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A simple python package to print a keras NN training history.

Project description

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A python package to print a Keras model training history

How do I install this package?

As usual, just download it using pip:

pip install plot_keras_history

Tests Coverage

Since some software handling coverages sometime get slightly different results, here’s three of them:

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Usage

Let’s say you have a model generated by the function my_keras_model:

Plotting a training history

In the following example we will see how to plot and either show or save the training history:

standard

from plot_keras_history import plot_history
import matplotlib.pyplot as plt

model = my_keras_model()
history = model.fit(...).history
plot_history(history)
plt.show()
plot_history(history, path="standard.png")
plt.close()

Plotting into separate graphs

By default, the graphs are all in one big image, but for various reasons you might need them one by one:

from plot_keras_history import plot_history
import matplotlib.pyplot as plt

model = my_keras_model()
history = model.fit(...).history
plot_history(history, path="singleton", single_graphs=True)
plt.close()

Reducing the history noise with Savgol Filters

In some occasion it is necessary to be able to see the progress of the history to interpolate the results to remove a bit of noise. A parameter is offered to automatically apply a Savgol filter:

interpolated

from plot_keras_history import plot_history
import matplotlib.pyplot as plt

model = my_keras_model()
history = model.fit(...).history
plot_history(history, path="interpolated.png", interpolate=True)
plt.close()

Automatic aliases

A number of metrics are automatically converted from the default ones to more talking ones, for example “lr” becomes “Learning Rate”, or “acc” becomes “Accuracy”.

All the available options

def plot_history(
    history, # Either the history object or a pandas DataFrame. When using a dataframe, the index name is used as abscissae label.
    style:str="-", # The style of the lines.
    interpolate: bool = False, # Wethever to interpolate or not the graphs datapoints.
    side: float = 5, # Dimension of the graphs side.
    graphs_per_row: int = 4, # Number of graphs for each row.
    customization_callback: Callable = None, # Callback for customizing the graphs.
    path: str = None, # Path where to store the resulting image or images (in the case of single_graphs)
    single_graphs: bool = False #  Wethever to save the graphs as single of multiples.
)

Chaining histories

It’s common to stop and restart a model’s training, and this would break the history object into two: for this reason the method chain_histories is available:

from plot_keras_history import chain_histories

model = my_keras_model()
history1 = model.fit(...).history
history2 = model.fit(...).history
history = chain_histories(history1, history2)

Extras

Numerous additional metrics are available in extra_keras_metrics

Project details


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