From 400cc5dcfc1001d60819b1a44dba4a75483d0714 Mon Sep 17 00:00:00 2001
From: Sunny <sunnyharjani@me.com>
Date: Sun, 16 Sep 2018 23:26:56 -0400
Subject: [PATCH 1/2] Indexing fixes

Fixed indexing issues by casting floats to ints.
Also updated sigmoid function call and fixed print statement.
Added .py version.
---
 LogisticPytorch.ipynb | 2121 ++++++++++++++++++++---------------------
 LogisticPytorch.py    |  130 +++
 2 files changed, 1177 insertions(+), 1074 deletions(-)
 create mode 100644 LogisticPytorch.py

diff --git a/LogisticPytorch.ipynb b/LogisticPytorch.ipynb
index 85c5a88..3996019 100644
--- a/LogisticPytorch.ipynb
+++ b/LogisticPytorch.ipynb
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -16,10 +14,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "N = 100\n",
@@ -28,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,25 +33,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# center the first N/2 points at (-2,-2)\n",
-    "X[:N/2,:] = X[:N/2,:] - 2*np.ones((N/2,D))\n",
+    "X[:int(N / 2), :] = X[:int(N / 2), :] - 2 * np.ones((int(N / 2), D))\n",
     "\n",
     "# center the last N/2 points at (2, 2)\n",
-    "X[N/2:,:] = X[N/2:,:] + 2*np.ones((N/2,D))\n",
+    "X[int(N / 2):, :] = X[int(N / 2):, :] + 2 * np.ones((int(N / 2), D))\n",
     "\n",
     "# labels: first N/2 are 0, last N/2 are 1\n",
-    "T = np.array([0]*(N/2) + [1]*(N/2)).reshape(100,1)"
+    "T = np.array([0] * int(N / 2) + [1] * int(N / 2)).reshape(100, 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,10 +59,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Model(torch.nn.Module):\n",
@@ -77,7 +69,7 @@
     "        self.linear = torch.nn.Linear(2, 1) # 2 in and 1 out\n",
     "        \n",
     "    def forward(self, x):\n",
-    "        y_pred = F.sigmoid(self.linear(x))\n",
+    "        y_pred = torch.sigmoid(self.linear(x))\n",
     "        return y_pred\n",
     "    \n",
     "# Our model    \n",
@@ -86,11 +78,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/site-packages/torch/nn/functional.py:52: UserWarning: size_average and reduce args will be deprecated, please use reduction='elementwise_mean' instead.\n",
+      "  warnings.warn(warning.format(ret))\n"
+     ]
+    }
+   ],
    "source": [
     "criterion = torch.nn.BCELoss(size_average=True)\n",
     "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
@@ -98,1025 +97,1027 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
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+      "/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:8: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n",
+      "  \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
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+      "(974, tensor(0.2029))\n",
+      "(975, tensor(0.2029))\n",
+      "(976, tensor(0.2029))\n",
+      "(977, tensor(0.2029))\n",
+      "(978, tensor(0.2029))\n",
+      "(979, tensor(0.2029))\n",
+      "(980, tensor(0.2029))\n",
+      "(981, tensor(0.2028))\n",
+      "(982, tensor(0.2028))\n",
+      "(983, tensor(0.2028))\n",
+      "(984, tensor(0.2028))\n",
+      "(985, tensor(0.2028))\n",
+      "(986, tensor(0.2028))\n",
+      "(987, tensor(0.2028))\n",
+      "(988, tensor(0.2028))\n",
+      "(989, tensor(0.2028))\n",
+      "(990, tensor(0.2028))\n",
+      "(991, tensor(0.2028))\n",
+      "(992, tensor(0.2028))\n",
+      "(993, tensor(0.2027))\n",
+      "(994, tensor(0.2027))\n",
+      "(995, tensor(0.2027))\n",
+      "(996, tensor(0.2027))\n",
+      "(997, tensor(0.2027))\n",
+      "(998, tensor(0.2027))\n",
+      "(999, tensor(0.2027))\n"
      ]
     }
    ],
@@ -1138,7 +1139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1146,25 +1147,11 @@
      "output_type": "stream",
      "text": [
       "data is\n",
-      "\n",
-      " 1.0270  0.9022\n",
-      "[torch.FloatTensor of size 1x2]\n",
-      "\n",
-      "Variable containing:\n",
-      "1.00000e-02 *\n",
-      " -2.6297 -2.6428\n",
-      "[torch.FloatTensor of size 1x2]\n",
-      "\n",
+      "tensor([[0.9169, 0.9204]])\n",
+      "tensor([[-0.0209, -0.0191]])\n",
       "data is\n",
-      "\n",
-      "-0.3595\n",
-      "[torch.FloatTensor of size 1]\n",
-      "\n",
-      "Variable containing:\n",
-      "1.00000e-03 *\n",
-      "  5.8335\n",
-      "[torch.FloatTensor of size 1]\n",
-      "\n"
+      "tensor([-0.2480])\n",
+      "tensor([-0.0018])\n"
      ]
     }
    ],
@@ -1178,7 +1165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1187,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1196,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1205,27 +1192,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final gradient descend: [Parameter containing:\n",
-      " 1.0270  0.9022\n",
-      "[torch.FloatTensor of size 1x2]\n",
-      ", Parameter containing:\n",
-      "-0.3595\n",
-      "[torch.FloatTensor of size 1]\n",
-      "]\n"
+      "('Final gradient descend:', [Parameter containing:\n",
+      "tensor([[0.9169, 0.9204]], requires_grad=True), Parameter containing:\n",
+      "tensor([-0.2480], requires_grad=True)])\n"
      ]
     },
     {
      "data": {
-      "image/png": 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GRyPWQPNltLeY7XImnN0QnIJY2erUvA1YmZVB8zjlkFqOOh9pu1rx++GznzUV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\noKUHFRSLlgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f9ff6d0>"
+       "<matplotlib.figure.Figure at 0x10d13f690>"
       ]
      },
      "metadata": {},
@@ -1235,7 +1217,7 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "print \"Final gradient descend:\", w\n",
+    "print(\"Final gradient descend:\", w)\n",
     "# plot the data and separating line\n",
     "plt.scatter(X[:,0], X[:,1], c=T.reshape(N), s=100, alpha=0.5)\n",
     "x_axis = np.linspace(-6, 6, 100)\n",
@@ -1246,22 +1228,13 @@
     "plt.ylabel('X(2)')\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1273,7 +1246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "version": "2.7.15"
   }
  },
  "nbformat": 4,
diff --git a/LogisticPytorch.py b/LogisticPytorch.py
new file mode 100644
index 0000000..2045a51
--- /dev/null
+++ b/LogisticPytorch.py
@@ -0,0 +1,130 @@
+
+# coding: utf-8
+
+# In[9]:
+
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+
+# In[10]:
+
+
+N = 100
+D = 2
+
+
+# In[11]:
+
+
+X = np.random.randn(N,D)*2
+
+
+# In[12]:
+
+
+# center the first N/2 points at (-2,-2)
+X[:int(N / 2), :] = X[:int(N / 2), :] - 2 * np.ones((int(N / 2), D))
+
+# center the last N/2 points at (2, 2)
+X[int(N / 2):, :] = X[int(N / 2):, :] + 2 * np.ones((int(N / 2), D))
+
+# labels: first N/2 are 0, last N/2 are 1
+T = np.array([0] * int(N / 2) + [1] * int(N / 2)).reshape(100, 1)
+
+
+# In[13]:
+
+
+x_data = Variable(torch.Tensor(X))
+y_data = Variable(torch.Tensor(T))
+
+
+# In[14]:
+
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.linear = torch.nn.Linear(2, 1) # 2 in and 1 out
+        
+    def forward(self, x):
+        y_pred = torch.sigmoid(self.linear(x))
+        return y_pred
+    
+# Our model    
+model = Model()
+
+
+# In[15]:
+
+
+criterion = torch.nn.BCELoss(size_average=True)
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+
+# In[16]:
+
+
+# Training loop
+for epoch in range(1000):
+    # Forward pass: Compute predicted y by passing x to the model
+    y_pred = model(x_data)
+    
+    # Compute and print loss
+    loss = criterion(y_pred, y_data)
+    print(epoch, loss.data[0])
+    
+    # Zero gradients, perform a backward pass, and update the weights.
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+
+# In[17]:
+
+
+w = np.array([])
+for f in model.parameters():
+    print('data is')
+    print(f.data)
+    print(f.grad)
+
+
+# In[18]:
+
+
+w = list(model.parameters())
+
+
+# In[19]:
+
+
+w0 = w[0].data.numpy()
+
+
+# In[20]:
+
+
+w1 = w[1].data.numpy()
+
+
+# In[21]:
+
+
+import matplotlib.pyplot as plt
+
+print("Final gradient descend:", w)
+# plot the data and separating line
+plt.scatter(X[:,0], X[:,1], c=T.reshape(N), s=100, alpha=0.5)
+x_axis = np.linspace(-6, 6, 100)
+y_axis = -(w1[0] + x_axis*w0[0][0]) / w0[0][1]
+line_up, = plt.plot(x_axis, y_axis,'r--', label='gradient descent')
+plt.legend(handles=[line_up])
+plt.xlabel('X(1)')
+plt.ylabel('X(2)')
+plt.show()
+

From d6fdb358e78242b96254bce5f1977f7e7a057216 Mon Sep 17 00:00:00 2001
From: Sunny <sunnyharjani@me.com>
Date: Sun, 16 Sep 2018 23:40:26 -0400
Subject: [PATCH 2/2] Prettied up README

---
 LogisticPytorch.png | Bin 0 -> 58130 bytes
 LogisticPytorch.py  |  54 +-------------------------------------------
 README.md           |   3 ++-
 3 files changed, 3 insertions(+), 54 deletions(-)
 create mode 100644 LogisticPytorch.png

diff --git a/LogisticPytorch.png b/LogisticPytorch.png
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literal 0
HcmV?d00001

diff --git a/LogisticPytorch.py b/LogisticPytorch.py
index 2045a51..4812126 100644
--- a/LogisticPytorch.py
+++ b/LogisticPytorch.py
@@ -1,31 +1,13 @@
-
-# coding: utf-8
-
-# In[9]:
-
-
 import numpy as np
 import torch
 import torch.nn.functional as F
 from torch.autograd import Variable
 
-
-# In[10]:
-
-
 N = 100
 D = 2
 
-
-# In[11]:
-
-
 X = np.random.randn(N,D)*2
 
-
-# In[12]:
-
-
 # center the first N/2 points at (-2,-2)
 X[:int(N / 2), :] = X[:int(N / 2), :] - 2 * np.ones((int(N / 2), D))
 
@@ -35,17 +17,10 @@
 # labels: first N/2 are 0, last N/2 are 1
 T = np.array([0] * int(N / 2) + [1] * int(N / 2)).reshape(100, 1)
 
-
-# In[13]:
-
-
 x_data = Variable(torch.Tensor(X))
 y_data = Variable(torch.Tensor(T))
 
 
-# In[14]:
-
-
 class Model(torch.nn.Module):
     def __init__(self):
         super(Model, self).__init__()
@@ -58,17 +33,9 @@ def forward(self, x):
 # Our model    
 model = Model()
 
-
-# In[15]:
-
-
 criterion = torch.nn.BCELoss(size_average=True)
 optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 
-
-# In[16]:
-
-
 # Training loop
 for epoch in range(1000):
     # Forward pass: Compute predicted y by passing x to the model
@@ -83,38 +50,18 @@ def forward(self, x):
     loss.backward()
     optimizer.step()
 
-
-# In[17]:
-
-
 w = np.array([])
 for f in model.parameters():
     print('data is')
     print(f.data)
     print(f.grad)
 
-
-# In[18]:
-
-
 w = list(model.parameters())
 
-
-# In[19]:
-
-
 w0 = w[0].data.numpy()
 
-
-# In[20]:
-
-
 w1 = w[1].data.numpy()
 
-
-# In[21]:
-
-
 import matplotlib.pyplot as plt
 
 print("Final gradient descend:", w)
@@ -127,4 +74,5 @@ def forward(self, x):
 plt.xlabel('X(1)')
 plt.ylabel('X(2)')
 plt.show()
+#plt.savefig('LogisticPytorch.png')
 
diff --git a/README.md b/README.md
index 3e62064..0bd0188 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,2 @@
-# notebooks
+# Pytorch logistic regression example
+![Result](LogisticPytorch.png)
\ No newline at end of file