<jupyter_start><jupyter_text><jupyter_code># Download text zip file

import zipfile

# Download zip file of pizza_steak images
!wget https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip
# Unzip the downloaded file
zip_ref = zipfile.ZipFile("/content/jena_climate_2009_2016.csv.zip", "r")
zip_ref.extractall()
zip_ref.close()
import pandas as pd
df=pd.read_csv("/content/jena_climate_2009_2016.csv")  
df.head()
data=df.drop(columns=['Date Time']).to_numpy()
data
import tensorflow as tf
dataset=tf.keras.preprocessing.timeseries_dataset_from_array(
    data=data,
    targets=None,
    sequence_length=10,
    sequence_stride=1,
    sampling_rate=1,
    batch_size=128,
    shuffle=False,
    seed=None,
    start_index=None,
    end_index=None,
)
dataset
print(dataset[:,:,0])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12242424242424242, 'fraction_numerical': 0.04, 'mean_word_length': 5.661290322580645, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '539146', 'license': 'MIT', 'n_tokens_mistral': 332, 'n_tokens_neox': 309, 'n_words': 50, 'path': 'rnn2.ipynb', 'repo_name': 'PeterPirog/tensorflow-deep-learning', 'size': '825'}