rsepassi

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt

rsepassi

# Countdown
function countdown(){
   echo "Countdown started: $1 minutes" 
   background_countdown $1 &
}
function background_countdown(){
   date1=$((`date +%s` + $1 * 60)); 
   while [ "$date1" -ge `date +%s` ]; do
     :
   done

rsepassi

import tensorflow as tf
import tensorflow_datasets as tfds

# tfds works with Eager and Graph modes
tf.enable_eager_execution()

# 0. Select the dataset you'd like to use
print(tfds.list_builders())

# 1. Construct the DatasetBuilder

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rsepassi

import tensorflow_datasets as tfds

# Download the dataset and create a tf.data.Dataset
ds, info = tfds.load("mnist", split="train", with_info=True)

# Access relevant metadata with DatasetInfo
print(info.splits["train"].num_examples)
print(info.features["label"].num_classes)

# Build your input pipeline

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# Install: pip install tensorflow-datasets
import tensorflow_datasets as tfds
mnist_data = tfds.load("mnist")
mnist_train, mnist_test = mnist_data["train"], mnist_data["test"]
assert isinstance(mnist_train, tf.data.Dataset)

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import tensorflow_datasets as tfds

# Fetch the dataset directly
mnist = tfds.image.MNIST()
# or by string name
mnist = tfds.builder('mnist')

# Describe the dataset with DatasetInfo
assert mnist.info.features['image'].shape == (28, 28, 1)
assert mnist.info.features['label'].num_classes == 10

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import tensorflow_datasets as tfds

datasets = tfds.load("mnist")
train_dataset, test_dataset = datasets["train"], datasets["test"]
assert isinstance(train_dataset, tf.data.Dataset)

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# See the built-in configs
configs = tfds.text.IMDBReviews.builder_configs
assert "bytes" in configs

# Address a built-in config with tfds.builder
imdb = tfds.builder("imdb_reviews/bytes")
# or when constructing the builder directly
imdb = tfds.text.IMDBReviews(config="bytes")
# or use your own custom configuration
my_encoder = tfds.features.text.ByteTextEncoder(additional_tokens=['hello'])

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imdb = tfds.builder("imdb_reviews/subwords8k")

# Get the TextEncoder from DatasetInfo
encoder = imdb.info.features["text"].encoder
assert isinstance(encoder, tfds.features.text.SubwordTextEncoder)

# Encode, decode
ids = encoder.encode("Hello world")
assert encoder.decode(ids) == "Hello world"