TensorFlow API memo
API of TensorFlow
Make a note of what you've researched about the TensorFlow API. I will add it every time I check it. Confirmed with version 1.21.
Reference link
-Installing TensorFlow on Windows was easy even for Python beginners -[Explanation for beginners] TensorFlow basic syntax and concept -[Explanation for beginners] TensorFlow tutorial MNIST (for beginners) -[Introduction to TensorBoard] Visualize TensorFlow processing to deepen understanding -[Introduction to TensorBoard: image] TensorFlow Visualize image processing to deepen understanding -[Introduction to TensorBoard: Projector] Make TensorFlow processing look cool -Visualize TensorFlow tutorial MNIST (for beginners) with TensorBoard -[Explanation for beginners] TensorFlow Tutorial Deep MNIST -Install matplotlib with Jupyter Notebook and display graph -Yuki Kashiwagi's facial features to understand TensorFlow [Part 1]
Description
It returns the number of elements in the Tensor dimension. I've seen the size of the image file.
Basic syntax
shape(
input,
name=None,
out_type=tf.int32
)
Example 1
Returns the shape of a one-dimensional three-element array
import tensorflow as tf
sess = tf.InteractiveSession()
print(sess.run(tf.shape((tf.range(3)))))
result
[3]
Example 2
Reshape 0 to 11 and store in Tensor. Returns that Shape.
import tensorflow as tf
sess = tf.InteractiveSession()
three_dim = tf.reshape(tf.range(6),[1,2,3])
print(sess.run(three_dim))
print(sess.run(tf.shape(three_dim)))
Result (top is Tensor content, bottom is shape result)
[[[0 1 2] [3 4 5]]]
[1 2 3]
Description
It will make the numbers in order. It comes in handy when checking the operation.
Basic syntax
range(limit, delta=1, dtype=None, name='range')
range(start, limit, delta=1, dtype=None, name='range'))
Example 1
Store 0 to 11 in Tensor
import tensorflow as tf
sess = tf.InteractiveSession()
print(sess.run(tf.range(12)))
result
[ 0 1 2 3 4 5 6 7 8 9 10 11]
Example 2
Reshape 0 to 11 and store in Tensor. This method is useful for checking the operation.
import tensorflow as tf
sess = tf.InteractiveSession()
print(sess.run(tf.reshape(tf.range(12), [3,4])))
result
[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]]
Description
Convert tensor format.
Basic syntax
reshape(
tensor,
shape,
name=None
)
Example 1
Convert a 1D array from 0 to 11 to a 2x6 2D array
import tensorflow as tf
sess = tf.InteractiveSession()
print(sess.run(tf.reshape(tf.range(12), [2,6])))
result
[[ 0 1 2 3 4 5] [ 6 7 8 9 10 11]]
Example 2
Convert a 1D array from 0 to 11 to a 2x3x2 3D array
import tensorflow as tf
sess = tf.InteractiveSession()
print(sess.run(tf.reshape(tf.range(12), [2,3,2])))
result
[[[ 0 1] [ 2 3] [ 4 5]]
[[ 6 7] [ 8 9] [10 11]]]
Example 3
Convert a 1D array from 0 to 11 to a 2x3x2 3D array (using -1) -1 means a wildcard and can only be used once (do not use it like [-1, -1, 2]). In this example, 12 variables are set to $ 12 ÷ 2 ÷ 2 = 3 $ and 3 is calculated.
import tensorflow as tf
sess = tf.InteractiveSession()
print(sess.run(tf.reshape(tf.range(12), [2,-1,2])))
result
[[[ 0 1] [ 2 3] [ 4 5]]
[[ 6 7] [ 8 9] [10 11]]]
Description
Convert the order of tensors. It is easy to understand in [TensorFlow] View API document -Math edition-.
Basic syntax
transpose(
a,
perm=None,
name='transpose'
)
Example 1
Permutation of a 2x6 2D array from 0 to 11. Since it is two-dimensional, it is a simple matrix transformation.
import tensorflow as tf
sess = tf.InteractiveSession()
x = (tf.reshape(tf.range(12), [-1,2]))
print(sess.run(x))
print(sess.run(tf.transpose(x)))
result
$ x $ Tensor
[[ 0 1] [ 2 3] [ 4 5] [ 6 7] [ 8 9] [10 11]]
The result of transposing $ x $
[[ 0 2 4 6 8 10] [ 1 3 5 7 9 11]]
Example 2
Ordering of a 4-dimensional array from 0 to 11. The order is specified by perm. In this example, the original Tensor is sorted in the order of 3D, 0D, 1D, and 2D.
import tensorflow as tf
sess = tf.InteractiveSession()
y = (tf.reshape(tf.range(12), [2,2,1,3]))
print(sess.run(y))
print(sess.run(tf.transpose(y, perm=[3,0,1,2])))
result
$ y $ Tensor
[[[[ 0 1 2]] [[ 3 4 5]]] [[[ 6 7 8]] [[ 9 10 11]]]]
The result of transposing $ y $
[[[[ 0] [ 3]] [[ 6] [ 9]]] [[[ 1] [ 4]] [[ 7] [10]]] [[[ 2] [ 5]] [[ 8] [11]]]]
Description
Returns random numbers limited to up to twice the standard deviation according to a normal distribution.
Basic syntax
truncated_normal(
shape,
mean=0.0,
stddev=1.0,
dtype=tf.float32,
seed=None,
name=None
)
Example
Create 300 million random numbers with standard deviation of 0.1 and display them as a histogram.
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
sess = tf.InteractiveSession()
x = sess.run(tf.truncated_normal([30000], stddev=0.1))
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.hist(x, bins=100)
ax.set_title('Histogram tf.truncated_normal')
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()
This is a reference (random_normal, normal normal distribution)
Description
Function wrapper. If the argument main is None, main.main is executed. It seems to be convenient when calling with a command. Although it is in English, it is described in detail in Stackoverflow.
Basic syntax
run(
main=None,
argv=None
)
Description
Output to TensorBoard graph.
Basic syntax
scalar(
name,
tensor,
collections=None
)
Example
Output the value of $ x + y $ to TensorBoard. Compare with or without tf.summary.scalar
import tensorflow as tf
sess = tf.InteractiveSession()
#TensorBoard information output directory
log_dir = '/tmp/tensorflow/mnist/logs/try01'
#Delete the specified directory if it exists and recreate it
if tf.gfile.Exists(log_dir):
tf.gfile.DeleteRecursively(log_dir)
tf.gfile.MakeDirs(log_dir)
#1 with a constant+ 2
x = tf.constant(1, name='x')
y = tf.constant(2, name='y')
z_out = x + y
z_no_out = x + y
#Output z on the graph with this command
tf.summary.scalar('z', z_out)
#Draw a graph with SummaryWriter
summary_writer = tf.summary.FileWriter(log_dir , sess.graph)
#Run
print(sess.run(z_out))
print(sess.run(z_no_out))
#SummaryWriter closed
summary_writer.close()
Result (left is when tf.summary.scalar is used, right is not)
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