TypeError with 3d array

Hi @maxpumperla, thanks for all your help with this. I’m having an issue with using a 3d array for my X values. My training X values are in an array as float64 with the dimensions (926, 100, 1). These values are feed into a LSTM model. The 3d array is to fulfill the Keras format of n=926, timesteps=100, features=1. My model works when I do not use it with Hyperas. When I try to run it with Hyperas, I get this error:

File "/anaconda/lib/python3.6/inspect.py", line 636, in getfile
    'function, traceback, frame, or code object'.format(object))
TypeError: **(outputs entire array)**
is not a module, class, method, function, traceback, frame, or code object

Below is my model for reference:

def model(train_x,train_y,test_x,test_y):
    model = Sequential()

    model.add(LSTM(
        input_dim=1,
        output_dim=50,
        return_sequences=True))
    model.add(Dropout({{uniform(0,.5)}}))

    model.add(LSTM(
        100,
        return_sequences=False))
    model.add(Dropout({{uniform(0,.5)}}))
                
    model.add(Dense(
            output_dim=1))
    model.add(Activation("sigmoid"))

    start = time.time()
    model.compile(loss="binary_crossentropy", optimizer="rmsprop",metrics=['accuracy'],class_mode="binary")
    print("> Compilation Time : ", time.time() - start)
    
# Fitting model
    model.fit(
            train_x,
            train_y,
            batch_size={{choice([16,32,64,128,256,512])}},
            nb_epoch=5,
            validation_split=.05,
            show_accuracy=True)
    Score, Acc = model.evaluate(test_x,test_y)
    print('Accuracy', Acc)
    return {'loss':-Acc,'status':STATUS_OK,'model':model}


if __name__ == '__main__':
    best_run, best_model = optim.minimize(model=model,
                                          data=data(),
                                          algo=tpe.suggest,
                                          max_evals=5,
                                          trials=Trials())
    train_x, train_y, test_x, test_y = data()
    print("Evalutation of best performing model:")
    print(best_model.evaluate(test_x,test_y))
    print('Best performing hyper-paramaters:')
    print(best_run)

Any ideas? Thanks!

[loewenm]

Hello all - I’ve figured out the issue here; at least from my standpoint…

I thought the wrapper was a bit more flexible in terms of its parameter inputs when you call optim.minimize. It’s not flexible. I believe the majority of you are likely either trying to pass in your X_train, y_train, X_test, y_test, and/or the model directly. Simply put, you can’t use this wrapper in this fashion. You must pass in a “module, class, method, function, traceback, frame, or code object”, no exceptions.

So, when Max suggests using the following code:

    best_run, best_model = optim.minimize(model = create_model, 
                                          data = data, 
                                          algo = tpe.suggest, 
                                          max_evals = 5, 
                                          trials = Trials(), 
                                          notebook_name = None)

You literally need to pass in the parameters as such. You must make a data() function that returns the four data series and you must make a self-contained function that creates and returns the model i.e. create_model().

I suppose this is written explicitly in the instructions/setup and the error thrown also suggests this is the case as well… I’m a little ashamed that I didn’t read it thoroughly enough. This feels like a Zoolander moment (“the files are in the computer”). lol!

Thank you Max and team for putting hyperas together. It’s much less frustrating once you read the instructions… 😉

[anasouzac]

Hey, guys! I’m having the same problem… I’m running my code on a Jupyter Notebook. Is that a problem? Here’s the error:

---

TypeError Traceback (most recent call last) <ipython-input-4-27652ebc1ee2> in <module> 9 max_evals=5, 10 trials=Trials(), —> 11 notebook_name=‘teste’) 12 x_train, x_train, x_val, y_val = data() 13 print(“Evalutation of best performing model:”)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\hyperas\optim.py in minimize(model, data, algo, max_evals, trials, functions, rseed, notebook_name, verbose, eval_space, return_space, keep_temp) 67 notebook_name=notebook_name, 68 verbose=verbose, —> 69 keep_temp=keep_temp) 70 71 best_model = None

~\AppData\Local\Continuum\anaconda3\lib\site-packages\hyperas\optim.py in base_minimizer(model, data, functions, algo, max_evals, trials, rseed, full_model_string, notebook_name, verbose, stack, keep_temp) 96 model_str = full_model_string 97 else: —> 98 model_str = get_hyperopt_model_string(model, data, functions, notebook_name, verbose, stack) 99 temp_file = ‘./temp_model.py’ 100 write_temp_files(model_str, temp_file)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\hyperas\optim.py in get_hyperopt_model_string(model, data, functions, notebook_name, verbose, stack) 196 197 functions_string = retrieve_function_string(functions, verbose) –> 198 data_string = retrieve_data_string(data, verbose) 199 model = hyperopt_keras_model(model_string, parts, aug_parts, verbose) 200

~\AppData\Local\Continuum\anaconda3\lib\site-packages\hyperas\optim.py in retrieve_data_string(data, verbose) 217 218 def retrieve_data_string(data, verbose=True): –> 219 data_string = inspect.getsource(data) 220 first_line = data_string.split(“\n”)[0] 221 indent_length = len(determine_indent(data_string))

~\AppData\Local\Continuum\anaconda3\lib\inspect.py in getsource(object) 971 or code object. The source code is returned as a single string. An 972 OSError is raised if the source code cannot be retrieved.“”" –> 973 lines, lnum = getsourcelines(object) 974 return ‘’.join(lines) 975

~\AppData\Local\Continuum\anaconda3\lib\inspect.py in getsourcelines(object) 953 raised if the source code cannot be retrieved.“”" 954 object = unwrap(object) –> 955 lines, lnum = findsource(object) 956 957 if istraceback(object):

~\AppData\Local\Continuum\anaconda3\lib\inspect.py in findsource(object) 766 is raised if the source code cannot be retrieved.“”" 767 –> 768 file = getsourcefile(object) 769 if file: 770 # Invalidate cache if needed.

~\AppData\Local\Continuum\anaconda3\lib\inspect.py in getsourcefile(object) 682 Return None if no way can be identified to get the source. 683 “”" –> 684 filename = getfile(object) 685 all_bytecode_suffixes = importlib.machinery.DEBUG_BYTECODE_SUFFIXES[:] 686 all_bytecode_suffixes += importlib.machinery.OPTIMIZED_BYTECODE_SUFFIXES[:]

~\AppData\Local\Continuum\anaconda3\lib\inspect.py in getfile(object) 664 raise TypeError('module, class, method, function, traceback, frame, or ’ 665 ‘code object was expected, got {}’.format( –> 666 type(object).name)) 667 668 def getmodulename(path):

TypeError: module, class, method, function, traceback, frame, or code object was expected, got tuple

---

And my final code is:

from hyperopt import Trials, STATUS_OK, tpe from hyperas import optim from hyperas.distributions import choice, uniform

if name == ‘main’: best_run, best_model = optim.minimize(model=model, data=data(), algo=tpe.suggest, max_evals=5, trials=Trials(), notebook_name=‘teste’) x_train, x_train, x_val, y_val = 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)