<jupyter_start><jupyter_text><jupyter_code>import tensorflow as tf
import numpy as np
from tensorflow import keras
model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])
model.compile(optimizer='sgd', loss='mean_squared_error')
x = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)
y = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)
model.fit(x, y, epochs=1000)
print(model.predict([10.0]))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2, 'fraction_numerical': 0.07252747252747253, 'mean_word_length': 6.015384615384615, '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': '191943', 'license': 'MIT', 'n_tokens_mistral': 214, 'n_tokens_neox': 197, 'n_words': 37, 'path': '1_HelloWorld.ipynb', 'repo_name': 'vvmk007/coLab', 'size': '455'}