<jupyter_start><jupyter_text>Shapes and Ranks<jupyter_code>import tensorflow as tf
import numpy as np
a = tf.Variable(1)
print('Rank: {}'.format(tf.rank(a)))
print('Shape: {}'.format(tf.shape(a)))

b = tf.Variable([1, 2, 3, 4])
print('Rank: {}'.format(tf.rank(b)))
print('Shape: {}'.format(tf.shape(b)))

c = tf.Variable([[1, 2, 3, 4],[2, 4, 6, 8]])
print('Rank: {}'.format(tf.rank(c)))
print('Shape: {}'.format(tf.shape(c)))
print(c[1,3])
d = tf.Variable([[[1, 2, 3, 4],[11, 22, 33, 44]],[[111, 222, 333, 444],[1111, 2222, 3333, 4444]]])
print('Rank: {}'.format(tf.rank(d)))
print('Shape: {}'.format(tf.shape(d)))
print(d[0,1,2])
print(d)
import tensorflow.keras.backend as K
import tensorflow.keras.layers as KL

IMAGE_SHAPE = np.array([1024,1024,3])

input_image_akTwelve = KL.Input(shape=[None, None, IMAGE_SHAPE[2]], name="input_image")
print(input_image_akTwelve)
print(K.shape(input_image_akTwelve))
print(K.int_shape(input_image_akTwelve))

input_image_superlee = KL.Input(shape=IMAGE_SHAPE.tolist(), name="input_image")
print(input_image_superlee)
input_image_superlee = input_image_superlee
print(input_image_superlee)
e = tf.squeeze(d,-1)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21374045801526717, 'fraction_numerical': 0.058524173027989825, 'mean_word_length': 8.007633587786259, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '521016', 'license': 'MIT', 'n_tokens_mistral': 537, 'n_tokens_neox': 482, 'n_words': 85, 'path': 'other/Notebook tests/Shapes and Ranks.ipynb', 'repo_name': 'LukasStu/Mask_RCNN_Humanpose', 'size': '1179'}