#python #python-3.x #pytorch
Вопрос:
Это мой код;
for img_loc in list(self.train_data)[idx]:
images_set.append(self.load_ucf_image(img_loc))
print(images_set)
И это его результат
[tensor([[[ 1.7865, 1.8893, 1.9578, ..., -1.3815, -0.4054, 0.2967],
[ 1.7694, 1.8722, 1.9578, ..., -0.6452, -0.4054, 0.1254],
[ 1.7523, 1.8722, 1.9749, ..., -0.5082, -0.6623, -0.3541],
...,
[-1.9809, -1.6384, -1.2617, ..., -1.7754, -1.0562, -0.9020],
[-2.0494, -1.8268, -1.5014, ..., -1.1075, -1.4672, -1.7069],
[-1.9980, -1.8953, -1.4672, ..., -1.7412, -1.7069, -1.2445]],
[[ 1.0805, 1.2556, 1.3606, ..., -1.1954, -0.1975, 0.4678],
[ 1.1155, 1.2731, 1.3957, ..., -0.4426, -0.1975, 0.3102],
[ 1.1155, 1.2731, 1.3957, ..., -0.2850, -0.4426, -0.1800],
...,
[-1.9657, -1.7556, -1.4580, ..., -1.8957, -1.2304, -1.1253],
[-2.0007, -1.9132, -1.6856, ..., -1.1954, -1.6331, -1.9482],
[-1.9482, -1.9482, -1.5980, ..., -1.8431, -1.8782, -1.4230]],
[[ 1.4548, 1.6640, 1.8034, ..., -0.4624, 0.5311, 1.3502],
[ 1.5071, 1.7163, 1.8557, ..., 0.2871, 0.5311, 1.1411],
[ 1.5245, 1.7511, 1.9080, ..., 0.3916, 0.2348, 0.6182],
...,
[-1.4907, -1.3164, -1.0550, ..., -1.5953, -0.9504, -0.8284],
[-1.5430, -1.4907, -1.2816, ..., -0.8633, -1.3164, -1.6476],
[-1.4907, -1.5256, -1.2119, ..., -1.5081, -1.5256, -1.1421]]]), tensor([[[-0.4054, -0.3883, -0.4739, ..., 2.1119, 2.0948, 2.0777],
[-0.3712, -0.2856, -0.4397, ..., 2.0948, 2.0777, 2.0434],
[-0.3541, -0.2513, 0.1083, ..., 2.0777, 2.0434, 2.0263],
...,
[-1.0219, -1.2617, -1.2103, ..., -0.0629, -0.1999, -0.2171],
[-1.0048, -1.3302, -1.3302, ..., -0.0801, -0.1828, -0.4226],
[-1.3302, -0.9705, -1.0562, ..., -0.0458, -0.1486, -0.7993]],
[[-0.2325, -0.2150, -0.2675, ..., 1.5707, 1.5007, 1.4132],
[-0.1975, -0.1099, -0.2500, ..., 1.6057, 1.5357, 1.4482],
[-0.2150, -0.1099, 0.2752, ..., 1.6057, 1.5532, 1.4482],
...,
[-1.1779, -1.4230, -1.3704, ..., -0.3901, -0.4776, -0.4776],
[-1.1779, -1.5630, -1.5280, ..., -0.4076, -0.4601, -0.6527],
[-1.5630, -1.2304, -1.2829, ..., -0.3200, -0.3901, -1.0028]],
[[ 0.7751, 0.7925, 0.6531, ..., 2.0300, 1.9428, 1.8731],
[ 0.8099, 0.8971, 0.6356, ..., 2.0823, 1.9951, 1.9254],
[ 0.8099, 0.8797, 1.1759, ..., 2.1171, 2.0300, 1.9603],
...,
[-0.8981, -1.1073, -1.0201, ..., -0.0092, -0.0790, -0.0441],
[-0.8981, -1.2293, -1.1944, ..., -0.0267, -0.0615, -0.2184],
[-1.2990, -0.9156, -0.9853, ..., 0.0431, -0.0092, -0.5670]]]), tensor([[[ 1.9749, 1.8208, 1.9749, ..., -0.8678, -0.8335, -0.8678],
[ 2.0777, 1.8893, 2.0777, ..., -0.8507, -0.8164, -0.8507],
[ 2.1290, 2.0777, 2.1290, ..., -0.7993, -0.7822, -0.7993],
...,
[-1.2617, -1.3644, -1.4672, ..., -1.9980, -2.0665, -2.1008],
[-1.1760, -1.2617, -1.3815, ..., -2.0665, -2.0837, -2.0837],
[-1.0904, -1.1760, -1.3302, ..., -2.1008, -2.0323, -2.0837]],
[[ 1.3256, 1.1856, 1.3431, ..., -0.7402, -0.7227, -0.7577],
[ 1.4482, 1.3081, 1.5007, ..., -0.7227, -0.7227, -0.7402],
[ 1.5532, 1.5357, 1.6057, ..., -0.6702, -0.6877, -0.6877],
...,
[-1.4230, -1.5630, -1.7031, ..., -2.0007, -2.0357, -2.0357],
[-1.3354, -1.4405, -1.6155, ..., -2.0357, -2.0357, -2.0357],
[-1.2479, -1.3529, -1.5630, ..., -2.0357, -1.9832, -2.0182]],
[[ 1.7163, 1.5768, 1.7511, ..., 0.0082, -0.0092, -0.0441],
[ 1.8383, 1.6814, 1.8905, ..., 0.0431, 0.0431, -0.0267],
[ 1.9254, 1.8905, 1.9777, ..., 0.0953, 0.0779, -0.0092],
...,
[-1.1073, -1.2293, -1.4036, ..., -1.6650, -1.6824, -1.6824],
[-1.0550, -1.1596, -1.3164, ..., -1.7696, -1.7173, -1.6999],
[-0.9678, -1.0724, -1.2641, ..., -1.7696, -1.6650, -1.6999]]]), tensor([[[-0.6794, -0.6452, -0.6452, ..., 2.1804, 2.1290, 2.0777],
[-0.6794, -0.6452, -0.5596, ..., 2.1633, 2.1804, 2.0948],
[-0.6452, -0.5767, -0.4739, ..., 2.1462, 2.0948, 2.0434],
...,
[-1.9980, -2.0494, -2.0494, ..., -1.4158, -1.3130, -1.3130],
[-2.0152, -2.0665, -1.9980, ..., -1.3644, -1.2445, -1.1760],
[-2.0152, -2.0494, -2.0323, ..., -1.3473, -1.2445, -1.0904]],
[[-0.6176, -0.5826, -0.5651, ..., 1.6933, 1.6057, 1.5182],
[-0.6176, -0.5476, -0.4776, ..., 1.7633, 1.7108, 1.5707],
[-0.5826, -0.4776, -0.3901, ..., 1.7983, 1.6933, 1.6057],
...,
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[-1.9657, -2.0357, -2.0182, ..., -1.6856, -1.5455, -1.3179]],
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[ 0.1476, 0.3045, 0.5136, ..., 2.1520, 2.0648, 1.9428],
...,
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[-1.7173, -1.7870, -1.7696, ..., -1.3861, -1.2119, -1.0201]]]), tensor([[[-0.3712, -0.4054, -0.3369, ..., 2.0092, 1.9407, 1.8379],
[-0.3027, -0.2684, -0.3712, ..., 2.0263, 1.9749, 1.8550],
[-0.1486, -0.3198, -0.3712, ..., 2.0092, 1.9578, 1.9064],
...,
[-1.9638, -2.0494, -2.0494, ..., -1.9638, -2.0665, -2.0152],
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[-1.1418, -1.9638, -2.0323, ..., -2.0323, -2.0323, -1.9980]],
[[-0.1975, -0.2325, -0.1975, ..., 1.2906, 1.1856, 1.0280],
[-0.1275, -0.0924, -0.2325, ..., 1.3256, 1.2556, 1.1331],
[-0.0049, -0.1800, -0.2325, ..., 1.3782, 1.3081, 1.2031],
...,
[-2.0357, -2.0357, -2.0357, ..., -1.8957, -1.9482, -1.8782],
[-2.0357, -2.0357, -1.9832, ..., -1.8782, -1.9482, -1.8256],
[-1.2129, -2.0007, -2.0357, ..., -1.9132, -1.8431, -1.7906]],
[[ 0.7751, 0.7402, 0.7925, ..., 1.6291, 1.5420, 1.4025],
[ 0.8448, 0.9145, 0.7576, ..., 1.6988, 1.6291, 1.4722],
[ 0.9842, 0.8448, 0.7576, ..., 1.7685, 1.6988, 1.5768],
...,
[-1.7173, -1.7696, -1.7696, ..., -1.4210, -1.4559, -1.3861],
[-1.6476, -1.6999, -1.6127, ..., -1.4036, -1.4559, -1.3164],
[-0.8284, -1.6302, -1.6476, ..., -1.4210, -1.3339, -1.2816]]]), tensor([[[-0.3198, -0.3883, -0.4226, ..., 1.8893, 1.7352, 1.7352],
[-0.2684, -0.3369, -0.3541, ..., 1.8893, 1.7523, 1.7523],
[-0.1999, -0.2342, -0.3712, ..., 1.9235, 1.8037, 1.8037],
...,
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[-1.9638, -2.0494, -2.0665, ..., -1.7925, -1.7583, -1.6898]],
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[ 0.9319, 0.8971, 0.7576, ..., 1.4897, 1.3154, 1.2805],
...,
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[-1.7173, -1.7696, -1.7696, ..., -1.1770, -1.0201, -0.9504]]]), tensor([[[ 2.0605, 2.0434, 2.0263, ..., -0.3198, -0.3027, -0.2856],
[ 2.0434, 2.0263, 2.0263, ..., -0.2171, -0.2171, -0.2171],
[ 2.0777, 2.0263, 2.0092, ..., -0.1999, -0.1828, -0.1999],
...,
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...,
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...,
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[ 0.5485, 0.4788, 0.4962, ..., -1.7870, -1.8044, -1.8044]]]), tensor([[[-0.3369, -0.3198, -0.3541, ..., 2.0605, 2.0777, 2.0948],
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...,
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[-2.0494, -2.0494, -1.9638, ..., 0.5193, 0.5022, 0.4166]],
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...,
[-2.0357, -2.0357, -1.9832, ..., 0.2227, 0.2402, 0.2227],
[-1.9832, -2.0182, -2.0182, ..., 0.2577, 0.2752, 0.2227],
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[[ 0.5659, 0.5834, 0.6531, ..., 2.0125, 2.0125, 1.9603],
[ 0.6705, 0.7054, 0.7925, ..., 2.0997, 2.0997, 2.0125],
[ 0.7402, 0.8099, 0.8797, ..., 2.1346, 2.1346, 2.0474],
...,
[-1.7696, -1.7870, -1.7347, ..., 0.5485, 0.6182, 0.6182],
[-1.7173, -1.7522, -1.7522, ..., 0.5834, 0.6356, 0.6008],
[-1.8044, -1.8044, -1.7173, ..., 0.5834, 0.6182, 0.5834]]]), tensor([[[ 2.0777, 2.0605, 2.0777, ..., 0.1426, 0.1083, 0.0569],
[ 2.1119, 2.0777, 2.0948, ..., 0.1939, 0.1597, 0.0569],
[ 2.0948, 2.0777, 2.0948, ..., 0.1768, 0.1426, 0.0056],
...,
[ 0.4679, 0.6906, 0.7248, ..., -0.5253, -0.5082, -0.5938],
[ 0.0912, 0.6049, 0.7248, ..., -0.6281, -0.9877, -0.5082],
[-0.5253, 0.5707, 0.6734, ..., -0.8678, -0.6452, -0.2342]],
[[ 1.9559, 2.0084, 2.0784, ..., 0.2227, 0.1877, 0.1352],
[ 2.0259, 2.0609, 2.0959, ..., 0.2227, 0.1877, 0.0826],
[ 2.0084, 2.0609, 2.0959, ..., 0.2227, 0.1702, 0.0301],
...,
[ 0.2052, 0.3627, 0.3452, ..., -0.7927, -0.8277, -0.9153],
[-0.1450, 0.2752, 0.3452, ..., -0.9678, -1.3880, -0.9153],
[-0.7752, 0.2402, 0.3102, ..., -1.2829, -1.0903, -0.7052]],
[[ 2.3088, 2.3437, 2.4308, ..., 1.1934, 1.1585, 1.1062],
[ 2.3611, 2.3786, 2.4483, ..., 1.2108, 1.1759, 1.0714],
[ 2.3437, 2.3786, 2.4483, ..., 1.1585, 1.1585, 1.0191],
...,
[ 0.6008, 0.7402, 0.7054, ..., -0.5147, -0.5495, -0.6367],
[ 0.2348, 0.6182, 0.7054, ..., -0.6890, -1.1247, -0.6541],
[-0.3927, 0.5834, 0.6182, ..., -1.0201, -0.8458, -0.4624]]]), tensor([[[ 2.1119, 2.1633, 2.0948, ..., -0.1143, -0.1486, -0.1657],
[ 2.0777, 2.0605, 2.1119, ..., -0.0458, -0.0629, -0.0972],
[ 2.1462, 2.1633, 2.0777, ..., 0.0569, 0.0569, 0.0056],
...,
[ 0.3652, 0.4679, 0.6563, ..., -2.0323, -1.9638, -1.9980],
[ 0.1083, 0.4679, 0.6734, ..., -1.8610, -1.8953, -1.9638],
[-0.4911, 0.4337, 0.6392, ..., -0.6109, -1.8097, -1.8953]],
[[ 1.5007, 1.6408, 1.6933, ..., -0.1275, -0.1450, -0.1625],
[ 1.5357, 1.6232, 1.7633, ..., -0.0399, -0.0224, -0.0574],
[ 1.6758, 1.7983, 1.8508, ..., 0.0651, 0.0826, 0.0301],
...,
[ 0.0826, 0.1176, 0.2577, ..., -2.0357, -1.9832, -1.9832],
[-0.1450, 0.1527, 0.2927, ..., -1.9307, -1.9132, -1.9832],
[-0.7227, 0.1702, 0.3102, ..., -0.7402, -1.9307, -2.0182]],
[[ 1.8383, 2.0125, 2.0648, ..., 0.8099, 0.7925, 0.7751],
[ 1.8905, 1.9603, 2.1171, ..., 0.9145, 0.9145, 0.8797],
[ 2.0125, 2.1520, 2.2043, ..., 1.0191, 1.0714, 1.0191],
...,
[ 0.5311, 0.5485, 0.6182, ..., -1.8044, -1.7522, -1.7696],
[ 0.2871, 0.5311, 0.6531, ..., -1.6824, -1.6824, -1.7522],
[-0.3055, 0.5311, 0.6531, ..., -0.5147, -1.6999, -1.7870]]]), tensor([[[ 1.3755, 1.6153, 1.7694, ..., 0.0912, 0.2282, 0.1083],
[ 1.4269, 1.6495, 1.7694, ..., 0.1768, -0.0801, -0.7650],
[ 1.3755, 1.6324, 1.8037, ..., 0.0741, -0.5253, -0.0116],
...,
[-1.5528, -1.5699, -1.8097, ..., -0.8849, -0.6794, -0.6965],
[-1.6042, -1.5870, -1.8097, ..., -0.7479, -0.9534, -1.3302],
[-1.6898, -1.6727, -1.7412, ..., -1.6384, -1.3130, -0.7822]],
[[ 0.6429, 0.9230, 1.0980, ..., 0.2052, 0.3277, 0.2052],
[ 0.6954, 0.9405, 1.1331, ..., 0.2927, 0.0126, -0.6877],
[ 0.6604, 0.9580, 1.1681, ..., 0.1702, -0.4951, 0.0476],
...,
[-1.4230, -1.3880, -1.6155, ..., -0.7927, -0.6527, -0.7752],
[-1.4230, -1.3704, -1.5805, ..., -0.6352, -0.9678, -1.4230],
[-1.5105, -1.4405, -1.5105, ..., -1.6681, -1.4230, -0.9503]],
[[ 0.7925, 1.0539, 1.2805, ..., 1.1585, 1.2457, 1.1062],
[ 0.8971, 1.1411, 1.3502, ..., 1.2457, 0.9319, 0.2173],
[ 0.8971, 1.1759, 1.4200, ..., 1.0888, 0.4265, 0.9145],
...,
[-0.8284, -0.8284, -1.1421, ..., -0.4798, -0.3230, -0.4101],
[-0.8633, -0.8633, -1.1247, ..., -0.3753, -0.6193, -1.0898],
[-0.9504, -0.9853, -1.0898, ..., -1.3164, -1.0201, -0.5495]]]), tensor([[[ 0.0056, 0.0912, 0.1254, ..., 2.1633, 2.1119, 2.1119],
[ 0.0569, 0.1597, 0.1254, ..., 2.1633, 2.1119, 2.1462],
[ 0.0227, 0.1426, 0.1597, ..., 2.1804, 2.1462, 2.0948],
...,
[-0.5082, -0.9705, -0.6109, ..., 0.6049, -0.1999, -1.0219],
[-0.2171, -0.6452, -0.7650, ..., 0.6049, -0.6281, -0.9877],
[-1.1075, 0.0227, -0.6281, ..., 0.4337, -0.8507, -1.1247]],
[[ 0.1001, 0.2052, 0.2402, ..., 1.8859, 1.7458, 1.5882],
[ 0.1527, 0.2402, 0.2402, ..., 1.8859, 1.7458, 1.6232],
[ 0.1176, 0.2227, 0.2577, ..., 1.9034, 1.7633, 1.6232],
...,
[-0.8978, -1.3704, -0.9503, ..., 0.3627, -0.3725, -1.0903],
[-0.6176, -1.0553, -1.1604, ..., 0.3627, -0.7752, -1.0028],
[-1.5630, -0.3725, -1.0203, ..., 0.2227, -1.0028, -1.1429]],
[[ 1.0191, 1.1585, 1.1934, ..., 2.2566, 2.0648, 1.9428],
[ 1.0714, 1.2108, 1.1585, ..., 2.2914, 2.0997, 1.9777],
[ 1.0365, 1.1934, 1.1759, ..., 2.3088, 2.1694, 1.9777],
...,
[-0.6367, -1.1073, -0.6715, ..., 0.6356, -0.0441, -0.7238],
[-0.3927, -0.8284, -0.8981, ..., 0.6356, -0.4973, -0.6541],
[-1.3164, -0.1487, -0.7587, ..., 0.4614, -0.7238, -0.7936]]]), tensor([[[ 2.0777, 2.1119, 2.1119, ..., 0.0569, -0.0629, -0.2171],
[ 2.0777, 2.1290, 2.1119, ..., 0.0056, -0.1143, -0.2513],
[ 2.0777, 2.0948, 2.1290, ..., 0.0056, -0.1143, -0.1999],
...,
[ 0.4851, 0.5707, 0.5707, ..., -0.2171, -0.8849, -0.9192],
[ 0.3481, 0.5707, 0.5536, ..., -1.1075, -0.7650, -0.3883],
[-0.1657, 0.5193, 0.5193, ..., -0.8678, -0.3712, -1.1760]],
[[ 1.6408, 1.8158, 1.9384, ..., 0.1527, -0.0224, -0.1625],
[ 1.6758, 1.8508, 1.9909, ..., 0.1001, -0.0574, -0.1975],
[ 1.6758, 1.8683, 2.0084, ..., 0.0476, -0.0574, -0.1800],
...,
[ 0.2577, 0.2752, 0.2577, ..., -0.6176, -1.3004, -1.3354],
[ 0.1352, 0.2752, 0.2402, ..., -1.5455, -1.1779, -0.7927],
[-0.3725, 0.2227, 0.2052, ..., -1.2829, -0.7752, -1.5280]],
[[ 1.9603, 2.1520, 2.3088, ..., 1.0714, 0.8971, 0.6705],
[ 2.0125, 2.2217, 2.3437, ..., 1.0191, 0.8099, 0.6008],
[ 2.0125, 2.2217, 2.3960, ..., 0.9668, 0.8099, 0.6356],
...,
[ 0.5659, 0.5659, 0.5311, ..., -0.3927, -1.0724, -1.0724],
[ 0.3916, 0.5485, 0.5136, ..., -1.3513, -0.9504, -0.5321],
[-0.1138, 0.4962, 0.4788, ..., -1.0550, -0.5147, -1.2467]]]), tensor([[[ 0.0398, -0.8678, -0.1486, ..., 1.6153, 1.0502, 0.7077],
[ 0.0741, -0.1486, -0.7822, ..., 1.6495, 1.1358, 0.7933],
[-0.2513, -0.0972, -0.6965, ..., 1.6495, 1.1872, 0.7933],
...,
[-0.6452, -1.3815, -0.9192, ..., -1.9124, -1.6727, -1.7583],
[-1.1932, -0.7822, -1.3644, ..., -1.8782, -1.6727, -1.8097],
[-1.5699, -1.4158, -0.6452, ..., -1.7925, -1.7069, -1.8268]],
[[ 0.0826, -0.7927, -0.0574, ..., 0.8179, 0.2752, -0.0399],
[ 0.1176, -0.1099, -0.7052, ..., 0.8529, 0.3277, 0.0476],
[-0.2325, -0.0399, -0.6176, ..., 0.8529, 0.3803, 0.0476],
...,
[-0.8627, -1.5105, -0.9678, ..., -1.7206, -1.5280, -1.6155],
[-1.4405, -0.9503, -1.4755, ..., -1.6506, -1.4755, -1.6155],
[-1.8256, -1.5980, -0.7402, ..., -1.5280, -1.5105, -1.6331]],
[[ 1.0191, 0.1128, 0.8622, ..., 0.9842, 0.3916, 0.1302],
[ 1.0365, 0.8099, 0.2173, ..., 1.0191, 0.4614, 0.1999],
[ 0.6531, 0.8274, 0.2871, ..., 1.0191, 0.5136, 0.1999],
...,
[-0.5321, -1.1596, -0.6541, ..., -1.2467, -1.0376, -1.1247],
[-1.0550, -0.5495, -1.0724, ..., -1.1944, -0.9678, -1.1421],
[-1.4384, -1.1944, -0.3055, ..., -1.0898, -1.0027, -1.1247]]]), tensor([[[-0.3369, -0.2684, -0.1486, ..., 2.0434, 2.1290, 2.0605],
[-0.3883, -0.3027, -0.1657, ..., 2.0605, 2.1462, 2.0605],
[-0.3883, -0.2684, -0.1486, ..., 2.0605, 2.1462, 2.0263],
...,
[-1.1075, -1.0219, -0.2684, ..., 0.4166, 0.3652, 0.2111],
[-0.5596, -0.9020, -1.1760, ..., 0.3481, 0.3823, 0.0227],
[-1.3815, -0.5938, -1.0562, ..., 0.3138, 0.3652, -0.3712]],
[[-0.3550, -0.2850, -0.1625, ..., 1.7458, 1.7458, 1.6057],
[-0.4076, -0.3200, -0.1800, ..., 1.7983, 1.7808, 1.6232],
[-0.4076, -0.2850, -0.1625, ..., 1.8333, 1.7983, 1.6232],
...,
[-1.5805, -1.5280, -0.7577, ..., 0.0476, 0.0651, -0.0049],
[-1.0203, -1.4055, -1.6856, ..., 0.0126, 0.1352, -0.1450],
[-1.7556, -0.9853, -1.4755, ..., -0.0224, 0.1176, -0.5476]],
[[ 0.5136, 0.6182, 0.7751, ..., 2.0474, 2.0474, 1.8905],
[ 0.4614, 0.5834, 0.7576, ..., 2.1171, 2.0997, 1.9428],
[ 0.4614, 0.6182, 0.7751, ..., 2.1868, 2.1520, 1.9603],
...,
[-1.2293, -1.1944, -0.4624, ..., 0.3568, 0.3916, 0.3742],
[-0.6715, -1.0724, -1.3861, ..., 0.3219, 0.4091, 0.1825],
[-1.4384, -0.6890, -1.2119, ..., 0.2871, 0.3916, -0.2184]]]), tensor([[[-1.4329, -0.4397, 0.8276, ..., -0.6281, -0.6281, -0.6623],
[-0.8335, 0.5022, 1.4783, ..., -0.5938, -0.5767, -0.5938],
[ 0.0569, 1.1700, 1.5468, ..., -0.5767, -0.5424, -0.5253],
...,
[-2.1008, -2.1008, -2.0665, ..., -1.9809, -1.9295, -1.9467],
[-2.1008, -2.1179, -2.0665, ..., -1.9980, -1.9980, -2.0494],
[-2.0837, -2.1179, -2.0665, ..., -2.0665, -2.0665, -1.9638]],
[[-1.8431, -1.0028, 0.1702, ..., -0.6176, -0.5826, -0.6176],
[-1.3179, -0.0924, 0.7654, ..., -0.5826, -0.5301, -0.5476],
[-0.4951, 0.5203, 0.8179, ..., -0.5301, -0.4601, -0.4426],
...,
[-2.0182, -2.0182, -2.0007, ..., -2.0357, -2.0182, -2.0357],
[-2.0182, -2.0357, -2.0182, ..., -2.0007, -2.0182, -2.0357],
[-2.0007, -2.0357, -2.0182, ..., -2.0182, -2.0357, -2.0007]],
[[-1.5779, -0.7587, 0.3916, ..., 0.1999, 0.2522, 0.2522],
[-1.0724, 0.1651, 1.0017, ..., 0.2696, 0.3393, 0.3219],
[-0.2532, 0.7751, 1.0191, ..., 0.3045, 0.4091, 0.4614],
...,
[-1.8044, -1.8044, -1.8044, ..., -1.8044, -1.7696, -1.7870],
[-1.7870, -1.8044, -1.7696, ..., -1.6999, -1.7173, -1.7696],
[-1.7696, -1.7696, -1.7347, ..., -1.6999, -1.7347, -1.6476]]])]
У меня есть 16 изображений формы 3x112x112, когда я проверяю форму набора изображений с помощью этого кода;
print(np.array(images_set, dtype='object').shape)
Я получаю;
(16,)
а затем я проверяю форму первого индекса набора изображений, поскольку это изображение 16, а затем использую этот код;
print(np.array(images_set[0]).shape)
Я получаю, что каждое 16 изображений имеет такую форму
(3, 112, 112)
как я могу сделать это в такой форме`(16, 3, 112, 112)?
Комментарии:
1. Почему бы просто не сделать
np.array(images_set)это ?
Ответ №1:
Более общий формат:
import tensorflow as tf
import numpy as np
#Let's make a prototype of one image using ones (just to reproduce the problem without original data...)
one_liketensor=np.ones((3,112,112))
#Then let's notice the same can be seen in tensor-format as follows:
one_liketensor_as_tensor=tf.constant(one_liketensor)
#Let's define length of the list...where the tensors are...
length_of_list=16
#And then let's make an array of the target shape...
multi_array=np.ones((length_of_list,one_liketensor.shape[0],one_liketensor.shape[1],one_liketensor.shape[2]))
for i in range(length_of_list):
#For clarificatlin let's multiply each distict "image" with the number i to easily undestand the structure of the result...
multi_array[i,:]=i*one_liketensor
#...but naturally the "one_liketensor" is something special data ... thus there is need to take this information directly from this source
#And next let's print the result
print(multi_array)
#And let's transform that to tensor-format
multi_array_as_tensor=tf.constant(multi_array)
#And ... tadaa ... you have the material in the preferred format:
print("Shape of the result is: ",multi_array_as_tensor.shape)
…где «входная информация» — это длина списка и форма (и источник) тензоров; 
Комментарии:
1. у вас есть возможность проверить приведенную выше ссылку?
2. ДА. Ваша цель-просто объединить несколько изображений в «обычный» тензор? В случае, если я предложу вам следовать простой процедуре, показанной в ответе III
Ответ №2:
Применение трюков, показанных в приведенном ниже коде, вполне вероятно, решит вашу проблему:
import tensorflow as tf
import numpy as np
#Let's make a prototype of one image using ones (just to reproduce the problem without original data...)
one_image=np.ones((3,112,112))
#Then let's notice the same can be seen in tensor-format as follows:
one_image_as_tensor=tf.constant(one_image)
#And then let's make an array of the target shape...
multi_array=np.ones((16,3,112,112))
for i in range(16):
#For clarificatlin let's multiply each distict "image" with the number i to easily undestand the structure of the result...
multi_array[i,:]=i*one_image
#And next let's print the result
print(multi_array)
#And let's transform that to tensor-format
multi_array_as_tensor=tf.constant(multi_array)
#And ... tadaa ... you have the material in the preferred format:
print("Shape of the result is: ",multi_array_as_tensor.shape)
Комментарии:
1. вы сделали одни и те же изображения, в то время как в моем случае есть 16 разных изображений, все они имеют разное значение пикселя.
2. Используйте в строке 14 следующее: multi_array[i,:]=набор изображений[i]
3. Я сохранил аналогичный новый вопрос, проверьте его, я сохранил там его решения. Я столкнулся с одной проблемой в этом, проверьте ее и ответьте мне там.
4. datascience.stackexchange.com/questions/99880/…
5. Хммм…интересно! Нашли ли вы теперь достаточно хорошо «не закодированное» решение для вашей проблемы?
Ответ №3:
Ответ III — в случае, если цель состоит в том, чтобы использовать изображения в качестве входных данных для CNN
import numpy as np
import tensorflow as tf
#One image...whatever the shape is...
one_image=np.ones((3,112,112))
#The list just as an example...length is arbitrary...
list_of_images=(one_image,one_image,one_image,one_image,one_image,one_image)
#Make the tensorformat directly...
tensorimuoto2=tf.constant(list_of_images)
#And show the shape of the result...which is ready to input to CNN...
print(tensorimuoto2.shape)