## talos

Hyperparameter Scanning and Optimization for Keras.

Talos is a solution that helps finding hyperparameter configurations for Keras models. To perform hyperparameter optimization with Talos, there is no need to learn any new syntax, or change anything in the way Keras models are created. Keras functionality is fully exposed, and any parameter can be included in the scans.

Talos is made for data scientists and data engineers that want to remain in complete control of their Keras models, but are tired of mindless parameter hopping and confusing optimization solutions that add complexity instead of taking it away.

## Development Objectives

Currently Talos yields state-of-the-art results (e.g. Iris dataset 100% and Wisconsin Breast Cancer dataset 99.4%) across a range of prediction tasks in a semi-automatic manner, while providing the simplest available method for hyperparameter optimization with Keras.

Read the roadmap HERE

## Benefits

Based on a review of more than 30 hyperparameter optimization and scanning solutions, Talos offers the most intuitive, easy-to-learn, and permissive access to important hyperparameter optimization capabilities.

- works with ANY Keras model
- very easy to implement
- adds zero new overhead
- provides several ways to reduce random-search complexity
- no need to learn any new syntax
- no blackbox / other statistical complexity
- improved f1 performance metric for binary, multi-label, multi-class and continuous predictions

## Install

```
pip install talos
```

Or from git repo:

```
pip install git+https://github.com/autonomio/talos.git
```

## How to use

Let's consider an example of a simple Keras model:

```
model = Sequential()
model.add(Dense(8, input_dim=x_train.shape[1], activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(y_train.shape[1], activation='softmax'))
model.compile(optimizer='adam',
loss=categorical_crossentropy,
metrics=['acc'])
out = model.fit(x_train, y_train,
batch_size=20,
epochs=200,
verbose=0,
validation_data=[x_val, y_val])
```

To prepare the model for a talos scan, we simply replace the parameters we want to include in the scans with references to our parameter dictionary (example of dictionary provided below). The below example code complete here.

```
def iris_model(x_train, y_train, x_val, y_val, params):
model = Sequential()
model.add(Dense(params['first_neuron'], input_dim=x_train.shape[1], activation=params['activation']))
model.add(Dropout(params['dropout']))
model.add(Dense(y_train.shape[1], activation=params['last_activation']))
model.compile(optimizer=params['optimizer'],
loss=params['losses'],
metrics=['acc'])
out = model.fit(x_train, y_train,
batch_size=params['batch_size'],
epochs=params['epochs'],
verbose=0,
validation_data=[x_val, y_val])
return out, model
```

As you can see, the only thing that changed, is the values that we provide for the parameters. We then pass the parameters with a dictionary:

```
p = {'lr': (2, 10, 30),
'first_neuron':[4, 8, 16, 32, 64, 128],
'hidden_layers':[2,3,4,5,6],
'batch_size': [2, 3, 4],
'epochs': [300],
'dropout': (0, 0.40, 10),
'weight_regulizer':[None],
'emb_output_dims': [None],
'optimizer': [Adam, Nadam],
'losses': [categorical_crossentropy, logcosh],
'activation':[relu, elu],
'last_activation': [softmax]}
```

The above example is a simple indication of what is possible. Any parameter that Keras accepts, can be included in the dictionary format.

Talos accepts lists with values, and tuples (start, end, n). Learning rate is normalized to 1 so that for each optimizer, lr=1 is the default Keras setting. Once this is all done, we can run the scan:

```
h = ta.Scan(x, y,
params=p,
dataset_name='first_test',
experiment_no='2',
model=iris_model,
grid_downsample=0.5)
```

## Optimization

One of the challenges in the paradigm where Talos exposes Keras models fully, is the evaluation of permutations across different experiments, as it is common that evaluation metrics can change depending on the model.

This is overcome by having a grading system that first detects the kind of data (type of prediction and distribution of truth values), and then uses a relevant robust scoring mechanism (f1 score) to produce an apples-to-apples "grade" for each permutation, which is then stored in the master log. The same score is available for binary, multi-label, multi-class (one hot encoded) and continuous data. For each type of prediction, the score is produced following the same procedure, resulting in a true apples-to-apples performance metric.