<jupyter_start><jupyter_text>Load tensorflow<jupyter_code>import tensorflow as tf
tf.__version__<jupyter_output><empty_output><jupyter_text>creating constant<jupyter_code>hello =  tf.constant('hello world')
print(hello)<jupyter_output>Tensor("Const:0", shape=(), dtype=string)<jupyter_text>note:  N dimensional data is called tensors **Defining a shape**<jupyter_code>greeting = tf.constant('hello world', name='nirmal', shape=[6,2])
print(greeting)
greeting2 = tf.constant('tensorflow learning',name='nirmal')
print(greeting2)<jupyter_output>Tensor("nirmal_1:0", shape=(), dtype=string)<jupyter_text>observe how tensorflow is tracking name of different tensor, see first parameter in tensor<jupyter_code>greeting3 = tf.constant('tracking tensor',name='jpt')
print(greeting3)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1341614906832298, 'fraction_numerical': 0.011180124223602485, 'mean_word_length': 12.661016949152541, 'pattern_counts': {'":': 0, '<': 15, '<?xml version=': 0, '>': 15, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '12435', 'license': 'MIT', 'n_tokens_mistral': 283, 'n_tokens_neox': 281, 'n_words': 48, 'path': 'Great learning/tensorflow_basic.ipynb', 'repo_name': 'NirmalSilwal/Deep-Learning', 'size': '805'}