Create 3d loss surface visualizations with optimizer path

MLVTK

A loss surface visualization tool.

Simple feed-forward network trained on chess data, using elu activation and Adam optimizer

Simple feed-forward network trained on chess data, using tanh activation and SGD optimizer

3 layer feed-forward network trained on hand written letters data, using relu activation, SGD optimizer and learning rate of 2.0. Example of what happens to path when learning rate is too high

Simple feed-forward network trained on chess data, using hard-sigmoid activation and RMSprop optimizer

Why?

• :shipit: Simple: A single line addition is all that is needed.

• :question: Informative: Gain insight into what your model is seeing.

• :notebook: Educational: See how your hyper parameters and architecture impact your

models perception.

Quick Start

Install locally (Also works in google Colab!):

pip install mlvtk

Optionally for use with jupyter notebook/lab:

Notebook

pip install "notebook>=5.3" "ipywidgets==7.5"

Lab

pip install jupyterlab "ipywidgets==7.5"

# Basic JupyterLab renderer support
jupyter labextension install [email protected]

# OPTIONAL: Jupyter widgets extension for FigureWidget support
jupyter labextension install @jupyter-widgets/jupyterlab-manager [email protected]

Basic Example

from mlvtk.base import Vmodel
import tensorflow as tf
import numpy as np

# NN with 1 hidden layer
inputs = tf.keras.layers.Input(shape=(None,100))
dense_1 = tf.keras.layers.Dense(50, activation='relu')(inputs)
outputs = tf.keras.layers.Dense(10, activation='softmax')(dense_1)
_model = tf.keras.Model(inputs, outputs)

# Wrap with Vmodel
model = Vmodel(_model)
model.compile(optimizer=tf.keras.optimizers.SGD(),
loss=tf.keras.losses.CategoricalCrossentropy(), metrics=['accuracy'])

# All tf.keras.(Model/Sequential/Functional) methods/properties are accessible
# from Vmodel

model.summary()
model.get_config()
model.get_weights()
model.layers

# Create random example data
x = np.random.rand(3, 10, 100)
y = np.random.randint(9, size=(3, 10, 10))
xval = np.random.rand(1, 10, 100)
yval = np.random.randint(9, size=(1,10,10))

# Only difference, model.fit requires validation_data (tf.data.Dataset, or
# other container
history = model.fit(x, y, validation_data=(xval, yval), epochs=10, verbose=0)

# Calling model.surface_plot() returns a plotly.graph_objs.Figure
# model.surface_plot() will attempt to display the figure inline

fig = model.surface_plot()

# fig can save an interactive plot to an html file,
fig.write_html("surface_plot.html")

# or display the plot in jupyter notebook/lab or other compatible tool.
fig.show()

GitHub

https://github.com/tm-schwartz/mlvtk

Source: https://pythonawesome.com/create-3d-loss-surface-visualizations-with-optimizer-path/