# Plot Keras History

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`

## 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:

```
from plot_keras_history import show_history, plot_history
import matplotlib.pyplot as plt
model = my_keras_model()
history = model.fit(...)
show_history(history)
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(...)
plot_history(history, path="singleton", single_graphs=True)
plt.close()
```

### Plotting multiple histories

Let's suppose you are training your model on multiple holdouts and you would like to plot all of them, plus an average. Fortunately, we got you covered!

```
from plot_keras_history import plot_history
import matplotlib.pyplot as plt
histories = []
for holdout in range(10):
model = my_keras_model()
histories.append(model.fit(...))
plot_history(
histories,
show_standard_deviation=False,
show_average=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:

```
from plot_keras_history import plot_history
import matplotlib.pyplot as plt
model = my_keras_model()
history = model.fit(...)
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".

### Automatic normalization

The library automatically normalizes the ranges of metrics that are known to be either in [-1, 1] or [0, 1] ranges in order to avoid visual biases.

### 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(...)
history2 = model.fit(...)
history = chain_histories(history1, history2)
```

### Extras

Numerous additional metrics are available in extra_keras_metrics

### Cite this software

If you need a bib file to cite this work, here you have it:

```
@software{Cappelletti_Plot_Keras_History_2022,
author = {Cappelletti, Luca},
doi = {10.5072/zenodo.1054923},
month = {4},
title = {{Plot Keras History}},
version = {1.1.36},
year = {2022}
}
```