mll14qVw8eLJ+ENKwD4pefXr48XJhUVubvyDSNEEza2Iv2sVFQ3tQuqOBdoZ2w\nSQjjJAV6kSeE7Yrsksohapbq3bu33KIdwp5CiBtIEeYHiKqBlN0+UxZwnK2mpkPmTnuhW4OCVi4L\nneHTJjMrvq+1x9SpncItlZIOkNbycmmoqJAzM2aiNMhF6bT8vz32iE46sxUEPVVMLcwMNWOGXfRQ\n0B/ENvKh0AIxCTNO0qYgF61UVOySysEb1IStZ579/+zfP3xFsrCSD55pOTs60is5ESVXsgddYSN8\nm7DSboI94sYzDgjZInM5WwQ94g8T+NtKS+XRqVOltV8/6UgiTCq7gTYVBpMwRwSZoeJ49/1/kHwz\npZOk2GYOHkWaELYrsksqB7/d3FYJ2N7DnhCPcw7bQWPw2sQx2uoT7KaLGWf41EsTFit8gV31vuzN\nZsRpE09re65csZ0B+P8gceKJ8217PuFrtrMWZwraqUlKOZRRxJSU6H9sdTWUlcHChXDKKbB5c+cx\n27bldu6GBli/Xj/ftAnGjoXGxtzbWl4OTz4J6XT3fZWVMHly5+t586Cmxr7tZbQxkSe6vtnWBk88\n0eXE6TQsWtjM+KOaaaOEBqqppoEy2ljECaRpsbvg9u12x/mpqoKNG2HmTBg1CiZO7N4Zo0bp95qb\nzeeqroa6unjXb2qCBQtg3Tp9na9+FZ56qvO1154xY+Ddd/WxP/sZLFkSfD7/H2TdOv3ahlza7rFi\nBYwfr3/bhgZ9rm99CxYt0u2GzI+8qPtxZWX6/aA/YDZxzpHdr0G/q2PnJAkNU6ht5MhDusxSc6mk\nEDUwzKfsvn8rK7M3s5qtG+1SwyeiaJMatkgt9bKc0cYR6tatW+Xpp5+W6667Tm6uq5P3SctczpaZ\n3CRzOdt+xpDP5k3jTKavQhVTy54NeOGZXrhmWHtszSv51liyIZeyt/macaLO4XwJfRJ2BbNSdoZ0\n3FISUfdwPkUs85UNy+e8LrXqY58i+ERqqZelHGkl2FsrK+WxU0+Vww8/XMrKygSQsrIyeWLQIDsH\nsmmzWWDe26qrzfGz2Z0xZ475fErpY2zw6oLbrpSU3R5b84ptpnQ+grPYQkJd5nOfJSnlUNRmpWxG\njdKz31xNSUp1nT3/9rfRFo64tDW28MR5/8Pk05vCp+BNTYy58kjelWYWcDrrqaOO9UzkiYzp50+R\n19ne1MTTCxdSPmYM1157LccccwxHHHQQ1Z/5TH5foKYGSkuhxWCCqq7WYuK88/Sxv/hF8I/S1KSP\nOf103RcicOWV5ut7x3z962bzhWeCaW62/xGzTXG25hURuOoquP12/bqlpfO4q67Sf6y6uvxMLibT\nld/E1VMsWKD7JIgAk6Zj56NPKYeJE7UJNoiaGn2vtrWFK49UCn7yEzjrLHj1VbjssuSVQ8P2MtbP\nfxmevhemTYOrr9Y7/PbazI1XSTOT+aXVeSXrdSXwSHU16o034L77tE163jztqMmFdLpTWEKwwPQE\n4dCh+pi334ZVq8I7vKUF5s+Hp5/WP9xVV4ULHD9RwqepSbfP73yyoaFB+xkg2Aexfn13Ie/3AzQ3\n6z9RKgXf/S5ce21y9nfTyCcfP0auFJuycvQ8+Uw7gDuB1cAqYAFQG3LcW8CrwF+IMeUJKrwXtZ75\ntGnhYaL+UNj8zEkdmS3gGkGRRfmWWo3avIUdoopLhW1jx+qO89fYCbNHZ/8AcRa/9kpPRB0XFfGT\nr/PJ1gTUk6aVYosgKjYzl8OaODLWtOX3YRgPlGWe/xD4Ychxb2GxLGj2FqQcopaftMnvyVW2VFWJ\n3HijyJTRKyRNY/B9HBUyWlsrrQ8/LK0R4aKmXIRQwZuLoAwrxhQkOPPVqt6ydDbC2yR8klKuUUK3\npwVkMTmAi01ZOaxJSjnkZVYSkUW+ly+ilwwtGEGRfs8+C//yL52Rfjaz86jIxIoKbTkQ6W6GHjMG\n+Jc3uOSSqxnftIA2ymKFjDZu3cqV06Zxx/bt7GH4rsqqR3zkah9rbtZf1MPruPHjdWiq32xiskOD\n7jSTr6Klxc4MU1amTTthRDmfyst1OysrtQnK//2y22MyX/W0aSXKxNWTJBEy6+jbJKFhtLLiSeDc\nkH1/B14GXgKm2J5zx8yhsVEaZz8mtemQ0bplYIl3nGlAmErp2Ygxyi9zkcYcQkbbQBYeeqgsvv12\naTfNHmxH2blukcWYAkbGUdMybzpnavfUqebZh02FxKj6SbNndxbFGzvW3A9hC/WIONOKiMt87oPQ\nU2Yl4FngtYDtVN8xN6F9DoHrQgN7ZR73RC8jepThelOAlcDK4cOH75hqz019Q2r4JOQ+7Vp7aPmy\nZuPs3KhA0o26GF3UTeAzAcQyAfmFyqZN4cK5f//8TDhKiRx7bNelFbOXWTQWYwqw+9sIy6iCUrW1\nemHsfKqjZvW/0QQTterStGnh13CmFUcfpMeUQ+QJ4ELgf4Eqy+O/B1xjc+whBx204+acwc2iaA+W\nYwG1hxqXroiR39ORyTPYJMsZE2nr7ejokFdffVV+etddcs/o0XJbZaXcBLIJZKtS0m5SFtlCxSTk\n4ixzl4uQjTsythWWpjIZNTV6dpFE4TebUW1UrRL/eq1BFJMfwOGwICnlkJfPQSl1IvBd4GgRCSw+\noZSqBkpEZGvm+Xj0+tLRbN68o5TDKNZRTQPb6NftsGoaqNu+GpAddujKCcczOdtm7isFMGbUKN5d\nP5EFvy1h/aU/oq7lr515Bp4pO2N3l1SK1atX89xzz/Hcc8/xxz/+kQ8//BCAESNGcMxZZzF27Fga\nDj+coStWaHuxCNxzT7S9NsrO/O67cNd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6Uyl1ve/9kUqpP2fef1wpVVGgdu6hlFqslFqb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f98663e7d30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Vw+JGSXmf3/usFRV6xmQjp0yDzChBnO3nhG7Za69FUlp6rkCllJSUyGGHHSZX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\nLxaB22+PttdG2ZnfeQduvRVuuslsz88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"text/plain": [
"<matplotlib.figure.Figure at 0x7f988a272748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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/XfrBWolB/lqzkiWvRDWTBCWFXXst3H23d1t+9vhUCq6/3t/B3dzsv2+xiZ8g\nKC4/kYBf/QrOPdfcf+F+/y0tug/OngttbXo/4yDT17p1urheFN+DiZUkldJmJxGvs0Ii8Tx77fUH\n2tp+T1FRCzU1NVx77bWMGjWKY489lqqqKmbN2M4bixo8TT+ZlLRvZ9zKe3mm/ngqEyNp8Ehe22Eu\nStPJaezlO7jxxs4fRKadccMGePHFzskmImYJKFEjCaIkt/j9KEA7etxfipIStsG2ro1kQRwaJl9H\ntiuHXLZltMRLtmYSLxPHxInRZr/O5CzbfYVNVg75quPk7E0Q1vfMFVM2ezSbmLz0+/Sb9bcLtElp\naUoqK5tlwYKtnm2kpj8p1WzyaaNNFG1SVZqSajbJkvLjRJSSVOWeUq0+9+63K9TU6Kis7JiZx50I\nE+cXIaiukvOjCNhSbwS0SgzyN/cG9B4NC4F30BVZf+RxzShgC/C39DHFpO1slENPJT8VGoWiIOMq\nBtfUFJz7UFnZ+fcXZEqKU2jGWVU1m75nyh3TjWpMfhvbt2+X//3f/5U777xThg6dLjrPIIdxq6uT\nJYzsYvrpS73UcYtMLZqyI+nM3aA2F22SKr7wNhdlDkjYgIVtAJ3NjyWuL4KpAAv4oOPyOcRhVmoF\nrhORN5RSfYDXlVLzRSSzXNZiETk9hvv5kkuM+M5EIexl4BBX7aA5c6AoYFNbkc5lMUxCYjMJKqDm\nR9QIJZOQ2Sh9zzR9BdcZgt1202Y2pbpaMlpbW3n99dd59dVXWbhwIa+99hqNafvUfvvdRGnpdlpa\nwj8w35DbYcMYWfV3NjT4mH58QmFHsowNlQcwZ+SdrH31n76hppSXw/nn670//WhogMmTc09IySSO\nULUoAizAXltETNs/x6Fh3AfwLHByxnOjgOejthV15VBIpZp7ikLZy8CrX7lkK4eZSyZOjHa91+Su\ntta/f34rsaCVUUWF3m/AwXRSGKXvXt9rJwDHSbQtLdX/19V1fl+tra2ybNkyueeee+S0006TPn36\nCCCAHHTQQTJp0iT5wx/+IJ988kmkSCjfiXK24VROo6NHB19z881m+4sOGZJF50MIcxrHHarWS1YO\nO1BKDQYOA/7qcfoopdSbwAbgehF5O857Q2GVau4p4sjSzQe5ZisHTcyqqnRouOn1XgQVUAtbiTkr\no+bmrtWUhwzR5w8+2HxSOGyYuVPZr5Cgu3abiM4NOf74dlauXL5jt7NFixaxZcsWAIYPH8748eM5\n4YQTGDVAEKRcAAAgAElEQVRqFPvss0+XNjNXgCLefdoxUTapAV5Soj3vYVRWwoEHwpIl/l+C9HsJ\n5Z//DL5PNjkMIjpi4t//XS8/3bS3w4oV4cv2KAIsYIkoWsHnThwaRvS3pAp4HRjncW43oCr9/2nA\n6oB2JgDLgGWDBg2KoLrtykEkPzZwN/nwZeQjNDzqJNVvVWW6EgsrFDh9uvl3M5UK9q+AXgl4rTqC\n/RVtAkcLIF/5ylfkyksukdcmTZIt119v/GE6n9Xtt4cURpy/uLNHvbKyawXAiRPNowWqq/UyLOjD\nmDLFbMkVdM+Kiuhfar9YZpMvmJuoAsxnKVoF70gcMj2WRqAUeBm41vD6D4B+YddFNSvFvRNWbySf\nCjIfzv58hYZnXu8I1LIybXmorDRrw3Q8w66rrY2mtIMis0C3l/l9bm9vl7vvXidlZc0BcnG7vPvu\nP8MH00Bj+zYx7Y3ghAhnmzQT7Z3Zr6B+m3jjw5RRpn0ybByivI+wH182AszDXkuhbBOKzmv/L+CB\ngGu+BKj0/0cA/3QeBx07c7RSvqKJ8qUg8+HLyKbNKL4LJ2t59OgOu7vznaio0HIgrA3TlVjYdbW1\n0ZS2iVJqb2+XlStXysMPPyznnHOO7L333gK3SlBUUVmZyMzpzcHZhIsXG/+Iunwem0LCyqDjSx8k\nyIuLRS67LNgBlPklCFsulpfrL4Nf1dPMkDcTYRIlPMxk2R6DACsk5XCsNnOxnI5Q1dOAK4Ar0tdc\nhQ5zfRP4C3C0Sdu55jl0V6nmqORbgeWj/bg3r3faDMpOnj49+ha7XmPg93s1UWpBJTHcQj3suoCw\n9MhmsaqyBjn/O+dL//79Jf3bkwEDBsj5558vl1++IGCv5LSMGr3Af1AqK3PbSGPmzPDZeTIZbgIq\nK8vuS5v55U8mdVuJRMdy0eQLYTpzMY0eiLJsz1GAFYxyyOexM5bP6K5oorgVZNhvIJvfclhSWzLp\nreBMLCLZrPQzFc7ixcFld5zPK6w8j/s6Y6XdpDep6ZtolIrixk7x/QupkQaQ+cOGySvf/76sXrFC\n2tvbRSTcX1GVaJaZRRcED4yfcDcRcCYb2SQSejZhMuPOFNgmy+3MZLGw2klRVwSm9sR8/bhDsMqh\nl9JbneZx/wbCktqC2vf7rTs7jgU5fzMVkLPSzxTcYZPMvn21jLrttuD3kam0pk/vKCg3fXrXsfrH\nP/4hz02ZIg1lZbJVKWkgKU9wnkzlFs8EMS/BFlgDiU3BWcXFxWYDlusXZcqUaBo82+Vw2BZ1tbXe\ny9KgXYfc4xBmynI74rsJqxx6KfmOJoqb+no9yTv+eLPAElMFZypDvH7PpaXB553aaaZ9jZpNXVam\nzddh/U8kOrbH9JNtzz//sTzxxBNy6aWXytChQyUJUh91UBwt5BJwHeU32kXRLlV84Z9V7D5KSvxt\n8rk6Vd1HXZ3ZKkOpYEUSNhuJ+oNzPqig3dgyx8HrwzV1auWBuJSDLbzXzcSRSNld/Pa38L3v6V+E\nm+Li6MXqMkPeV62KvjEN6P3XcznvxskReOaZaNnUmWHsQdetXx+c+Hr66SXAZVRXl3P88cfz62OO\noe8f/xi8FZ4XGUks3/wmfPbSMuacOo217fsytPVd76ziTEpK/AfRL6nCjTsl3qsaocP99+uCcWFU\nVsJnn2WfvBPlBxdURdFN5jhEKaLXi7DKoZsJKm9g8tvrLjZt8lYMoBVDaal37pKXgvNKImtv17+d\nzKSxMPzuG5Xq6o7yGNnsoGZCZSXsuecWbrrp7zQ2jgC6CotEooopU1Zy0037UVxcrHcwijoo0FUr\np1KUjz2Z8U0hgs6NUv6KoW9f83onjrC86CKYPdv7mtZWqK/XyWtBmYolJdCvX/bZrVF+cE8/HTz2\nyWSH8ssch+7ek7YbiKcGh8UY57tVXa1/F0rpv25h1R2kUjBrlpZFs2Z1lQm33eatGByUzyZemb83\n92SsoUG32dCgJ8bZyMA4ttctLYUTToDVq/X7diaXcdPUtIVJk/bhV796gZaWriWnAbZvL6O9fbBW\nDJB9ZxIJePPNjg8zanGpsjIt4LwoLdXbxUUpzFVeDl/9qv/5xkbYay///UmhQyENH+4/JmHL7WRS\nb5Kd+YVVSj/v/OCWLoUrr3S2nvPm9NN1Knt3FyjrKeKwTeXr2Bl9Dg49GW5r4tsLK2MzcmTOxSOl\npMTMnO5uv64uO0e2n+nYHdafe5u6dDVsEdgk3/jGVfKzn/1M7rjjXd9y113M+LnUH3K/qYkTzQs0\nKSUyalTw9cmkv1PVK4poyZKQsCkfR7PjNHIXgsolecckPNDE6VTI0SIZYB3S/hRKueq4ieN9mYbS\nTpoU/Fs56aROm0/59icsBDbIuQxavrjbz1V2+r1vr7yvigpzBQYiRUVNcsIJf5YpU1bKli3NOz6w\n1G13SnWFd9ayp2zzEphR31TQFndegs8ktNQJB3N32Gum0bev3kUtbNC99inw+yLlI1rJEfgm0RG9\nqMSCVQ4+9JYM6ahEeV+ZSmTTpo7HkyaZbYv5+efBQr2iIrgfTtjmEUf4R0eGbWbTt294GY04lIM7\naskto6ZMESFwW0u9Uigvb5Hq6vbOfc34wJaUHyfV6nOpqmg1+15GjdX3elOmS6ywukXuw13UKdtN\nMyoqsvtBZrPcNolWCpvBBK2aChCrHDwo1HLV2eCEkI4eLXLppTp00uR9ZQpOZ2buCOEgYayUnqU7\nzJhhbplw92PJkvCJo3O/zPfl1D7KLC+diVtOnH128H3CQnDdEY0NDQ3y8ssvy+TJk2XYsNsEvghs\nd8oUDznl80VsIikzSy+WqWe/LjOnN0f7PnrNDoJidpXSpiX3a8rL9V8/zb5kifkqpbraPKEk85gy\nJcIbz5FcVw7OaqkXYZWDB701wcxNU1N40TW/9xXH7meZkzpHSZ14ojYl+U1Go+QMBCWYZe6BYELQ\n515ZqftfW+sful5e3iJnnvm0HH300VJSUiKAlJSUyJFHjpJksslX9i5enEWHwL+kahjuGcOkSSKP\nPhr+hc+cbTvLSL/Z9/TpwTH+7vaDqgmafGG7AxN/RbY+jQK1X1vl4EFvSzDLJKwMQ9j7mjkzHmet\n3+/BZHxnzgyefBYXayUTJDvr6qKNW66/f6iXoqIK+cY3viGTJ0+Wl156SbZu3brjM3FPvsvK9Bj7\nKoawgcrsnNvmFyRg/OyKfgMZpW3Twcz8wIOqCUb9cvnhFsBO+ntUYWxik82l5G+B2a+tcvCgN68c\nsp31u9/XbbdFf73XkVmc0sFkfE2SXkePDjfxhv7uM2ZtSxZv8/ytvvZasyxatEh+8pOfyNe/PlFg\nk+hoolYpKmqQRKJRfvGL12TLli2ht+pUeTRI6Jqmf5eXa00TJmCCvhx9+ugZRWYbM2ZkL7xMPsSw\naoJu5ZWt8PRzLmXTnom/wtSnUeD264JSDsCpwCrgPWCyx/kE8HT6/F+BwSbt7kr7OQRV9TSdiIVF\nGLmPqGXtRczGN2zl4NRcC7smUJH7hD823fZT+e1jjXL55f+Qf/3XOTJq1KlSXl4ugCil5LDDDpMf\n/vBGue661+WWW5qyswSYzBhzDanK/LKGaWWnfK3bdBT0QYWtKEyUg0k1wVzitU1nSz3xwy7wWWjB\nKAegGFgDDAXK0GW5D8q4ZhLwSPr/c4GnTdo2VQ7uSWRdXcGu9nyJ4gd0H5n+gSgrh4qKYBOU3+y9\no2aPvi6zrliYXOzbV/sUwt6vrwkwQGi0g2wCqUGXsT700EPl6quvljlz5kh9fX3uH1SUGWNYnH/Q\nkSlgotpLg4SX88EH/UDCVj6ZX7x8JO2Yrr56QhgXuP26kJTDUcDLrsc3AzdnXPMycFT6/xLgM2La\n7McvzLqurnD3c3CTSzRgpuN25kz/qKZMAf3KK+E5BplBGpmrfD/7u1+0UlVVh0wJmpwG/d7bnnhC\nWkOEbnNlpXy6bl0sn4+IdAi/2lqzzR0cJk+O/sF6CZggQemu7ueQjX3RrdyCNHw2EQPZYOq36Qlh\nbFcOxsqhFnjM9fhC4MGMa94CBroeryGGbUIL3PS3o49xmKedI8ghGjZrd0JE58/viGYMuldtbef3\nEWWsw8pTp1LBm5E517a1tckbb7wh/3HXXXLPYYfJwuJiaQsbpDh/oKZJFV5CasIE8w82qP9hH2xm\nOGo2KxaTSqPduQQv5JVDgduvd1rlAEwAlgHLBg0aFDgIYQp80qSejTIz+X2FTZCKinRlg/Jyf1OO\n3z1BTyzdeQPz55vLC7dyiHWylNaYSyb+p1RXNEtVVbtrfNpl5sx35YEHHpAzzzxTdt99d6lBl7He\nqpS0oc1HkQV1NkRZ1nkNQlgCRpCw99raLmxHoSBfQzZj1pM1Xkz9Nj0ljHtaeQZQSMqhx8xKYYLV\nycztic/NdKYdZjF49FH/dvr29Y7s8/tNL1oUTV64rRWxmVkzflSNlf3kieSlcs4xL8rIkffLHnsM\nENA+g6FDh8rEiy6SbVFnwnHNJqMs67yEVNCGMZkftImACduL1HR3tXyOWZzEGa2UDwp0P+JCUg4l\nwFpgiMsh/S8Z1/wgwyH9e5O2w5RDVJNMd04ygnIOKio6fodhK9SwJFQnSTbstxJ157WqqmgBM0Zy\npalJ2n3ebD3I/vvtJxdffLHMmDFDPvjgg/AbB33Q2cT4Z2Jq9/YrBzF9evhrnXwBEwETpqHDYoTz\n+ePIV0JYZhmR6dMLThgXGgWjHHRfOA14N20uuiX9XB0wNv1/EvhDOpR1CTDUpN0w5RA1YrA7J0dh\nPkF3mYqgFaqpfAr7jc+cabaTW0mJd02jbM2s7e3t8u6778q0adPkl0ceKVt93kxbZaW0/+53XRsI\nGwD3zmVxxPhnDpqJYvIrB5FKhb8+ypfSxI6azcoh19pBBWxi2RUpKOWQryObaKWwSVp3BTaE5Ry4\n7fki/ivUqBNnP1ljErpeWuq9r7GDiQxob2+XNWvWyGOPPSbnn3++7LvvvuKYie6uqvJ3Jvt9OHHH\n+Dslmk1muSazjzDhvmSJyG67+b8+yow9TEObFs/LVAy51A7qDVEhuxi7hHIYMuRwo1Wq81sP24Og\nrCy+lUOYfAlbOSQSZnIq6urI2XI3s82ZM8MrOAeWhEh3tOm2n8rMSa/J1Nu372j7gw8+kOnTp8vF\nF18sgwYN2qEM9t57bznnnHPk4YcflpUrV+qVQVTbVNQlS5gyiZoIY+IIDhOATU36vu4NrrOdXTsa\n2vkwM8PXopbZqKjQX9ZszTQFHta5K7JLKIeiosMjl24PEoAVFfFMZExm0GFmnETCX045yXzO7zXz\nfkF10fwqMoRtaPPKK+ZvuK2yUrZVVEjdGWfIkCFDdiiDPffcU77zne/Igw8+KG+//ba0t7d3bidb\n21QUs0WYXd7PoRt0/7iEe1wOzMxMxMy+eN0ncwydWGanUmu2yqrAE8J2RXYJ5QCHR5qkBcmewCqa\nETBdRadS4VnAYef9KhEElbTx+526FYTfpNPvDbf5zJybQB447DB58N57Zfny5dLW1hY+gM5M3NFw\nxcXanvX974fXszERrEEz2WTSX7OazHL9Kpx2Z7x0LmYcp/9TpvhHJ0Q1BdmVQ8GxSyoHk++a16q7\nvFz/HuL4DQdVNPaqeuAn+MOyk8N+r16T6aCSGFVl22TmpNekaVOqQ75Nb5bU9Ce7DMxHH30kTz31\nlFxxxRVyXf/+8oVPx9ohevnpJUv8hUl5ee6OzKAZQpA2dqJ9TBSUs7TzKniXbydsHMI4ToFe4Alh\nuyK7pHIIW6U6v11n+0pHIUQNpPDzA4TVQMrsX1AWcJTDL6HPa+cy3xU+rTK17Cdae0yc2CHcEglp\nB2kpLZXGsjI5J20mSoJcmkzK/7fHHuFJZ6aCoLuKqfmZoerqzKKHvL4gppEP+RaIcZhx4jYF2Wil\ngmKXVA7OpMZvP/PM72fQVrZ+JR8c03JmdKRTciJMrmROuvxm+CZhpe6jrCz8dxc4IWSLzOQ8EfSM\n30/gNxQXyxMTJ0pLnz7SHkeYVGYHTSoMxmGO8DJDRfHuu78guWZKx0mhrRwcCjQhbFdkl1QObru5\nqRIw/Q07QjxKG6aTRu+9ieO/V+AKn3pJYbDDF5hV78s8TGacJvG0pm1li+kKwP0FiRJPnGvfcwlf\nM121WFPQTk1cyqGEAqaoSH9jKyuhpATmzoUzzoDNmzuuaWjIru3GRli7Vv+/aROMGgVNTdn3tbQU\nnnsOksmu58rLYfz4jsezZkFVVfZ9B2hthWee6dxuMgnz5m5jzHHbaKWIRiqppJESWpnHKSRpNmt8\n+/boHaqogI0bYepUGDYMxo3rOhjDhunntm0LbquyEoYOjXb/VArmzIE1a/R9vvUtePHFjsdOf0aO\nhA0b9LW/+Q0sWODdnvsLsmaNfmxCNn13WLoUxozRH25jo27rBz+AefN0vyH9Ic/rel1JiX7e6wuY\nSZQ2MsfV63O17JzEoWHydQwZcninVWo2lRTCJoa5lN13HyUl5mbWXPeCyZygbt26VV566SW56aab\n5NahQ+VjkjKT82Qqt8hMzjNfMeTaoTCbc76KqWWuBpzwTCdc068/puaVXGssmZBN2dtczThhbVhf\nQq+EXcGslJkhHbWURNhvOJcilrnKhiUz3pZq9blU8YUoWqWMbQLtxvcpL2+RM898So466igpKSkR\nQEpKSuSZffYxcyAHHSYbzDtHZWVw/GzmYMyYEdyeUvoaE5y64KY7JWX2x9S8YpopnYvgLLSQUJv5\n3GuJSzkU9fDCJRLDhunVb7Yopc051dV69fzss+EWjqhsb2rlmQvnaNuRX+OpFCOvOYYN0p9pTKSO\n27mM/6QSczuTpL6g/NlLKG1r44YbbmDevHls3riRs7duReXyBqqqws0GlZXajDRxIlx8sf+HkkrB\nhRd2jEUqBddcE9y2iL4m7INZuhQGDIArrzT/EB1bnINjXqmu1u878wvijIMIXHutfpxMdr6urk6b\n0qZN02Y1x/wTlSDTldvE1V3MmaPHy4vMcbTslBS0zyGTceO0CdaLqiptMm1t9bflJxLwq1/BuefC\nihXR5IopTduLeXf2m/DSfTBpElx3nT7httemf3jlbGM8TwKwjQSzGO/ZpqKdChpo2uFDaONlTmFk\nVQnq3XfhwQe1UJo1SztqssERfPPm6cde9uhrr9WCceBAfc26dbB8uf+ANzfD7Nnw0kv6g7v2Wn+B\n48bLoeImldL9czufTGhs1H4G8PZBrF2r/QVuu7rbD7Btm/4SJRJw441www3x2d+dmY/XWObix8iW\nQlNWlu4nl2UHcA+wElgOzAGqfa77AFgB/I0ISx6vwnth+5lPmuQfJuoOhY3bnNRxtMskftXVVGJQ\nanUJNVJNvVSxRRStUsUWqaZeFnFMsA/B2dghrLiU3zFqlB44d40dP3t05gcQZfNrp/RE2HVhET+5\nOp9MTUDdaVoptAiiQjNzWYyJImODjtxeDGOAkvT/dwN3+1z3AQbbgmYeXsohbPtJk/yebGVLRYXI\nj38sUlMTdF27TOF2/wuqq6XlscekxSdctCntTK7jFvldFGdyFCHtPvyKMXkJzly1qrMtnYnwDhI+\ncTmfwoRudwvIQnIAF5qyshgTl3LIyawkIvNcD/+C3jI0b3hF+r3yCvzLv3SYek1W52GRiWVl2nIg\n0jXKb+RIfb/LLtPWjUzKaWQ47/q23bR1K9dMmsRd27ezh8d5t6kpEtnax7Zt02/UwRm4MWO0Dd1t\nNgmyQ4MetOaAcNnmZjMzTEmJNu34EfQhg44rbm3VMcSpVOf3l9mfIPNVd5tWwkxc3UkcIbOW3k0c\nGkYrK54DLvA59z7wBvA6MMG0TWflEBaUYhpY4lwXNCFMJPS9gqL8ckk2awWZe8QRMv+nP5W2oGQz\n01l2tkdoMSaPmXHYssxZzgX1e+LE4NWHSYXEsPpJ06d3FMUbNSp4HPw26hGxphURm/ncC6G7zErA\nK8BbHseZrmtuQfscPPeFBvZN/90bvY3ocQH3mwAsA5YNGjRox0o7KLoys/aQVxa1e3UeKNyTTboY\nXciPoLMFoF2q+EKqqZclBNqcOguVTZv8hXPfvrmZcJQSOfHEzlsrZm6zGFiMycPubyIswwpKVVfr\njbFzqY7a9QPwN8GE7bo0aZL/PaxpxdIL6TblENoAXAL8H1BheP0dwPUm1x522OHG8jGz9tCiRVHy\nexzhvkmWMDLU1tve3i4rVqyQe+/9tdTU3C/l5XfKEM6TD0nIVqWkDf/aRV2ESpCQi7LNXTZCNurM\n2FRYBpXJqKrSq4s4Cr+ZzGrDapW492v1opD8ABaLAXEph5x8DkqpU4EbgeNFxLP4hFKqEigSka3p\n/8eg95cOZfNm80oOznWOGXrs2K4mc3cpgJHDhrFh7TjmPFvE2it+ztDmdxjHM7rEhGPKTtvdJZFg\n5cqVLFy4kIULF/LnP/+ZTz/9FIDBgwd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6Qx/B9jNubD/jxfYzPmLpY0E7pC0Wi8XSMxT6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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f986abf9550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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mP8qVV7o4B8fwIKmaJCrK96KL4Ac/CC4rTB+fzcI3vhFu4O7uDl+32MZOEOWX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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f986632fb00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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02Ukk6KwwLdXId/Z8mN93dtJRVkZDQwOXXXYZ48aN4+ijj6auro77527j2WUt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\n1m86Mn1QUSsKG+VgU02wkHh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WcM1xSqnnfMdWpdTp2XNzlVL/8J07pL/6mb2u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\n/wUt7U5iEMO3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f98663419e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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XkkrBKafA17+unz/zjBb2JlIpuPderRi9FZTt9Did1mYre5KJ8XflMxwOR1EQV9AXMrlu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\neyilFimlVmfudw8453il1F99t61KqdMzx2Yrpf7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"text/plain": [
"<matplotlib.figure.Figure at 0x7f9866341a58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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rru5+0yj8v7+9XdfBW3Oho0OvZ2wyfb37rk6uF8f3YGMlSae12Ukk6KgwubKB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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f98661d4b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tr_data = tensor.from_numpy(data)\n",
"tr_label = tensor.from_numpy(label.astype(int))\n",
"# plot the classification results using the current model parameters\n",
"def plot_status(w, b, title='origin'):\n",
" global bd_x, bd_y, data \n",
" pr = np.add(np.dot(data, w), b)\n",
" lbl = pr[:, 0] < pr[:, 1]\n",
" \n",
" plt.figure(figsize=(6,3));\n",
" plt.plot(bd_x, bd_y, 'k', label='truth line') \n",
" plt.plot(data[lbl, 0], data[lbl, 1], 'ro', ms=7)\n",
" plt.plot(data[~lbl, 0], data[~lbl, 1], 'bo', ms=7)\n",
" plt.legend(loc='best')\n",
" plt.title(title)\n",
" plt.xlim(-1, 1);\n",
" plt.ylim(data[:, 1].min()-1, data[:, 1].max()+1)\n",
" \n",
"# sgd\n",
"for i in range(1000):\n",
" act = dense.forward(True, tr_data)\n",
" lvalue = lossfunc.forward(True, act, tr_label)\n",
" dact = lossfunc.backward()\n",
"\n",
" dact /= tr_data.shape[0]\n",
" _, dp = dense.backward(True, dact)\n",
"\n",
" # update the parameters\n",
" opt.apply(i, dp[0], p[0], 'w')\n",
" opt.apply(i, dp[1], p[1], 'b')\n",
"\n",
" if (i%100 == 0):\n",
" print('training loss = %f' % lvalue.l1())\n",
" plot_status(tensor.to_numpy(p[0]), tensor.to_numpy(p[1]),title='epoch %d' % i)\n",
"\n",
"#train(dat, label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The layer class has forward and backward functions for back-propagation.\n",
"* forward() accepts two arguments, the first one indicates the phase (training or evaluation); the second one includes the input tensor(s); It outputs the layer values as a single or a list of tensors.\n",
"* backward() accepts two arguments, the first one is not used currently; the second one includes the gradients of the layer values. It outputs a tuple, where the first field includes the gradient tensor(s) of the input(s), and the second field includes a list of gradients for the parameters.\n",
"\n",
"The optimzier class **apply** function updates the parameter values using the gradients. The first argument is the iteration ID, followed by the gradient tensor and the value tensor. Each parameter tensor has a name associated with it, which is used by Optimizer to keep some internal data (e.g., history gradients) for each parameter.\n",
"\n",
"The loss class computes the loss value given the predictions and the ground truth in **forward()** function. It computes the gradients of the predictions w.r.t the loss function and outputs the gradient tensor(s) by **backward()** function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Observation\n",
"\n",
"We can see that prediction of the data points are getting correct labels with the training going on."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Next [CNN example](./cnn.ipynb)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "py3",
"language": "python",
"name": "py3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}