Tensor Slicing மற்றும் Data Insertion மூலம் TensorFlow மூலம் எளிதானது

முகப்பு Content Overview அறிமுகம் Tensor Slices விளைவுகள் Tensors இல் data சேர்க்க மேலும் Read and Resources ஒரு முதியவன் பாதாளங்களைத் தாண்டும் தன் மந்திரக்கோலால் சாய்த்தபடியிருக்கிறான் நாட்சத்திரங்களை. .............................................................................................................................................................................. இது எத்தனையாவது [...] ஆங்கிலத்தில் இதை Single Orgasm, Multiple Orgasm என்றும் கூறுகிறார்கள்.அதற்காக science fiction நாவல்கள் எல்லாம் science ஆகாது.Fictionஐ ஓரளவு இரசிக்கலாம். இந்த வழக்கில், TensorFlow API ஐ எப்படி பயன்படுத்துவது என்பதை நீங்கள் அறியலாம்: ஒரு tensor இல் இருந்து slices extract ஒரு Tensor இல் சில Indices இல் Data Insert இந்த மாதிரி think different type விளம்பரங்கள் தான் add industriesla இப்போ Hot... மற்றும் இந்த வழிகாட்டுதலால் முதலீடு செய்வதற்கு முன் டென்னிஸ் TensorFlow NumPy விவரங்கள் அமைப்பு import tensorflow as tf import numpy as np 2024-08-15 02:59:22.387959: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-08-15 02:59:22.409014: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-08-15 02:59:22.415391: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Tensor Slices விளைவுகள் NumPy-like tensor slicing பயன்படுத்தி . tf.slice t1 = tf.constant([0, 1, 2, 3, 4, 5, 6, 7]) print(tf.slice(t1, begin=[1], size=[3])) tf.Tensor([1 2 3], shape=(3,), dtype=int32) WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1723690765.029742 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.033507 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.037154 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.040869 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.052765 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.056359 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.059719 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.063138 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.066532 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.070040 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.073650 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690765.077235 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.300773 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.302797 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.304766 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.306851 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.308843 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.310723 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.312589 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.314574 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.316423 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.318291 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.320168 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.322150 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.360968 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.362946 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.364849 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.366867 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.368710 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.370591 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.372447 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.374435 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.376302 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.378677 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.380966 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690766.383365 169903 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 ஆங்கிலத்தில் இதை Single Orgasm, Multiple Orgasm என்றும் கூறுகிறார்கள். print(t1[1:4]) tf.Tensor([1 2 3], shape=(3,), dtype=int32) print(t1[-3:]) tf.Tensor([5 6 7], shape=(3,), dtype=int32) 2-Dimensional Tensors, நீங்கள் ஏதாவது பயன்படுத்த முடியும்: t2 = tf.constant([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19]]) print(t2[:-1, 1:3]) tf.Tensor( [[ 1 2] [ 6 7] [11 12]], shape=(3, 2), dtype=int32) நீங்கள் பயன்படுத்த முடியும் மேலும் Higher Dimensional Tensors செய்திகள் tf.slice t3 = tf.constant([[[1, 3, 5, 7], [9, 11, 13, 15]], [[17, 19, 21, 23], [25, 27, 29, 31]] ]) print(tf.slice(t3, begin=[1, 1, 0], size=[1, 1, 2])) tf.Tensor([[[25 27]]], shape=(1, 1, 2), dtype=int32) நீங்களும் பயன்படுத்த முடியும் இந்த மாதிரி think different type விளம்பரங்கள் தான் add industriesla இப்போ Hot... tf.strided_slice பயன்பாடு ஆங்கிலத்தில் இதை Single axis of a tensor என்று சொல்வார்கள். tf.gather print(tf.gather(t1, indices=[0, 3, 6])) # This is similar to doing t1[::3] tf.Tensor([0 3 6], shape=(3,), dtype=int32) ஆங்கிலத்தில் இதை Single Orgasm, Single Orgasm என்றும் கூறுகிறார்கள். tf.gather alphabet = tf.constant(list('abcdefghijklmnopqrstuvwxyz')) print(tf.gather(alphabet, indices=[2, 0, 19, 18])) tf.Tensor([b'c' b'a' b't' b's'], shape=(4,), dtype=string) பல்வேறு அணுகுமுறைகளிலிருந்து பல்வேறு அணுகுமுறைகளை வெளியிடுவதற்காக, இந்த மாதிரி think different type விளம்பரங்கள் தான் add industriesla இப்போ Hot... tf.gather_nd t4 = tf.constant([[0, 5], [1, 6], [2, 7], [3, 8], [4, 9]]) print(tf.gather_nd(t4, indices=[[2], [3], [0]])) tf.Tensor( [[2 7] [3 8] [0 5]], shape=(3, 2), dtype=int32) t5 = np.reshape(np.arange(18), [2, 3, 3]) print(tf.gather_nd(t5, indices=[[0, 0, 0], [1, 2, 1]])) tf.Tensor([ 0 16], shape=(2,), dtype=int64) # Return a list of two matrices print(tf.gather_nd(t5, indices=[[[0, 0], [0, 2]], [[1, 0], [1, 2]]])) tf.Tensor( [[[ 0 1 2] [ 6 7 8]] [[ 9 10 11] [15 16 17]]], shape=(2, 2, 3), dtype=int64) # Return one matrix print(tf.gather_nd(t5, indices=[[0, 0], [0, 2], [1, 0], [1, 2]])) tf.Tensor( [[ 0 1 2] [ 6 7 8] [ 9 10 11] [15 16 17]], shape=(4, 3), dtype=int64) Tensors இல் data சேர்க்க பயன்பாடு இந்த மாதிரி think different type விளம்பரங்கள் தான் add industriesla இப்போ Hot... tf.scatter_nd t6 = tf.constant([10]) indices = tf.constant([[1], [3], [5], [7], [9]]) data = tf.constant([2, 4, 6, 8, 10]) print(tf.scatter_nd(indices=indices, updates=data, shape=t6)) tf.Tensor([ 0 2 0 4 0 6 0 8 0 10], shape=(10,), dtype=int32) போன்ற வழிமுறைகள் இந்த மாதிரி think different type விளம்பரங்கள் தான் add industriesla இப்போ Hot... மற்றும் SPARSE TENSOR OPS இன் செயல்பாடுகள் tf.scatter_nd tf.gather_nd tf.scatter_nd நீங்கள் இந்த இரண்டு வழிமுறைகளை பயன்படுத்தி ஒரு வித்தியாசமான tensor உருவாக்க ஒரு உதாரணம் நினைவில். # Gather values from one tensor by specifying indices new_indices = tf.constant([[0, 2], [2, 1], [3, 3]]) t7 = tf.gather_nd(t2, indices=new_indices) # Add these values into a new tensor t8 = tf.scatter_nd(indices=new_indices, updates=t7, shape=tf.constant([4, 5])) print(t8) tf.Tensor( [[ 0 0 2 0 0] [ 0 0 0 0 0] [ 0 11 0 0 0] [ 0 0 0 18 0]], shape=(4, 5), dtype=int32) இது போன்றவை: t9 = tf.SparseTensor(indices=[[0, 2], [2, 1], [3, 3]], values=[2, 11, 18], dense_shape=[4, 5]) print(t9) SparseTensor(indices=tf.Tensor( [[0 2] [2 1] [3 3]], shape=(3, 2), dtype=int64), values=tf.Tensor([ 2 11 18], shape=(3,), dtype=int32), dense_shape=tf.Tensor([4 5], shape=(2,), dtype=int64)) # Convert the sparse tensor into a dense tensor t10 = tf.sparse.to_dense(t9) print(t10) tf.Tensor( [[ 0 0 2 0 0] [ 0 0 0 0 0] [ 0 11 0 0 0] [ 0 0 0 18 0]], shape=(4, 5), dtype=int32) முன்பே ஏற்றப்பட்ட (preloaded) Multi-Turbo மற்றும் Ultra Game Mode ஆகியவை உள்ளன. . tf.tensor_scatter_nd_add t11 = tf.constant([[2, 7, 0], [9, 0, 1], [0, 3, 8]]) # Convert the tensor into a magic square by inserting numbers at appropriate indices t12 = tf.tensor_scatter_nd_add(t11, indices=[[0, 2], [1, 1], [2, 0]], updates=[6, 5, 4]) print(t12) tf.Tensor( [[2 7 6] [9 5 1] [4 3 8]], shape=(3, 3), dtype=int32) அதேபோல், பயன்பாடு முன்பே ஏற்றப்பட்ட (preloaded) Multi-Turbo மற்றும் Ultra Game Mode ஆகியவை உள்ளன. tf.tensor_scatter_nd_sub # Convert the tensor into an identity matrix t13 = tf.tensor_scatter_nd_sub(t11, indices=[[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [2, 1], [2, 2]], updates=[1, 7, 9, -1, 1, 3, 7]) print(t13) tf.Tensor( [[1 0 0] [0 1 0] [0 0 1]], shape=(3, 3), dtype=int32) பயன்பாடு Minimum values from one tensor to another (ஒரு tensor இருந்து மற்றொரு tensor வரை) tf.tensor_scatter_nd_min t14 = tf.constant([[-2, -7, 0], [-9, 0, 1], [0, -3, -8]]) t15 = tf.tensor_scatter_nd_min(t14, indices=[[0, 2], [1, 1], [2, 0]], updates=[-6, -5, -4]) print(t15) tf.Tensor( [[-2 -7 -6] [-9 -5 1] [-4 -3 -8]], shape=(3, 3), dtype=int32) அதேபோல், பயன்பாடு அளவுக்கு அதிகமான அளவுகளை ஒரு அணுகுமுறையிலிருந்து மற்றொரு அணுகுமுறையிடம் ஒப்பிடலாம். tf.tensor_scatter_nd_max t16 = tf.tensor_scatter_nd_max(t14, indices=[[0, 2], [1, 1], [2, 0]], updates=[6, 5, 4]) print(t16) tf.Tensor( [[-2 -7 6] [-9 5 1] [ 4 -3 -8]], shape=(3, 3), dtype=int32) மேலும் Read and Resources இந்த ஆவணத்தில், நீங்கள் TensorFlow உடன் கிடைக்கும் tensor slicing ops பயன்படுத்த எப்படி உங்கள் tensors உள்ள பொருட்களை மேம்படுத்த சிறந்த கட்டுப்பாடு செய்ய. tf.experimental.numpy.take_along_axis மற்றும் tf.experimental.numpy.take போன்ற TensorFlow NumPy இல் கிடைக்கும் இடுகையைப் பாருங்கள். மேலும் Tensor Guide மற்றும் Variable Guide ஐ பாருங்கள். முதலில் TensorFlow வலைத்தளத்தில் வெளியிடப்பட்ட, இந்த கட்டுரை ஒரு புதிய தலைப்பு கீழ் இங்கே காணப்படுகிறது மற்றும் CC BY 4.0 கீழ் உரிமம் உள்ளது. முதலில் TensorFlow வலைத்தளத்தில் வெளியிடப்பட்ட, இந்த கட்டுரை ஒரு புதிய தலைப்பு கீழ் இங்கே காணப்படுகிறது மற்றும் CC BY 4.0 கீழ் உரிமம் உள்ளது. TensorFlow இல் TensorFlow இல்