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https://github.com/maxpumperla/hyperas

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
https://github.com/maxpumperla/hyperas

hyperopt hyperparameter-optimization keras

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Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

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# Hyperas [![Build Status](https://travis-ci.org/maxpumperla/hyperas.svg?branch=master)](https://travis-ci.org/maxpumperla/hyperas)  [![PyPI version](https://badge.fury.io/py/hyperas.svg)](https://badge.fury.io/py/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

```python

pip install hyperas

```



## Quick start



Assume you have data generated as such



```python

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



```python

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`).



```python

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:



```python

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



```python

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](https://github.com/maxpumperla/hyperas/issues/125).



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"](https://github.com/maxpumperla/hyperas/issues/83) or [OSError, Err22](https://github.com/maxpumperla/hyperas/issues/149), you may need add `notebook_name='simple_notebook'`(assume your current notebook name is `simple_notebook`) in `optim.minimize` function like this:



```python

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](https://github.com/maxpumperla/hyperas/blob/master/hyperas/optim.py) and [utils.py](https://github.com/maxpumperla/hyperas/blob/master/hyperas/utils.py).



### How to read the output of a hyperas model?



Hyperas translates your script into `hyperopt` compliant code, see [here](https://github.com/maxpumperla/hyperas/issues/140) 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):



```python

import pickle

def data(fname):

    with open(fname,'rb') as fh:

        return pickle.load(fh)

```

Note that your arguments must be implemented such that `repr` can show them in their entirety (such as strings and numbers).

If you want more complex objects, use the passed arguments to build them inside the `data` function.



And when you run your trials, pass a tuple of arguments to be substituted in as `data_args`:



```python

best_run, best_model = optim.minimize(

    model=model,

    data=data,

    algo=tpe.suggest,

    max_evals=64,

    trials=Trials(),

    data_args=('my_file.pkl',)

)

``` 



### What if I need more flexibility loading data and adapting my model?



Hyperas is a convenience wrapper around Hyperopt that has some limitations. If it's not _convenient_ to use in your situation, simply don't use it -- and choose Hyperopt instead. All you can do with Hyperas you can also do with Hyperopt, it's just a different way of defining your model. If you want to squeeze some flexibility out of Hyperas anyway, take a look [here](https://github.com/maxpumperla/hyperas/issues/141).



### Running hyperas in parallel?



You can use hyperas to run multiple models in parallel with the use of mongodb (which you'll need to install and setup users for).

 Here's a short example using MNIST:



1. Copy and modify [`examples/mnist_distributed.py`](examples/mnist_distributed.py) (bump up `max_evals` if you like):

2. Run `python mnist_distributed.py`. It will create a `temp_model.py` file. Copy this file to any machines that will be evaluating models.

     It will then begin waiting for evaluation results

3. On your other machines (make sure they have a python installed with all your dependencies, ideally with the same versions) run:

    ```bash

    export PYTHONPATH=/path/to/temp_model.py

    hyperopt-mongo-worker --exp-key='mnist_test' --mongo='mongo://username:[email protected]:27017/jobs'

    ```

4. Once `max_evals` have been completed, you should get an output with your best model. You can also look through 

    your mongodb and examine the results, to get the best model out and run it, do:

    

    ```python

    from pymongo import MongoClient

    from keras.models import load_model

    import tempfile

    c = MongoClient('mongodb://username:[email protected]:27017/jobs')

    best_model = c['jobs']['jobs'].find_one({'exp_key': 'mnist_test'}, sort=[('result.loss', -1)])

    temp_name = tempfile.gettempdir()+'/'+next(tempfile._get_candidate_names()) + '.h5'

    with open(temp_name, 'wb') as outfile:

        outfile.write(best_model['result']['model_serial'])

    model = load_model(temp_name)

    ```