IndentationError: unexpected indent error in your "complete example"

[ghost]

@maxpumperla Hi Max,
I think there is a serious issue whenever we utilize loops in hyperas. for example in your complete example in the first page of your GitHub repo, I get the following error:

File "", line 39
if conditional({{choice(['three', 'four'])}}) == 'four':
^
IndentationError: unexpected indent

I put the very complete/modified example of your code here:
`
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform, conditional
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU, SReLU, PReLU, ThresholdedReLU

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

This function is separated from 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 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(BatchNormalization())
model.add(Activation({{choice(['relu', 'softplus', 'softsign'])}}))
model.add(Dropout({{uniform(0, 0.5)}}))

model.add(Dense({{choice([32, 64, 128, 256, 512, 768, 1024])}}))
model.add(BatchNormalization())
model.add(Activation({{choice(['relu', 'sigmoid', 'hard_sigmoid', 'softmax', 'tanh', 'softplus', 'softsign'])}}))
model.add(Dropout({{uniform(0, 0.5)}}))

# If we choose 'four', add an additional fourth layer
# if conditional({{choice(['three', 'four'])}}) == 'four':
for _ in range(1, conditional({{choice([1, 4, 8, 16])}})):
    # model.add(Dense(100))
    model.add(Dense({{choice([32, 64, 128, 256, 512, 768, 1024])}}))
    model.add(BatchNormalization())
    # We can also choose between complete sets of layers
    model.add({{choice([Dropout(0.5), Dropout(0.25),
                        Activation('linear'), Activation('relu'), Activation('hard_sigmoid'), Activation('softmax')])}})
    model.add(Activation('relu'))

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

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

model.fit(X_train, Y_train,
          batch_size={{choice([64, 128, 256, 512, 768, 1024])}},
          nb_epoch=1,
          verbose=2,
          validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}

best_run, best_model = optim.minimize(model=model,
data=data,
algo=tpe.suggest,
max_evals=100,
trials=Trials(), notebook_name="simple_notebook")
best_model.save("best_model.h5")
X_train, Y_train, X_test, Y_test = data()
print("Evaluation of best performing model:")
print(best_model.evaluate(X_test, Y_test))
print(best_model.summary())
`

If you run this code, you will get.

File "", line 57
for _ in range(1, conditional({{choice([1, 4, 8, 16])}})):
^
IndentationError: unexpected indent

so, it is serious issue, and I think it's because you might forget special note there... the code work properly in previous versions :(

[maxpumperla]

Yes, that's very unfortunate. Thanks for reporting this. I'll investigate and add this case to the unit tests.

[ghost]

@maxpumperla I think it is hopefully not a big deal as it worked properly in previous version...
Also, I put the very complete example of hyperas which can be added to hyperas example directory if you are interested in...

[maxpumperla]

@ErmiaAzarkhalili fixed on master, was a very silly mistake

[ghost]

@maxpumperla Thanks, So we can close this issue...(I will open new issue for advanced layers in next days...)