Hyperas

Hyperas brings fast experimentation with Keras and hyperparameter optimization with Hyperopt together. It lets you use the power of hyperopt without having to learn the syntax of it. Instead, just define your keras model as you are used to, but use a simple template notation to define hyper-parameter ranges to tune.

Installation

pip install hyperas

Quick start

Assume you have data generated as such

def data():
    x_train = np.zeros(100)
    x_test = np.zeros(100)
    y_train = np.zeros(100)
    y_test = np.zeros(100)
    return x_train, y_train, x_test, y_test

and an existing keras model like the following

def create_model(x_train, y_train, x_test, y_test):
    model = Sequential()
    model.add(Dense(512, input_shape=(784,)))
    model.add(Activation('relu'))
    model.add(Dropout(0.2))
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.2))
    model.add(Dense(10))
    model.add(Activation('softmax'))

    # ... model fitting

    return model

To do hyper-parameter optimization on this model, just wrap the parameters you want to optimize into double curly brackets and choose a distribution over which to run the algorithm.

In the above example, let's say we want to optimize for the best dropout probability in both dropout layers. Choosing a uniform distribution over the interval [0,1], this translates into the following definition. Note that before returning the model, to optimize, we also have to define which evaluation metric of the model is important to us. For example, in the following, we optimize for accuracy.

Note: In the following code we use 'loss': -accuracy, i.e. the negative of accuracy. That's because under the hood hyperopt will always minimize whatever metric you provide. If instead you want to actually want to minimize a metric, say MSE or another loss function, you keep a positive sign (e.g. 'loss': mse).

from hyperas.distributions import uniform

def create_model(x_train, y_train, x_test, y_test):
    model = Sequential()
    model.add(Dense(512, input_shape=(784,)))
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0, 1)}}))
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0, 1)}}))
    model.add(Dense(10))
    model.add(Activation('softmax'))

    # ... model fitting

    score = model.evaluate(x_test, y_test, verbose=0)
    accuracy = score[1]
    return {'loss': -accuracy, 'status': STATUS_OK, 'model': model}

The last step is to actually run the optimization, which is done as follows:

best_run = optim.minimize(model=create_model,
                          data=data,
                          algo=tpe.suggest,
                          max_evals=10,
                          trials=Trials())

In this example we use at most 10 evaluation runs and the TPE algorithm from hyperopt for optimization.

Check the "complete example" below for more details.

Complete example

Note: It is important to wrap your data and model into functions as shown below, and then pass them as parameters to the minimizer. data() returns the data the create_model() needs. An extended version of the above example in one script reads as follows. This example shows many potential use cases of hyperas, including:

• Varying dropout probabilities, sampling from a uniform distribution
• Different layer output sizes
• Different optimization algorithms to use
• Varying choices of activation functions
• Conditionally adding layers depending on a choice
• Swapping whole sets of layers

from __future__ import print_function
import numpy as np

from hyperopt import Trials, STATUS_OK, tpe
from keras.datasets import mnist
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential
from keras.utils import np_utils

from hyperas import optim
from hyperas.distributions import choice, uniform


def data():
    """
    Data providing function:

    This function is separated from create_model() so that hyperopt
    won't reload data for each evaluation run.
    """
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    nb_classes = 10
    y_train = np_utils.to_categorical(y_train, nb_classes)
    y_test = np_utils.to_categorical(y_test, nb_classes)
    return x_train, y_train, x_test, y_test


def create_model(x_train, y_train, x_test, y_test):
    """
    Model providing function:

    Create Keras model with double curly brackets dropped-in as needed.
    Return value has to be a valid python dictionary with two customary keys:
        - loss: Specify a numeric evaluation metric to be minimized
        - status: Just use STATUS_OK and see hyperopt documentation if not feasible
    The last one is optional, though recommended, namely:
        - model: specify the model just created so that we can later use it again.
    """
    model = Sequential()
    model.add(Dense(512, input_shape=(784,)))
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0, 1)}}))
    model.add(Dense({{choice([256, 512, 1024])}}))
    model.add(Activation({{choice(['relu', 'sigmoid'])}}))
    model.add(Dropout({{uniform(0, 1)}}))

    # If we choose 'four', add an additional fourth layer
    if {{choice(['three', 'four'])}} == 'four':
        model.add(Dense(100))

        # We can also choose between complete sets of layers

        model.add({{choice([Dropout(0.5), Activation('linear')])}})
        model.add(Activation('relu'))

    model.add(Dense(10))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
                  optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})

    result = model.fit(x_train, y_train,
              batch_size={{choice([64, 128])}},
              epochs=2,
              verbose=2,
              validation_split=0.1)
    #get the highest validation accuracy of the training epochs
    validation_acc = np.amax(result.history['val_acc']) 
    print('Best validation acc of epoch:', validation_acc)
    return {'loss': -validation_acc, 'status': STATUS_OK, 'model': model}


if __name__ == '__main__':
    best_run, best_model = optim.minimize(model=create_model,
                                          data=data,
                                          algo=tpe.suggest,
                                          max_evals=5,
                                          trials=Trials())
    X_train, Y_train, X_test, Y_test = data()
    print("Evalutation of best performing model:")
    print(best_model.evaluate(X_test, Y_test))
    print("Best performing model chosen hyper-parameters:")
    print(best_run)

FAQ

Here is a list of a few popular errors

TypeError: require string label

You're probably trying to execute the model creation code, with the templates, directly in python. That fails simply because python cannot run the templating in the braces, e.g. {{uniform..}}. The def create_model(...) function is in fact not a valid python function anymore.

You need to wrap your code in a def create_model(...): ... function, and then call it from optim.minimize(model=create_model,... like in the example.

The reason for this is that hyperas works by doing template replacement of everything in the {{...}} into a separate temporary file, and then running the model with the replaced braces (think jinja templating).

This is the basis of how hyperas simplifies usage of hyperopt by being a "very simple wrapper".

TypeError: 'generator' object is not subscriptable

This is currently a known issue.

Just pip install networkx==1.11

NameError: global name 'X_train' is not defined

Maybe you forgot to return the x_train argument in the def create_model(x_train...) call from the def data(): ... function.

You are not restricted to the same list of arguments as in the example. Any arguments you return from data() will be passed to create_model()

notebook adjustment

If you find error like "No such file or directory" or OSError, Err22, you may need add notebook_name='simple_notebook'(assume your current notebook name is simple_notebook) in optim.minimize function like this:

best_run, best_model = optim.minimize(model=model,
                                      data=data,
                                      algo=tpe.suggest,
                                      max_evals=5,
                                      trials=Trials(),
                                      notebook_name='simple_notebook')

How does hyperas work?

All we do is parse the data and model templates and translate them into proper hyperopt by reconstructing the space object that's then passed to fmin. Most of the relevant code is found in optim.py and utils.py.

How to read the output of a hyperas model?

Hyperas translates your script into hyperopt compliant code, see here for some guidance on how to interpret the result.

How to pass arguments to data?

Suppose you want your data function take an argument, specify it like this using positional arguments only (not keyword arguments):