diff --git a/Manual-Neural-Network.ipynb b/Manual-Neural-Network.ipynb new file mode 100644 index 0000000..dac1056 --- /dev/null +++ b/Manual-Neural-Network.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class SimpleClass():\n", + " \n", + " def __init__(self,name):\n", + " print(\"Hello\"+ ' ' +name)\n", + " \n", + " def yell(self):\n", + " print(\"YELLING!!!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "s = \"world\"" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello Ed\n" + ] + } + ], + "source": [ + "x = SimpleClass('Ed')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "YELLING!!!\n" + ] + } + ], + "source": [ + "x.yell()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "class ExtendedClass(SimpleClass):\n", + " \n", + " def __init__(self):\n", + " super().__init__('Eddie')\n", + " print(\"EXTEND\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello Eddie\n", + "EXTEND\n" + ] + } + ], + "source": [ + "y = ExtendedClass()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "YELLING!!!\n" + ] + } + ], + "source": [ + "y.yell()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### " + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Numpy.ipynb b/Numpy.ipynb new file mode 100644 index 0000000..28119fb --- /dev/null +++ b/Numpy.ipynb @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "my_list = [1,2,3]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(np.array(my_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "arr = np.array(my_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(0,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 2, 4, 6, 8, 10])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(0,11,2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.zeros(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0.]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.zeros((3,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 11. ])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linspace(0,11,11)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[7, 3, 7],\n", + " [8, 3, 2],\n", + " [1, 9, 0]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.randint(0,10,(3,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([95, 11, 81, 70, 63, 87, 75, 9, 77, 40])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(101)\n", + "\n", + "np.random.randint(0,100,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4, 63, 40, 60, 92, 64, 5, 12, 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"outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import torchvision" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git 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Attempting to use lzma compression will result in a RuntimeError.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "mydata = np.random.randint(0,101,(50,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 97, 70, 23, 81],\n", + " [ 60, 40, 73, 7],\n", + " [ 60, 88, 87, 67],\n", + " [ 42, 25, 81, 100],\n", + " [ 45, 30, 69, 72],\n", + " [ 16, 100, 70, 14],\n", + " [ 25, 76, 32, 70],\n", + " [ 2, 29, 46, 65],\n", + " [ 71, 33, 11, 79],\n", + " [ 98, 16, 76, 9],\n", + " [ 0, 78, 61, 34],\n", + " [ 78, 43, 45, 87],\n", + " [ 95, 78, 6, 34],\n", + " [ 14, 44, 95, 27],\n", + " [ 32, 31, 90, 58],\n", + " [ 72, 15, 16, 59],\n", + " [ 5, 72, 31, 36],\n", + " [ 72, 30, 94, 55],\n", + " [ 55, 19, 91, 74],\n", + " [ 9, 20, 10, 34],\n", + " [ 3, 6, 25, 49],\n", + " [ 63, 61, 86, 55],\n", + " [ 25, 21, 0, 85],\n", + " [ 80, 66, 73, 51],\n", + " [ 96, 46, 35, 58],\n", + " [ 7, 4, 89, 25],\n", + " [ 92, 11, 77, 59],\n", + " [ 38, 94, 19, 46],\n", + " [ 34, 24, 94, 70],\n", + " [100, 91, 46, 76],\n", + " [ 43, 10, 35, 78],\n", + " [ 15, 24, 57, 6],\n", + " [ 51, 47, 47, 55],\n", + " [ 83, 5, 84, 40],\n", + " [100, 22, 26, 72],\n", + " [ 60, 83, 80, 92],\n", + " [ 28, 39, 82, 17],\n", + " [ 56, 20, 94, 85],\n", + " [ 72, 56, 63, 54],\n", + " [ 15, 60, 30, 72],\n", + " [ 41, 21, 86, 54],\n", + " [ 85, 7, 50, 87],\n", + " [ 48, 13, 69, 93],\n", + " [ 75, 20, 98, 96],\n", + " [ 41, 18, 14, 31],\n", + " [ 84, 13, 62, 6],\n", + " [ 13, 40, 77, 60],\n", + " [ 70, 18, 84, 26],\n", + " [ 97, 22, 24, 34],\n", + " [ 56, 83, 9, 95]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mydata" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(data=mydata, columns = ['f1','f2','f3','label'])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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41 18 14 31\n", + "45 84 13 62 6\n", + "46 13 40 77 60\n", + "47 70 18 84 26\n", + "48 97 22 24 34\n", + "49 56 83 9 95" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "X = df[['f1','f2','f3']]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "y = df['label']" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + 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b/Tensors1D.ipynb new file mode 100644 index 0000000..1ca386f --- /dev/null +++ b/Tensors1D.ipynb @@ -0,0 +1,1180 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.tensor([7,4,3,2,6])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(7)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(6)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'torch.LongTensor'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.type()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.tensor([0.0,1.0,2.0,3.0,4.0])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.float32" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + 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"a.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.FloatTensor([0,1,2,3,4])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.float32" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'torch.FloatTensor'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.type()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0., 1., 2., 3., 4.])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.tensor([0,1,2,3,4])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "a = a.type(torch.FloatTensor)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'torch.FloatTensor'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.type()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.float32" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = torch.tensor([4, 1, 2, 3, 4])\n", + "b=a.type(torch.float32)\n", + "b.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + 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"source": [ + "a_col" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "b_col = a.view(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[1],\n", + " [2],\n", + " [3],\n", + " [4],\n", + " [5]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b_col" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b_col.ndimension()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "numpy_array = np.array([0.0,1.0,2.0,3.0,4.0])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "torch_tensor = torch.from_numpy(numpy_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "back_to_numpy = torch_tensor.numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/eddie/.pyenv/versions/3.7.6/envs/pytorch/lib/python3.7/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "pandas_series = pd.Series([0.1,2,0.3,10.1])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "pandas_to_torch = torch.from_numpy(pandas_series.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "this_tensor = torch.tensor([0,1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "torch_to_list = this_tensor.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": 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[ + "c[0] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([100, 1, 2, 3, 4])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "c[4] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([100, 1, 2, 3, 0])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "d = c[1:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1, 2, 3])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "c[3:5] = torch.tensor([300,400])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([100, 1, 2, 300, 400])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "u = torch.tensor([1, 2, 3, -1])\n", + "v = u + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([2, 3, 4, 0])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "u = torch.tensor([1,0])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "v = torch.tensor([0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "w = u + v" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1, 1])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "z = 2*w" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([2, 2])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "u = torch.tensor([1,2])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "v = torch.tensor([3,2])" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "z = u * v" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([3, 4])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "v = torch.tensor([3,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "result = torch.dot(u,v)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(5)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.tensor([1.0,-1.0,1.0,-1.0])" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "mean_a = a.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_a" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "x=torch.tensor([0,np.pi/2,np.pi])" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + 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"source": [ + "x=torch.linspace(0,2*np.pi,100)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "y=torch.sin(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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b/Visualization.ipynb new file mode 100644 index 0000000..3ace716 --- /dev/null +++ b/Visualization.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(0,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "y = x**2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Y label')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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