変数の概要

import tensorflow as tf

# Uncomment to see where your variables get placed (see below)
# tf.debugging.set_log_device_placement(True)

2022-12-14 22:50:41.438311: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 22:50:41.438412: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 22:50:41.438434: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

my_tensor = tf.constant([[1.0, 2.0], [3.0, 4.0]])
my_variable = tf.Variable(my_tensor)

# Variables can be all kinds of types, just like tensors
bool_variable = tf.Variable([False, False, False, True])
complex_variable = tf.Variable([5 + 4j, 6 + 1j])

print("Shape: ", my_variable.shape)
print("DType: ", my_variable.dtype)
print("As NumPy: ", my_variable.numpy())

Shape:  (2, 2)
DType:  <dtype: 'float32'>
As NumPy:  [[1. 2.]
 [3. 4.]]

print("A variable:", my_variable)
print("\nViewed as a tensor:", tf.convert_to_tensor(my_variable))
print("\nIndex of highest value:", tf.math.argmax(my_variable))

# This creates a new tensor; it does not reshape the variable.
print("\nCopying and reshaping: ", tf.reshape(my_variable, [1,4]))

A variable: <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy=
array([[1., 2.],
       [3., 4.]], dtype=float32)>

Viewed as a tensor: tf.Tensor(
[[1. 2.]
 [3. 4.]], shape=(2, 2), dtype=float32)

Index of highest value: tf.Tensor([1 1], shape=(2,), dtype=int64)

Copying and reshaping:  tf.Tensor([[1. 2. 3. 4.]], shape=(1, 4), dtype=float32)

a = tf.Variable([2.0, 3.0])
# This will keep the same dtype, float32
a.assign([1, 2]) 
# Not allowed as it resizes the variable: 
try:
  a.assign([1.0, 2.0, 3.0])
except Exception as e:
  print(f"{type(e).__name__}: {e}")

ValueError: Cannot assign value to variable ' Variable:0': Shape mismatch.The variable shape (2,), and the assigned value shape (3,) are incompatible.

a = tf.Variable([2.0, 3.0])
# Create b based on the value of a
b = tf.Variable(a)
a.assign([5, 6])

# a and b are different
print(a.numpy())
print(b.numpy())

# There are other versions of assign
print(a.assign_add([2,3]).numpy())  # [7. 9.]
print(a.assign_sub([7,9]).numpy())  # [0. 0.]

[5. 6.]
[2. 3.]
[7. 9.]
[0. 0.]

# Create a and b; they will have the same name but will be backed by
# different tensors.
a = tf.Variable(my_tensor, name="Mark")
# A new variable with the same name, but different value
# Note that the scalar add is broadcast
b = tf.Variable(my_tensor + 1, name="Mark")

# These are elementwise-unequal, despite having the same name
print(a == b)

tf.Tensor(
[[False False]
 [False False]], shape=(2, 2), dtype=bool)

step_counter = tf.Variable(1, trainable=False)

with tf.device('CPU:0'):

  # Create some tensors
  a = tf.Variable([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
  b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
  c = tf.matmul(a, b)

print(c)

tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)

with tf.device('CPU:0'):
  a = tf.Variable([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
  b = tf.Variable([[1.0, 2.0, 3.0]])

with tf.device('GPU:0'):
  # Element-wise multiply
  k = a * b

print(k)

tf.Tensor(
[[ 1.  4.  9.]
 [ 4. 10. 18.]], shape=(2, 3), dtype=float32)