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‎tutorial-contents-notebooks/201_torch_numpy.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 201 Torch and Numpy\n",
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"\n",
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"View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n",
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"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
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"\n",
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"Dependencies:\n",
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"* torch: 0.1.11\n",
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"* numpy\n",
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"\n",
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"Details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"numpy array: [[0 1 2]\n",
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" [3 4 5]] \n",
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"torch tensor: \n",
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" 0 1 2\n",
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" 3 4 5\n",
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"[torch.LongTensor of size 2x3]\n",
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" \n",
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"tensor to array: [[0 1 2]\n",
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" [3 4 5]]\n"
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]
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}
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],
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"source": [
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"# convert numpy to tensor or vise versa\n",
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"np_data = np.arange(6).reshape((2, 3))\n",
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"torch_data = torch.from_numpy(np_data)\n",
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"tensor2array = torch_data.numpy()\n",
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"print(\n",
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" '\\nnumpy array:', np_data, # [[0 1 2], [3 4 5]]\n",
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" '\\ntorch tensor:', torch_data, # 0 1 2 \\n 3 4 5 [torch.LongTensor of size 2x3]\n",
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" '\\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"abs \n",
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"numpy: [1 2 1 2] \n",
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"torch: \n",
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" 1\n",
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" 2\n",
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" 1\n",
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" 2\n",
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"[torch.FloatTensor of size 4]\n",
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"\n"
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]
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}
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],
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"source": [
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"# abs\n",
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"data = [-1, -2, 1, 2]\n",
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"tensor = torch.FloatTensor(data) # 32-bit floating point\n",
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"print(\n",
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" '\\nabs',\n",
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" '\\nnumpy: ', np.abs(data), # [1 2 1 2]\n",
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" '\\ntorch: ', torch.abs(tensor) # [1 2 1 2]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\n",
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" 1\n",
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" 2\n",
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" 1\n",
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" 2\n",
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"[torch.FloatTensor of size 4]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tensor.abs()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"sin \n",
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"numpy: [-0.84147098 -0.90929743 0.84147098 0.90929743] \n",
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"torch: \n",
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"-0.8415\n",
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"-0.9093\n",
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" 0.8415\n",
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" 0.9093\n",
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"[torch.FloatTensor of size 4]\n",
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"\n"
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]
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}
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],
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"source": [
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"# sin\n",
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"print(\n",
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" '\\nsin',\n",
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" '\\nnumpy: ', np.sin(data), # [-0.84147098 -0.90929743 0.84147098 0.90929743]\n",
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" '\\ntorch: ', torch.sin(tensor) # [-0.8415 -0.9093 0.8415 0.9093]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\n",
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" 0.2689\n",
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" 0.1192\n",
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" 0.7311\n",
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" 0.8808\n",
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"[torch.FloatTensor of size 4]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tensor.sigmoid()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\n",
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" 0.3679\n",
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" 0.1353\n",
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" 2.7183\n",
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" 7.3891\n",
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"[torch.FloatTensor of size 4]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tensor.exp()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"mean \n",
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"numpy: 0.0 \n",
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"torch: 0.0\n"
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]
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}
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],
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"source": [
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"# mean\n",
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"print(\n",
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" '\\nmean',\n",
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" '\\nnumpy: ', np.mean(data), # 0.0\n",
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" '\\ntorch: ', torch.mean(tensor) # 0.0\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"matrix multiplication (matmul) \n",
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"numpy: [[ 7 10]\n",
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" [15 22]] \n",
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"torch: \n",
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" 7 10\n",
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" 15 22\n",
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"[torch.FloatTensor of size 2x2]\n",
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"\n"
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]
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}
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],
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"source": [
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"# matrix multiplication\n",
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"data = [[1,2], [3,4]]\n",
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"tensor = torch.FloatTensor(data) # 32-bit floating point\n",
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"# correct method\n",
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"print(\n",
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" '\\nmatrix multiplication (matmul)',\n",
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" '\\nnumpy: ', np.matmul(data, data), # [[7, 10], [15, 22]]\n",
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" '\\ntorch: ', torch.mm(tensor, tensor) # [[7, 10], [15, 22]]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"matrix multiplication (dot) \n",
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"numpy: [[ 7 10]\n",
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" [15 22]] \n",
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"torch: 30.0\n"
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]
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}
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],
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"source": [
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"# incorrect method\n",
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"data = np.array(data)\n",
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"print(\n",
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" '\\nmatrix multiplication (dot)',\n",
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" '\\nnumpy: ', data.dot(data), # [[7, 10], [15, 22]]\n",
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" '\\ntorch: ', tensor.dot(tensor) # this will convert tensor to [1,2,3,4], you'll get 30.0\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note that:\n",
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"\n",
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"torch.dot(tensor1, tensor2) → float\n",
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"\n",
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"Computes the dot product (inner product) of two tensors. Both tensors are treated as 1-D vectors."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\n",
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" 7 10\n",
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" 15 22\n",
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"[torch.FloatTensor of size 2x2]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tensor.mm(tensor)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\n",
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" 1 4\n",
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" 9 16\n",
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"[torch.FloatTensor of size 2x2]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tensor * tensor"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"30.0"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tensor.dot(tensor)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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