GenomeHandler with max_pooling set to False returns error

[cordeirojoao]

Hello, I was trying to implement the "hello world" example and exploring the GenomeHandler function parameters. I was able to put the example running but once I set the max_pooling=False I get the error 'a' must be greater than 0 unless no samples are taken on the devol.run function

Can someone help me on that?

Code

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0],x_train.shape[1], x_train.shape[2], 1).astype('float32') / 255
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1).astype('float32') / 255
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
dataset = ((x_train, y_train), (x_test, y_test))

genome_handler = GenomeHandler(max_conv_layers=6 ,max_dense_layers=2,max_filters=256,max_dense_nodes=1024,input_shape=x_train.shape[1:],n_classes=10, batch_normalization=True, dropout=True, max_pooling=False)
devol = DEvol(genome_handler)
model = devol.run(dataset=dataset, num_generations=1,pop_size=1,epochs=1)

Error

ValueError                                Traceback (most recent call last)
<ipython-input-36-9cc5827c046e> in <module>
      2                   num_generations=1,
      3                   pop_size=1,
----> 4                   epochs=1)

c:\users\joao\documents\devol\devollib\devol\devol.py in run(self, dataset, num_generations, pop_size, epochs, fitness, metric)
    120 
    121         # generate and evaluate initial population
--> 122         members = self._generate_random_population(pop_size)
    123         pop = self._evaluate_population(members,
    124                                         epochs,

c:\users\joao\documents\devol\devollib\devol\devol.py in _generate_random_population(self, size)
    233 
    234     def _generate_random_population(self, size):
--> 235         return [self.genome_handler.generate() for _ in range(size)]
    236 
    237     def _print_result(self, fitness, generation):

c:\users\joao\documents\devol\devollib\devol\devol.py in <listcomp>(.0)
    233 
    234     def _generate_random_population(self, size):
--> 235         return [self.genome_handler.generate() for _ in range(size)]
    236 
    237     def _print_result(self, fitness, generation):

c:\users\joao\documents\devol\devollib\devol\genome_handler.py in generate(self)
    208             for key in self.convolutional_layer_shape:
    209                 param = self.layer_params[key]
--> 210                 genome.append(np.random.choice(param))
    211         for i in range(self.dense_layers):
    212             for key in self.dense_layer_shape:

mtrand.pyx in mtrand.RandomState.choice()

ValueError: 'a' must be greater than 0 unless no samples are taken

[cordeirojoao]

@joeddav Hello Joe. I´m "stuck" on this step. I really would appreciate your help on this issue. Thank you very much for your attention :)