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A Simple MNIST Example

admin

data analysis and vis in jupyter nb

https://jupyter.org/try
https://hub.gke2.mybinder.org/user/ipython-ipython-in-depth-9if5hwc5/notebooks/binder/Index.ipynb

import tensorflow as tf
print(tf.__version__)

# Load and prepare data, convert labels to one-hot encoding

mnist= tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test= x_train/ 255.0, x_test/ 255.0
y_train= tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test= tf.keras.utils.to_categorical(y_test, num_classes=10)

# Configure the model layers
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(100, activation='relu'))
model.add(tf.keras.layers.Dense(50, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.summary()

# Configure the model training procedure
model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.01, momentum=0.9),
  loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),
  metrics=['accuracy'])
model.fit(x_train, y_train, epochs=20, batch_size=64, validation_split=0.2)
model.evaluate(x_test, y_test, batch_size=64)

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tensorflow pipenv keras

admin

https://github.com/flowxcode/deepflow
https://pipenv.pypa.io/en/latest/
https://pipenv.pypa.io/en/latest/install/

$ pipenv run python main.py

$ pipenv shell

https://stackoverflow.com/a/68673872/1650038

settings.json to pipvenv in home .local

"python.pythonPath": "${env:HOME}/.local/share/virtualenvs/deepflow-eho_wYiM/bin/python"

launch.json

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: Current File",
            "type": "python",
            "request": "launch",
            "program": "${file}",
            "console": "integratedTerminal",
            //"program": "/home/linx/.local/share/virtualenvs/deepflow-eho_wYiM/<program>",
        }
    ]
}

test x format

import tensorflow as tf
print("TensorFlow version:", tf.__version__)

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the path to neural networks on flask python

admin

https://towardsdatascience.com/a-financial-neural-network-for-quants-45ec0aaef73c

conda init
conda config --set auto_activate_base false
pip install notebook
cd /your/project/root/directory/
jupyter notebook

code left

c right

!! dont forget about requirements.txt

https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6

https://www.datacamp.com/community/tutorials/lstm-python-stock-market

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virtual env python

admin

different projects, different requirements different envs:
create your virtual environment

python3 -m venv tutorial-env
python -m venv venv
$ . venv/bin/activate

https://docs.python.org/3/tutorial/venv.html

add it in your git repo, .gitignore

echo "venv" >> .gitignore
create requirements.txt:
pip freeze > requirements.txt
git add requirements.txt

https://medium.com/wealthy-bytes/the-easiest-way-to-use-a-python-virtual-environment-with-git-401e07c39cde

pip install -r requirements.txt

https://boscacci.medium.com/why-and-how-to-make-a-requirements-txt-f329c685181e

tia

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AST LHS RHS

admin

https://www.codementor.io/@erikeidt/overview-of-a-compiler-zayyljs2s#evalutation-context-lhs-rhs-branch

https://en.wikipedia.org/wiki/Abstract_syntax_tree

https://en.wikipedia.org/wiki/Parse_tree

citation:

Left Hand Side vs. Right Hand Side

For example, in a = b + c, we evaluate the value of b and c, add them together, and then associate or store the result in a.  Here b and c have the context that we call right hand side — b and c are on the right hand side (of an assignment), whereas a has left hand side context — a is on the left hand side of an assignment.  We need the value of b and c yet the location of a to store the result.

A complex left hand side expression will itself also involve some right hand side evaluation.  For example, a[i] = 5 requires evaluting a and i as values (as if right hand side) and only the array indexing itself is evalutated as left hand side (for storage location).  5, of course, is understood as right hand side.

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