1181 lines
35 KiB
Plaintext
1181 lines
35 KiB
Plaintext
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{
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"metadata": {},
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"source": [
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"import torch"
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]
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"a = torch.tensor([7,4,3,2,6])"
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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{
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"tensor(7)"
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]
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"execution_count": 3,
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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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"a[0]"
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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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"tensor(6)"
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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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"a[4]"
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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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"torch.int64"
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},
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"execution_count": 5,
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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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"a.dtype"
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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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"'torch.LongTensor'"
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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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"a.type()"
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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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"source": [
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"a = torch.tensor([0.0,1.0,2.0,3.0,4.0])"
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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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"data": {
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"text/plain": [
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"torch.float32"
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]
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},
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"execution_count": 8,
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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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"a.dtype"
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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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"data": {
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"text/plain": [
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"'torch.FloatTensor'"
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]
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},
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"execution_count": 9,
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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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"a.type()"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = torch.tensor([0.0,1.0,2.0,3.0,4.0], dtype=torch.int32)"
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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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"torch.int32"
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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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"a.dtype"
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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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"source": [
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"a = torch.tensor([4.0, 1.0, 2.0, 3.0, 4])"
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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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"torch.float32"
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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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"a.dtype"
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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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"source": [
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"a = torch.FloatTensor([0,1,2,3,4])"
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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": 15,
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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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"torch.float32"
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]
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},
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"execution_count": 15,
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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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"a.dtype"
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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": 16,
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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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"'torch.FloatTensor'"
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]
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},
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"execution_count": 16,
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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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"a.type()"
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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": 17,
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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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"tensor([0., 1., 2., 3., 4.])"
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]
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},
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"execution_count": 17,
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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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"a"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = torch.tensor([0,1,2,3,4])"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = a.type(torch.FloatTensor)"
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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": 20,
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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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"'torch.FloatTensor'"
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]
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},
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"execution_count": 20,
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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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"a.type()"
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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": 21,
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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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"torch.float32"
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]
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},
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"execution_count": 21,
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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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"a = torch.tensor([4, 1, 2, 3, 4])\n",
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"b=a.type(torch.float32)\n",
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"b.dtype"
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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": 22,
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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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"torch.Size([5])"
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]
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},
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"execution_count": 22,
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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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"a.size()"
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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": 23,
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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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"1"
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]
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},
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"execution_count": 23,
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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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"a.ndimension()"
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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": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = torch.tensor([1,2,3,4,5])"
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"a_col = a.view(5,1)"
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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": 26,
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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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"tensor([[1],\n",
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" [2],\n",
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" [3],\n",
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" [4],\n",
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" [5]])"
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]
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},
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"execution_count": 26,
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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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"a_col"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"b_col = a.view(-1,1)"
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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": 28,
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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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"tensor([[1],\n",
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" [2],\n",
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" [3],\n",
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" [4],\n",
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" [5]])"
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]
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},
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"execution_count": 28,
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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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"b_col"
|
||
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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": 29,
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"outputs": [
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{
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"data": {
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"2"
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"execution_count": 29,
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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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"b_col.ndimension()"
|
||
|
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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": 30,
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"metadata": {},
|
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"outputs": [],
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"source": [
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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": 31,
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"metadata": {},
|
||
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"outputs": [],
|
||
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"source": [
|
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"numpy_array = np.array([0.0,1.0,2.0,3.0,4.0])"
|
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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": 32,
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"metadata": {},
|
||
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"outputs": [],
|
||
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"source": [
|
||
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"torch_tensor = torch.from_numpy(numpy_array)"
|
||
|
|
]
|
||
|
|
},
|
||
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{
|
||
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"cell_type": "code",
|
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"execution_count": 33,
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"metadata": {},
|
||
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"outputs": [],
|
||
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"source": [
|
||
|
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"back_to_numpy = torch_tensor.numpy()"
|
||
|
|
]
|
||
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},
|
||
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{
|
||
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"cell_type": "code",
|
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"execution_count": 35,
|
||
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"metadata": {},
|
||
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"outputs": [
|
||
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{
|
||
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"name": "stderr",
|
||
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"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",
|
||
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"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)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
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"cell_type": "code",
|
||
|
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"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": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"torch_to_list"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"new_tensor = torch.tensor([1,2,3,4,5])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 42,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"tensor(1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 42,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_tensor[0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"2"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_tensor[1].item()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"c = torch.tensor([20,1,2,3,4])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"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": [
|
||
|
|
"y=torch.sin(x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 81,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"tensor([ 0.0000e+00, 1.0000e+00, -8.7423e-08])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 81,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"y"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 84,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"-4.371138828673793e-07"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 84,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"y[2].item()*5"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 88,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"tensor([-2., -1., 0., 1., 2.])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 88,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"torch.linspace(-2,2,steps=5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 89,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"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": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x1295f5d10>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 95,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(x.numpy(),y.numpy())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 96,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"'1.4.0'"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 96,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"torch.__version__"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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
|
||
|
|
}
|