PyTorch_1_Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on several sources:<br>\n",
    "* http://deeplizard.com/\n",
    "* https://pytorch.org/docs/master/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import PyTorch\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.1.post2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check version number\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if CUDA/GPU is available\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if CUDA/GPU is available print CUDA version\n",
    "if torch.cuda.is_available(): torch.version.cuda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create tensors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details see [PyTorch Tensors Explained](http://deeplizard.com/learn/video/jexkKugTg04) and [Creating PyTorch Tensors for Deep Learning](http://deeplizard.com/learn/video/AglLTlms7HU)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate tensor\n",
    "t = torch.tensor([1, 2, 3]); t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__array__',\n",
       " '__array_priority__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dict__',\n",
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       " '__div__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__idiv__',\n",
       " '__ilshift__',\n",
       " '__imul__',\n",
       " '__index__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__ipow__',\n",
       " '__irshift__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__long__',\n",
       " '__lshift__',\n",
       " '__lt__',\n",
       " '__matmul__',\n",
       " '__mod__',\n",
       " '__module__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__nonzero__',\n",
       " '__or__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rdiv__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__rfloordiv__',\n",
       " '__rmul__',\n",
       " '__rpow__',\n",
       " '__rshift__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__weakref__',\n",
       " '__xor__',\n",
       " '_backward_hooks',\n",
       " '_base',\n",
       " '_cdata',\n",
       " '_coalesced_',\n",
       " '_dimI',\n",
       " '_dimV',\n",
       " '_grad',\n",
       " '_grad_fn',\n",
       " '_indices',\n",
       " '_make_subclass',\n",
       " '_nnz',\n",
       " '_values',\n",
       " '_version',\n",
       " 'abs',\n",
       " 'abs_',\n",
       " 'acos',\n",
       " 'acos_',\n",
       " 'add',\n",
       " 'add_',\n",
       " 'addbmm',\n",
       " 'addbmm_',\n",
       " 'addcdiv',\n",
       " 'addcdiv_',\n",
       " 'addcmul',\n",
       " 'addcmul_',\n",
       " 'addmm',\n",
       " 'addmm_',\n",
       " 'addmv',\n",
       " 'addmv_',\n",
       " 'addr',\n",
       " 'addr_',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'any',\n",
       " 'apply_',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argsort',\n",
       " 'as_strided',\n",
       " 'as_strided_',\n",
       " 'asin',\n",
       " 'asin_',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atan2_',\n",
       " 'atan_',\n",
       " 'backward',\n",
       " 'baddbmm',\n",
       " 'baddbmm_',\n",
       " 'bernoulli',\n",
       " 'bernoulli_',\n",
       " 'bincount',\n",
       " 'bmm',\n",
       " 'btrifact',\n",
       " 'btrifact_with_info',\n",
       " 'btrisolve',\n",
       " 'byte',\n",
       " 'cauchy_',\n",
       " 'ceil',\n",
       " 'ceil_',\n",
       " 'char',\n",
       " 'cholesky',\n",
       " 'chunk',\n",
       " 'clamp',\n",
       " 'clamp_',\n",
       " 'clamp_max',\n",
       " 'clamp_max_',\n",
       " 'clamp_min',\n",
       " 'clamp_min_',\n",
       " 'clone',\n",
       " 'coalesce',\n",
       " 'contiguous',\n",
       " 'copy_',\n",
       " 'cos',\n",
       " 'cos_',\n",
       " 'cosh',\n",
       " 'cosh_',\n",
       " 'cpu',\n",
       " 'cross',\n",
       " 'cuda',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'data',\n",
       " 'data_ptr',\n",
       " 'dense_dim',\n",
       " 'det',\n",
       " 'detach',\n",
       " 'detach_',\n",
       " 'device',\n",
       " 'diag',\n",
       " 'diag_embed',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'digamma',\n",
       " 'digamma_',\n",
       " 'dim',\n",
       " 'dist',\n",
       " 'div',\n",
       " 'div_',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dtype',\n",
       " 'eig',\n",
       " 'element_size',\n",
       " 'eq',\n",
       " 'eq_',\n",
       " 'equal',\n",
       " 'erf',\n",
       " 'erf_',\n",
       " 'erfc',\n",
       " 'erfc_',\n",
       " 'erfinv',\n",
       " 'erfinv_',\n",
       " 'exp',\n",
       " 'exp_',\n",
       " 'expand',\n",
       " 'expand_as',\n",
       " 'expm1',\n",
       " 'expm1_',\n",
       " 'exponential_',\n",
       " 'fft',\n",
       " 'fill_',\n",
       " 'flatten',\n",
       " 'flip',\n",
       " 'float',\n",
       " 'floor',\n",
       " 'floor_',\n",
       " 'fmod',\n",
       " 'fmod_',\n",
       " 'frac',\n",
       " 'frac_',\n",
       " 'gather',\n",
       " 'ge',\n",
       " 'ge_',\n",
       " 'gels',\n",
       " 'geometric_',\n",
       " 'geqrf',\n",
       " 'ger',\n",
       " 'gesv',\n",
       " 'get_device',\n",
       " 'grad',\n",
       " 'grad_fn',\n",
       " 'gt',\n",
       " 'gt_',\n",
       " 'half',\n",
       " 'hardshrink',\n",
       " 'histc',\n",
       " 'ifft',\n",
       " 'index_add',\n",
       " 'index_add_',\n",
       " 'index_copy',\n",
       " 'index_copy_',\n",
       " 'index_fill',\n",
       " 'index_fill_',\n",
       " 'index_put',\n",
       " 'index_put_',\n",
       " 'index_select',\n",
       " 'indices',\n",
       " 'int',\n",
       " 'inverse',\n",
       " 'irfft',\n",
       " 'is_coalesced',\n",
       " 'is_complex',\n",
       " 'is_contiguous',\n",
       " 'is_cuda',\n",
       " 'is_distributed',\n",
       " 'is_floating_point',\n",
       " 'is_leaf',\n",
       " 'is_nonzero',\n",
       " 'is_pinned',\n",
       " 'is_same_size',\n",
       " 'is_set_to',\n",
       " 'is_shared',\n",
       " 'is_signed',\n",
       " 'is_sparse',\n",
       " 'isclose',\n",
       " 'item',\n",
       " 'kthvalue',\n",
       " 'layout',\n",
       " 'le',\n",
       " 'le_',\n",
       " 'lerp',\n",
       " 'lerp_',\n",
       " 'lgamma',\n",
       " 'lgamma_',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log10_',\n",
       " 'log1p',\n",
       " 'log1p_',\n",
       " 'log2',\n",
       " 'log2_',\n",
       " 'log_',\n",
       " 'log_normal_',\n",
       " 'log_softmax',\n",
       " 'logdet',\n",
       " 'logsumexp',\n",
       " 'long',\n",
       " 'lt',\n",
       " 'lt_',\n",
       " 'map2_',\n",
       " 'map_',\n",
       " 'masked_fill',\n",
       " 'masked_fill_',\n",
       " 'masked_scatter',\n",
       " 'masked_scatter_',\n",
       " 'masked_select',\n",
       " 'matmul',\n",
       " 'matrix_power',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'min',\n",
       " 'mm',\n",
       " 'mode',\n",
       " 'mul',\n",
       " 'mul_',\n",
       " 'multinomial',\n",
       " 'mv',\n",
       " 'mvlgamma',\n",
       " 'mvlgamma_',\n",
       " 'name',\n",
       " 'narrow',\n",
       " 'narrow_copy',\n",
       " 'ndimension',\n",
       " 'ne',\n",
       " 'ne_',\n",
       " 'neg',\n",
       " 'neg_',\n",
       " 'nelement',\n",
       " 'new',\n",
       " 'new_empty',\n",
       " 'new_full',\n",
       " 'new_ones',\n",
       " 'new_tensor',\n",
       " 'new_zeros',\n",
       " 'nonzero',\n",
       " 'norm',\n",
       " 'normal_',\n",
       " 'numel',\n",
       " 'numpy',\n",
       " 'orgqr',\n",
       " 'ormqr',\n",
       " 'output_nr',\n",
       " 'permute',\n",
       " 'pin_memory',\n",
       " 'pinverse',\n",
       " 'polygamma',\n",
       " 'polygamma_',\n",
       " 'potrf',\n",
       " 'potri',\n",
       " 'potrs',\n",
       " 'pow',\n",
       " 'pow_',\n",
       " 'prelu',\n",
       " 'prod',\n",
       " 'pstrf',\n",
       " 'put_',\n",
       " 'qr',\n",
       " 'random_',\n",
       " 'reciprocal',\n",
       " 'reciprocal_',\n",
       " 'record_stream',\n",
       " 'register_hook',\n",
       " 'reinforce',\n",
       " 'relu',\n",
       " 'relu_',\n",
       " 'remainder',\n",
       " 'remainder_',\n",
       " 'renorm',\n",
       " 'renorm_',\n",
       " 'repeat',\n",
       " 'requires_grad',\n",
       " 'requires_grad_',\n",
       " 'reshape',\n",
       " 'reshape_as',\n",
       " 'resize',\n",
       " 'resize_',\n",
       " 'resize_as',\n",
       " 'resize_as_',\n",
       " 'retain_grad',\n",
       " 'rfft',\n",
       " 'roll',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'rsqrt',\n",
       " 'rsqrt_',\n",
       " 'scatter',\n",
       " 'scatter_',\n",
       " 'scatter_add',\n",
       " 'scatter_add_',\n",
       " 'select',\n",
       " 'set_',\n",
       " 'shape',\n",
       " 'share_memory_',\n",
       " 'short',\n",
       " 'sigmoid',\n",
       " 'sigmoid_',\n",
       " 'sign',\n",
       " 'sign_',\n",
       " 'sin',\n",
       " 'sin_',\n",
       " 'sinh',\n",
       " 'sinh_',\n",
       " 'size',\n",
       " 'slogdet',\n",
       " 'smm',\n",
       " 'softmax',\n",
       " 'sort',\n",
       " 'sparse_dim',\n",
       " 'sparse_mask',\n",
       " 'sparse_resize_',\n",
       " 'sparse_resize_and_clear_',\n",
       " 'split',\n",
       " 'split_with_sizes',\n",
       " 'sqrt',\n",
       " 'sqrt_',\n",
       " 'squeeze',\n",
       " 'squeeze_',\n",
       " 'sspaddmm',\n",
       " 'std',\n",
       " 'stft',\n",
       " 'storage',\n",
       " 'storage_offset',\n",
       " 'storage_type',\n",
       " 'stride',\n",
       " 'sub',\n",
       " 'sub_',\n",
       " 'sum',\n",
       " 'svd',\n",
       " 'symeig',\n",
       " 't',\n",
       " 't_',\n",
       " 'take',\n",
       " 'tan',\n",
       " 'tan_',\n",
       " 'tanh',\n",
       " 'tanh_',\n",
       " 'to',\n",
       " 'to_dense',\n",
       " 'to_sparse',\n",
       " 'tolist',\n",
       " 'topk',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'transpose_',\n",
       " 'tril',\n",
       " 'tril_',\n",
       " 'triu',\n",
       " 'triu_',\n",
       " 'trtrs',\n",
       " 'trunc',\n",
       " 'trunc_',\n",
       " 'type',\n",
       " 'type_as',\n",
       " 'unbind',\n",
       " 'unfold',\n",
       " 'uniform_',\n",
       " 'unique',\n",
       " 'unsqueeze',\n",
       " 'unsqueeze_',\n",
       " 'values',\n",
       " 'var',\n",
       " 'view',\n",
       " 'view_as',\n",
       " 'where',\n",
       " 'zero_']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print list of tensor attributes\n",
    "dir(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# but tensor on GPU if available\n",
    "if torch.cuda.is_available(): t.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1, 2, 3]), torch.Size([3]), torch.int64, device(type='cpu'))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print important attributes\n",
    "t.data, t.shape, t.dtype, t.device#, t.layout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See PyTorch documentation for [torch.Tensor data types](https://pytorch.org/docs/master/tensors.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummy data\n",
    "data = np.array([1,2,3]); data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 2., 3.])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use constructor\n",
    "t1 = torch.Tensor(data); t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use factory function\n",
    "t2 = torch.tensor(data); t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = torch.as_tensor(data); t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t4 = torch.from_numpy(data); t4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.float32, torch.int64, torch.int64, torch.int64)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.dtype, t2.dtype, t3.dtype, t4.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(int, float)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(7), type(7.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The prefered option is [`torch.tensor`](https://pytorch.org/docs/master/torch.html#torch.tensor) because it uses type inference, i.e., it inferes and uses the data type of the input data.<br>Contrary, [`torch.Tensor`](https://pytorch.org/docs/stable/tensors.html#torch.Tensor) always uses the default dtype.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.get_default_dtype()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.Tensor(data).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(data).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(data).float().dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other useful ways to construct tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0.],\n",
       "        [0., 0.]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.zeros(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1.],\n",
       "        [1., 1.]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.ones(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 0.],\n",
       "        [0., 1.]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.eye(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.8615, 0.7447],\n",
       "        [0.7829, 0.1524]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.arange(5).expand((3,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copy and share memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99,  2,  3])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0] = 99; data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1., 2., 3.]),\n",
       " tensor([1, 2, 3]),\n",
       " tensor([99,  2,  3]),\n",
       " tensor([99,  2,  3]))"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1, t2, t3, t4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Copy Data:** torch.Tensor() & torch.tensor()<br>\n",
    "**Share Data:** torch.as_tensor() & torch.from_numpy() (only accepts np.arrays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The prefered option is [`torch.as_tensor`](https://pytorch.org/docs/master/torch.html#torch.as_tensor) because it works with several input data types, i.e., list, tuple, NumPy ndarray, scalar, and other types.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank, axes, and shapes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional: Refresh your knowledge of [rank, axes, and shape of tensors for deep learning](http://deeplizard.com/learn/video/AiyK0idr4uM) and look at a [CNN Tensor Shape Example](http://deeplizard.com/learn/video/k6ZF1TSniYk)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0703, -2.0433, -0.6535],\n",
       "        [ 0.1943, -0.5165, -1.2701],\n",
       "        [ 1.0064, -1.8182, -0.2117]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5 = torch.randn((3,3)); t5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([3, 3]), torch.Size([3, 3]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5.shape, t.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5.numel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5.reshape(1,-1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([9, 1])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5.view(9,-1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[What's the difference between reshape and view in pytorch?](https://stackoverflow.com/questions/49643225/whats-the-difference-between-reshape-and-view-in-pytorch): \"If you just want to reshape tensors, use torch.reshape. If you're also concerned about memory usage and want to ensure that the two tensors share the same data, use torch.view.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([9])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5.view(9,-1).squeeze().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 3])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5.unsqueeze(dim=0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 1, 3])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5.unsqueeze(dim=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "t6 = torch.rand(3,4,5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 4, 5, 6])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([6, 4, 5, 3])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t6.transpose(0,-1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "t7 = torch.rand(28,28,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([28, 28, 3])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 28, 28])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t7.permute(2,0,1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continue with the [deeplizard tutorials](http://deeplizard.com/learn/video/fCVuiW9AFzY)."
   ]
  }
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