TensorFlow o‘z o‘z o‘z o‘z o‘z o‘z o‘z

Intsiz bilan bazlar Forma o‘z Indekslar Manipulativ formlar O‘z o‘z o‘z Televiziya tf.convert_to_tensor o‘z o‘z Tenzor o‘z. String Tenzorlar Tenzor bilan Tensorlar multidimensional arraylar, unidimensional adlarlar (tendorlar). O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z . dtype dtypes tf.dtypes Siz o‘z o‘z bildi O‘zlar o‘zlar o‘zlar o‘zlar o‘zlar. . Numida np.arrays Bütün tensorlar Python numara və stringlarni o‘zingizdir: siz heç vaxt tensorlarni update qoysan, o‘z o‘zingizdir. import tensorflow as tf import numpy as np 2024-08-15 03:05:18.327501: 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 03:05:18.348450: 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 03:05:18.354825: 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 bazlar İlk o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Bu o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. # This will be an int32 tensor by default; see "dtypes" below. rank_0_tensor = tf.constant(4) print(rank_0_tensor) tf.Tensor(4, shape=(), dtype=int32) WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1723691120.932442 176945 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 00:00:1723691120.936343 176945 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 00:00:1723691120.940040 176945 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 00:00:1723691120.943264 176945 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 00:00:1723691120.954872 176945 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 00:00:1723691120.958376 176945 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 00:00:1723691120.961894 176945 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 00:00:1723691120.964843 176945 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 00:00:1723691120.967730 176945 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 00:00:1723691120.971300 176945 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 00:00:1723691120.974711 176945 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 00:00:1723691120.977717 176945 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 00:00:1723691122.208679 176945 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 00:00:1723691122.210786 176945 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 00:00:1723691122.212791 176945 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 00:00:1723691122.214776 176945 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 00:00:1723691122.216798 176945 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 00:00:1723691122.218734 176945 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 00:00:1723691122.220650 176945 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 00:00:1723691122.222554 176945 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 00:00:1723691122.224486 176945 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 00:00:1723691122.226429 176945 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 00:00:1723691122.228329 176945 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 00:00:1723691122.230251 176945 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 00:00:1723691122.269036 176945 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 00:00:1723691122.271069 176945 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 00:00:1723691122.273006 176945 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 00:00:1723691122.274956 176945 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 00:00:1723691122.276917 176945 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 00:00:1723691122.278854 176945 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 00:00:1723691122.280754 176945 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 00:00:1723691122.282664 176945 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 00:00:1723691122.284613 176945 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 00:00:1723691122.287058 176945 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 00:00:1723691122.289508 176945 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 00:00:1723691122.291891 176945 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 Bir "vector" o'z "rank-1" tensor o'z bir list o'z vallar. # Let's make this a float tensor. rank_1_tensor = tf.constant([2.0, 3.0, 4.0]) print(rank_1_tensor) tf.Tensor([2. 3. 4.], shape=(3,), dtype=float32) A "matrix" or "rank-2" tensor has two axes: # If you want to be specific, you can set the dtype (see below) at creation time rank_2_tensor = tf.constant([[1, 2], [3, 4], [5, 6]], dtype=tf.float16) print(rank_2_tensor) tf.Tensor( [[1. 2.] [3. 4.] [5. 6.]], shape=(3, 2), dtype=float16) A scalar, shape: [] A vector, shape: [3] A matrix, shape: [3, 2] Tensors may have more axes; here is a tensor with three axes: # There can be an arbitrary number of # axes (sometimes called "dimensions") rank_3_tensor = tf.constant([ [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], [[10, 11, 12, 13, 14], [15, 16, 17, 18, 19]], [[20, 21, 22, 23, 24], [25, 26, 27, 28, 29]],]) print(rank_3_tensor) tf.Tensor( [[[ 0 1 2 3 4] [ 5 6 7 8 9]] [[10 11 12 13 14] [15 16 17 18 19]] [[20 21 22 23 24] [25 26 27 28 29]]], shape=(3, 2, 5), dtype=int32) Sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga. A 3-axis tensor, shape: [3, 2, 5] Bir tensorni NumPy arrayni qaytarib o‘z. O‘z Metoda : np.array tensor.numpy np.array(rank_2_tensor) array([[1., 2.], [3., 4.], [5., 6.]], dtype=float16) rank_2_tensor.numpy() array([[1., 2.], [3., 4.], [5., 6.]], dtype=float16) Tensorlar o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: Kompleks sayı Stringlar Basi O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z tf.Tensor Tendonlar o‘z Spare Tenzorlar Siz tensorlar, addition, element-like multiplication, matrix multiplication o‘zingizga qilmadi. a = tf.constant([[1, 2], [3, 4]]) b = tf.constant([[1, 1], [1, 1]]) # Could have also said `tf.ones([2,2], dtype=tf.int32)` print(tf.add(a, b), "\n") print(tf.multiply(a, b), "\n") print(tf.matmul(a, b), "\n") tf.Tensor( [[2 3] [4 5]], shape=(2, 2), dtype=int32) tf.Tensor( [[1 2] [3 4]], shape=(2, 2), dtype=int32) tf.Tensor( [[3 3] [7 7]], shape=(2, 2), dtype=int32) print(a + b, "\n") # element-wise addition print(a * b, "\n") # element-wise multiplication print(a @ b, "\n") # matrix multiplication tf.Tensor( [[2 3] [4 5]], shape=(2, 2), dtype=int32) tf.Tensor( [[1 2] [3 4]], shape=(2, 2), dtype=int32) tf.Tensor( [[3 3] [7 7]], shape=(2, 2), dtype=int32) Tensorlar o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. c = tf.constant([[4.0, 5.0], [10.0, 1.0]]) # Find the largest value print(tf.reduce_max(c)) # Find the index of the largest value print(tf.math.argmax(c)) # Compute the softmax print(tf.nn.softmax(c)) tf.Tensor(10.0, shape=(), dtype=float32) tf.Tensor([1 0], shape=(2,), dtype=int64) tf.Tensor( [[2.6894143e-01 7.3105854e-01] [9.9987662e-01 1.2339458e-04]], shape=(2, 2), dtype=float32) TensorFlow funksiya o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. TensorFlow funksiya o‘z o‘z o‘z o‘z o‘z As input, funksiya o‘z o‘z o‘z o‘z o‘z o‘z convertible. O‘z O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Note: Tensor Tensor tf.convert_to_tensor tf.convert_to_tensor([1,2,3]) tf.reduce_max([1,2,3]) tf.reduce_max(np.array([1,2,3])) Forma o‘z O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: Form: Tenzordan hər bir o‘zinin uzunluğudur. Bir skalar 0, bir vektor 1 o‘z, matris 2 o‘z. Axis or Dimension: Tenzorni o‘z o‘zadi. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Siz "tensor iki dimensiya" referentiga baxmaysa, rank-2 tensor o‘z o‘z o‘z o‘z 2D space. Note: Tenorlar və Bu objektid o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: tf.TensorShape rank_4_tensor = tf.zeros([3, 2, 4, 5]) A rank-4 tensor, shape: [3, 2, 4, 5] print("Type of every element:", rank_4_tensor.dtype) print("Number of axes:", rank_4_tensor.ndim) print("Shape of tensor:", rank_4_tensor.shape) print("Elements along axis 0 of tensor:", rank_4_tensor.shape[0]) print("Elements along the last axis of tensor:", rank_4_tensor.shape[-1]) print("Total number of elements (3*2*4*5): ", tf.size(rank_4_tensor).numpy()) Type of every element: Number of axes: 4 Shape of tensor: (3, 2, 4, 5) Elements along axis 0 of tensor: 3 Elements along the last axis of tensor: 5 Total number of elements (3*2*4*5): 120 Men o‘z o‘z o‘z O‘z Atributs qaytarmadi O‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z O‘z Bu diferensiya subtildir, amma o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Tensor.ndim Tensor.shape Tensor Tensor tf.rank tf.shape tf.rank(rank_4_tensor) tf.shape(rank_4_tensor) O‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar, o‘zlar. Typical axis order Indekslar Indikatorlar o‘z TensorFlow standart Python indexing rregulllarni qilmadi. NumPy indexing o‘z o‘z o‘z. Bir list o‘z o‘z o‘z o‘z o‘z o‘z Python 0 0 0 0 0 0 0 0 0 0 Negative index bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan Kolonlar, :, o‘z o‘zingizdir: start:stop:step rank_1_tensor = tf.constant([0, 1, 1, 2, 3, 5, 8, 13, 21, 34]) print(rank_1_tensor.numpy()) [ 0 1 1 2 3 5 8 13 21 34] Indeksing scalar o‘z o‘z o‘z: print("First:", rank_1_tensor[0].numpy()) print("Second:", rank_1_tensor[1].numpy()) print("Last:", rank_1_tensor[-1].numpy()) First: 0 Second: 1 Last: 34 Indeks o‘z O‘z o‘z o‘z o‘z o‘z: : print("Everything:", rank_1_tensor[:].numpy()) print("Before 4:", rank_1_tensor[:4].numpy()) print("From 4 to the end:", rank_1_tensor[4:].numpy()) print("From 2, before 7:", rank_1_tensor[2:7].numpy()) print("Every other item:", rank_1_tensor[::2].numpy()) print("Reversed:", rank_1_tensor[::-1].numpy()) Everything: [ 0 1 1 2 3 5 8 13 21 34] Before 4: [0 1 1 2] From 4 to the end: [ 3 5 8 13 21 34] From 2, before 7: [1 2 3 5 8] Every other item: [ 0 1 3 8 21] Reversed: [34 21 13 8 5 3 2 1 1 0] Multi-axis indexing qoladi Tensorlar bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. print(rank_2_tensor.numpy()) [[1. 2.] [3. 4.] [5. 6.]] Bu indeksni qoladi, o‘z o‘z o‘z qoladi. # Pull out a single value from a 2-rank tensor print(rank_2_tensor[1, 1].numpy()) You can index using any combination of integers and slices: # Get row and column tensors print("Second row:", rank_2_tensor[1, :].numpy()) print("Second column:", rank_2_tensor[:, 1].numpy()) print("Last row:", rank_2_tensor[-1, :].numpy()) print("First item in last column:", rank_2_tensor[0, -1].numpy()) print("Skip the first row:") print(rank_2_tensor[1:, :].numpy(), "\n") Second row: [3. 4.] Second column: [2. 4. 6.] Last row: [5. 6.] First item in last column: 2.0 Skip the first row: [[3. 4.] [5. 6.]] Here is an example with a 3-axis tensor: print(rank_3_tensor[:, :, 4]) tf.Tensor( [[ 4 9] [14 19] [24 29]], shape=(3, 2), dtype=int32) Selecting the last feature across all locations in each example in the batch Read o‘z O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Tenzor Slicing Guide qaytaradi Manipulativ formlar Tenzorni qaytarmaq çox faydalag. # Shape returns a `TensorShape` object that shows the size along each axis x = tf.constant([[1], [2], [3]]) print(x.shape) (3, 1) # You can convert this object into a Python list, too print(x.shape.as_list()) [3, 1] Siz o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. tf.reshape # You can reshape a tensor to a new shape. # Note that you're passing in a list reshaped = tf.reshape(x, [1, 3]) print(x.shape) print(reshaped.shape) (3, 1) (1, 3) TensorFlow, C-style "row-major" memori ordinansiya kulladi, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. print(rank_3_tensor) tf.Tensor( [[[ 0 1 2 3 4] [ 5 6 7 8 9]] [[10 11 12 13 14] [15 16 17 18 19]] [[20 21 22 23 24] [25 26 27 28 29]]], shape=(3, 2, 5), dtype=int32) O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. # A `-1` passed in the `shape` argument says "Whatever fits". print(tf.reshape(rank_3_tensor, [-1])) tf.Tensor( [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29], shape=(30,), dtype=int32) O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z S. o‘z. tf.reshape 1 Bu 3x2x5 tensorni, 3x2x5 ya 3x2x5 o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: print(tf.reshape(rank_3_tensor, [3*2, 5]), "\n") print(tf.reshape(rank_3_tensor, [3, -1])) tf.Tensor( [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14] [15 16 17 18 19] [20 21 22 23 24] [25 26 27 28 29]], shape=(6, 5), dtype=int32) tf.Tensor( [[ 0 1 2 3 4 5 6 7 8 9] [10 11 12 13 14 15 16 17 18 19] [20 21 22 23 24 25 26 27 28 29]], shape=(3, 10), dtype=int32) Some good reshapes. Reshaping va "tadi" qonaq qonaq bütün elementli yeni qonaqlar, amma siz axlarni qonaq qoysansa heç bir faydalaga qoysan. Axlar o‘zingizdir O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z Bu o‘z. tf.reshape tf.transpose # Bad examples: don't do this # You can't reorder axes with reshape. print(tf.reshape(rank_3_tensor, [2, 3, 5]), "\n") # This is a mess print(tf.reshape(rank_3_tensor, [5, 6]), "\n") # This doesn't work at all try: tf.reshape(rank_3_tensor, [7, -1]) except Exception as e: print(f"{type(e).__name__}: {e}") tf.Tensor( [[[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14]] [[15 16 17 18 19] [20 21 22 23 24] [25 26 27 28 29]]], shape=(2, 3, 5), dtype=int32) tf.Tensor( [[ 0 1 2 3 4 5] [ 6 7 8 9 10 11] [12 13 14 15 16 17] [18 19 20 21 22 23] [24 25 26 27 28 29]], shape=(5, 6), dtype=int32) InvalidArgumentError: { {function_node __wrapped__Reshape_device_/job:localhost/replica:0/task:0/device:GPU:0} } Input to reshape is a tensor with 30 values, but the requested shape requires a multiple of 7 [Op:Reshape] Some bad reshapes. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. (O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z). (O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z). None None T.RaggedTensor o‘z, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: O‘z. funksiya O‘z o‘z qilmadi. Daha çox Dadiq Dadiq O‘z o‘z o‘z Database o‘z o‘z o‘z Property o‘z. tf.Tensor Tensor.dtype O‘z o‘z o‘z o‘z Python o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. tf.Tensor TensorFlow sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga Python o‘z o‘z o‘z o‘z o‘z o‘z TensorFlow o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. tf.int32 tf.float32 Siz o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. the_f64_tensor = tf.constant([2.2, 3.3, 4.4], dtype=tf.float64) the_f16_tensor = tf.cast(the_f64_tensor, dtype=tf.float16) # Now, cast to an uint8 and lose the decimal precision the_u8_tensor = tf.cast(the_f16_tensor, dtype=tf.uint8) print(the_u8_tensor) tf.Tensor([2 3 4], shape=(3,), dtype=uint8) Televiziya O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. NumPy’da bilan bilan o‘z U o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. x = tf.constant([1, 2, 3]) y = tf.constant(2) z = tf.constant([2, 2, 2]) # All of these are the same computation print(tf.multiply(x, 2)) print(x * y) print(x * z) tf.Tensor([2 4 6], shape=(3,), dtype=int32) tf.Tensor([2 4 6], shape=(3,), dtype=int32) tf.Tensor([2 4 6], shape=(3,), dtype=int32) 1 o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. 3x1 matrisni matrisni 1x4 matrisni multiplikattiradi, 3x4 matrisni qaytaradi. 1 matrisni 3x4 matrisni. . [4] # These are the same computations x = tf.reshape(x,[3,1]) y = tf.range(1, 5) print(x, "\n") print(y, "\n") print(tf.multiply(x, y)) tf.Tensor( [[1] [2] [3]], shape=(3, 1), dtype=int32) tf.Tensor([1 2 3 4], shape=(4,), dtype=int32) tf.Tensor( [[ 1 2 3 4] [ 2 4 6 8] [ 3 6 9 12]], shape=(3, 4), dtype=int32) A broadcasted add: a times a gives a [3, 1] [1, 4] [3,4] O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: x_stretch = tf.constant([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]) y_stretch = tf.constant([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]) print(x_stretch * y_stretch) # Again, operator overloading tf.Tensor( [[ 1 2 3 4] [ 2 4 6 8] [ 3 6 9 12]], shape=(3, 4), dtype=int32) O‘z, broadcasting o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Siz o‘z televiziyalarni o‘z yolladi. . tf.broadcast_to print(tf.broadcast_to(tf.constant([1, 2, 3]), [3, 3])) tf.Tensor( [[1 2 3] [1 2 3] [1 2 3]], shape=(3, 3), dtype=int32) Matematikan o‘z, o‘z o‘z o‘z, o‘z o‘z o‘z. Bu o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. broadcast_to Bu o‘z daha komplikas o‘z o‘z. Jake VanderPlas o‘z o‘z o‘z. Men o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Bu seksiya Python Data Science Manual o‘zingizdir. tf.convert_to_tensor o‘z o‘z O‘z o‘z, o‘z o‘z O‘z Argument o‘z klasa Men, siz sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga. tf.matmul tf.reshape tf.Tensor O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z Bu o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z, Python o‘z o‘z o‘z O‘z o‘z automatikdir. convert_to_tensor ndarray TensorShape tf.Variable O‘z Sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga. tf.register_tensor_conversion_function Tenzor o‘z. Tenzor o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z Rainbow data o‘z. tf.ragged.RaggedTensor Bu, normal tensorni qaytarga qoysan: A , shape: tf.RaggedTensor [4, None] ragged_list = [ [0, 1, 2, 3], [4, 5], [6, 7, 8], [9]] try: tensor = tf.constant(ragged_list) except Exception as e: print(f"{type(e).__name__}: {e}") ValueError: Can't convert non-rectangular Python sequence to Tensor. Bu o‘z o‘z o‘z O‘z : tf.RaggedTensor tf.ragged.constant ragged_tensor = tf.ragged.constant(ragged_list) print(ragged_tensor) Forma o‘z Bu o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: tf.RaggedTensor print(ragged_tensor.shape) (4, None) String tenorlar O‘z Bu, o‘z sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga sizga siz tf.string dtype O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Bu funksiyalar manipulativdir. tf.strings O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: # Tensors can be strings, too here is a scalar string. scalar_string_tensor = tf.constant("Gray wolf") print(scalar_string_tensor) tf.Tensor(b'Gray wolf', shape=(), dtype=string) Vector o‘z o‘z o‘z o‘z: A vector of strings, shape: [3,] # If you have three string tensors of different lengths, this is OK. tensor_of_strings = tf.constant(["Gray wolf", "Quick brown fox", "Lazy dog"]) # Note that the shape is (3,). The string length is not included. print(tensor_of_strings) tf.Tensor([b'Gray wolf' b'Quick brown fox' b'Lazy dog'], shape=(3,), dtype=string) O‘z o‘z o‘z o‘z o‘z o‘z o‘z Prefix o‘z o‘z. Unicode stringni o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z TensorFlow-da Unicode texti qilmadi. b tf.string Unicode tutorialladi Unicode o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. tf.constant("🥳👍") Bazilima o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z . tf.strings tf.strings.split # You can use split to split a string into a set of tensors print(tf.strings.split(scalar_string_tensor, sep=" ")) tf.Tensor([b'Gray' b'wolf'], shape=(2,), dtype=string) # ...but it turns into a `RaggedTensor` if you split up a tensor of strings, # as each string might be split into a different number of parts. print(tf.strings.split(tensor_of_strings)) Three strings split, shape: [3, None] O‘z : tf.strings.to_number text = tf.constant("1 10 100") print(tf.strings.to_number(tf.strings.split(text, " "))) tf.Tensor([ 1. 10. 100.], shape=(3,), dtype=float32) Sizga sizga sizga sizga Siz o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. tf.cast byte_strings = tf.strings.bytes_split(tf.constant("Duck")) byte_ints = tf.io.decode_raw(tf.constant("Duck"), tf.uint8) print("Byte strings:", byte_strings) print("Bytes:", byte_ints) Byte strings: tf.Tensor([b'D' b'u' b'c' b'k'], shape=(4,), dtype=string) Bytes: tf.Tensor([ 68 117 99 107], shape=(4,), dtype=uint8) # Or split it up as unicode and then decode it unicode_bytes = tf.constant("アヒル 🦆") unicode_char_bytes = tf.strings.unicode_split(unicode_bytes, "UTF-8") unicode_values = tf.strings.unicode_decode(unicode_bytes, "UTF-8") print("\nUnicode bytes:", unicode_bytes) print("\nUnicode chars:", unicode_char_bytes) print("\nUnicode values:", unicode_values) Unicode bytes: tf.Tensor(b'\xe3\x82\xa2\xe3\x83\x92\xe3\x83\xab \xf0\x9f\xa6\x86', shape=(), dtype=string) Unicode chars: tf.Tensor([b'\xe3\x82\xa2' b'\xe3\x83\x92' b'\xe3\x83\xab' b' ' b'\xf0\x9f\xa6\x86'], shape=(5,), dtype=string) Unicode values: tf.Tensor([ 12450 12498 12523 32 129414], shape=(5,), dtype=int32) O‘z U o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Modul o‘z qilmadi data to and from bytes, o‘z decoding images and parsing csv. tf.string tf.io Spare Tenzorlar O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. tf.sparse.SparseTensor A , shape: tf.SparseTensor [3, 4] # Sparse tensors store values by index in a memory-efficient manner sparse_tensor = tf.sparse.SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4]) print(sparse_tensor, "\n") # You can convert sparse tensors to dense print(tf.sparse.to_dense(sparse_tensor)) SparseTensor(indices=tf.Tensor( [[0 0] [1 2]], shape=(2, 2), dtype=int64), values=tf.Tensor([1 2], shape=(2,), dtype=int32), dense_shape=tf.Tensor([3 4], shape=(2,), dtype=int64)) tf.Tensor( [[1 0 0 0] [0 0 2 0] [0 0 0 0]], shape=(3, 4), dtype=int32) TensorFlow website-da o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Code samples o‘z o‘z. Originally published on the , TensorFlow internet sayt CC BY 4.0. Apache 2.0 License. TensorFlow internet sayt