<jupyter_start><jupyter_text><jupyter_code>import tensorflow as tf

x = tf.constant([[1,0,2]])
y = tf.constant([[2],[3],[1]])
resultado = tf.matmul(x,y)
print (tf.Session().run(resultado))

x = tf.constant([[1,0],[0,0]])
y = tf.constant([[0,1],[1,0]])
resultado = tf.matmul(x,y)
print (tf.Session().run(resultado))


x = tf.constant([[0,1],[1,0]])
y = tf.constant([[1,0],[0,0]])
resultado = tf.matmul(x,y)
print (tf.Session().run(resultado))

x = tf.constant([[1,2],[3,1],[0,3]])
y = tf.constant([[1,0,1],[2,1,0]])
resultado = tf.matmul(x,y)
print (tf.Session().run(resultado))<jupyter_output>[[4]]
[[0 1]
 [0 0]]
[[0 0]
 [1 0]]
[[5 2 1]
 [5 1 3]
 [6 3 0]]
{'fraction_non_alphanumeric': 0.2926829268292683, 'fraction_numerical': 0.07926829268292683, 'mean_word_length': 5.98936170212766, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '289648', 'license': 'MIT', 'n_tokens_mistral': 333, 'n_tokens_neox': 320, 'n_words': 53, 'path': 'CalculoMatriz.ipynb', 'repo_name': 'ebasantos/Perceptron-Alg-Python', 'size': '656'}