AI

Setup Virtual Enviornment

Install Virtualenv

pip3 install -U pip virtualenv

Create a virtuale enviornment in your project directory

cd my_project
virtualenv env

Activate virtual enviornment

./env/Scripts/activate

Upgrade pip

pip install --upgrade pip
pip list # show packages installed within the virtual enviornment

Exit virtual enviornment

deactivate

Install TensorFlow

Choose what package you want to install:

tensorflow - Latest stable release (2.x) for CPU_only -tensorflow-gpu - Latest stable release with GPU support (Ubuntu & Windows) -tf-nightly - Unstable build with GPU support (Ubuntu & Windows) -tensorflow==1.15 - The final version of TensorFlow 1.x

pip install --upgrade tensorflow

test installation:

python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

Datasets

Install tensorflow_datasets

pip install --upgrade tensorflow_datasets

dataset class archetecture:

    tfds.core.DatasetInfo(
       name='mnist',
       version=1.0.0,
       description='The MNIST database of handwritten digits.',
       homepage='http://yann.lecun.com/exdb/mnist/',
       features=FeaturesDict({
           'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
           'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10)
       },
       total_num_examples=70000,
       splits={
           'test': <tfds.core.SplitInfo num_examples=10000>,
           'train': <tfds.core.SplitInfo num_examples=60000>
       },
       supervised_keys=('image', 'label'),
       citation='"""
           @article{lecun2010mnist,
             title={MNIST handwritten digit database},
             author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
             journal={ATT Labs [Online]. Available: http://yann. lecun. com/exdb/mnist},
             volume={2},
             year={2010}
           }
     """',
 )

Usage

import tensorflow_datasets as tfds
import tensorflow as tf

# tfds works in both Eager and Graph modes
tf.compat.v1.enable_eager_execution()

# See available datasets
print(tfds.list_builders())

# Construct a tf.data.Dataset
ds_train = tfds.load(name="mnist", split="train", shuffle_files=True)

# Build your input pipeline
ds_train = ds_train.shuffle(1000).batch(128).prefetch(10)
for features in ds_train.take(1):
  image, label = features["image"], features["label"]

trainesb/AI

AI

Setup Virtual Enviornment

Install TensorFlow

Datasets