예제로 배우는 텐서 입문

two_dim_tensor = [
	[1, 2, 3],
	[4, 5, 6]
]

horizontal_tensor = [1, 2, 3] 
vertical_tensor = [ 
	[1],
	[2]
	[3]
]

three_dimensional_tensor = [
	[
		[1, 2, 3],
		[4, 5, 6]	
	],
	[
		[7, 8, 9],
		[10, 11, 12]
	]
]

ragged_3d_tensor = [
	[
		[1, 2, 3],     # 3 elements.
		[4, 5]         # 2 elements.
	],
	[
		[6, 7, 8, 9],  # 4 elements.
		[10, 11, 12]   # 3 elements. 
	]
]

# ragged_3d_tensor shape: (2, 2, *)

import numpy as np
import tensorflow as tf

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

>>> a + b
array([5, 7, 9])

>>> a - b
array([-3, -3, -3])

>>> a / b
array([0.25, 0.4 , 0.5 ])

>>> a * b
array([ 4, 10, 18])

>>> print(a.shape)
(3,)
>>> print(a.dtype)
int64

>>> e = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])
>>> e = e.reshape(-1)
>>> e
array([1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> tf.convert_to_tensor(e)
<tf.Tensor: shape=(9,), dtype=int64, numpy=array([1, 2, 3, 4, 5, 6, 7, 8, 9])>

>>> sparse_tensor = tf.sparse.SparseTensor(indices=[[0, 0], [1, 2]],
                                       values=[1, 2],
                                       dense_shape=[3, 4])

>>> print(sparse_tensor, "\n")
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))

>>> print(tf.sparse.to_dense(sparse_tensor))
tf.Tensor(
[[1 0 0 0]
 [0 0 2 0]
 [0 0 0 0]], shape=(3, 4), dtype=int32)

# !pip install wandb
import wandb
wandb.login()  # Make sure you have an account. This will prompt you for your API token.

run = wandb.init(project="My physics project", name="run_1")

# Code to run simulation here.
# Imagine periodically, a function here returns tensor output describing the physics simulation.
x, y, z = func1()  # Let's say your function returns position in 3 coordinates.   

# Every log is a time step in the line plot. 
# For instance, if you had a for loop that iterated 10 times, each time a log was made, the line plots on 
# W&B would have exactly 10 data points for an "x", "y", and a "z" graph!
run.log({
	"x": x,
	"y": y,
	"z": z
})

# Let's say you don't want separate line charts for each dimension. You can do a 3D point cloud instead!
# Below is a trivial example. Imagine you have an array "positions" of shape (n, 3) where n is the number of points 
# (one for each time step) and 3 is for the number of axes (x, y, and z).
positions = np.array([[x+i, y+i, z+i] for i in range(100)])

run.log({
	"point_scene": wandb.Object3D(positions)
})

run.finish()

run.log({
	"mean_loss": np.mean(train_losses),
	"sum_loss": np.sum(train_losses),
	"val_mean_loss": np.mean(val_losses),
	"val_sum_loss": np.sum(val_losses),
	"val_mean_dice_coeff": np.mean(dice_coeffs),
	"lr": lr
})