def titanic():
# here use a standard 2d dictionary for inputting the param boundaries
p = {'lr': (0.5, 5, 10),
'first_neuron': [4, 8, 16],
'batch_size': [20, 30, 40],
'dropout': (0, 0.5, 5),
'optimizer': ['Adam', 'Nadam'],
'losses': ['logcosh', 'binary_crossentropy'],
'activation': ['relu', 'elu'],
'last_activation': ['sigmoid']}
return p
def iris():
from keras.optimizers import Adam, Nadam
from keras.losses import logcosh, categorical_crossentropy
from keras.activations import relu, elu, softmax
# here use a standard 2d dictionary for inputting the param boundaries
p = {'lr': (0.5, 5, 10),
'first_neuron': [4, 8, 16, 32, 64],
'hidden_layers': [0, 1, 2, 3, 4],
'batch_size': (2, 30, 10),
'epochs': [2],
'dropout': (0, 0.5, 5),
'weight_regulizer': [None],
'emb_output_dims': [None],
'shapes': ['brick', 'triangle', 0.2],
'optimizer': [Adam, Nadam],
'losses': [logcosh, categorical_crossentropy],
'activation': [relu, elu],
'last_activation': [softmax]}
return p
def breast_cancer():
from keras.optimizers import Adam, Nadam, RMSprop
from keras.losses import logcosh, binary_crossentropy
from keras.activations import relu, elu, sigmoid
# then we can go ahead and set the parameter space
p = {'lr': (0.5, 5, 10),
'first_neuron': [4, 8, 16, 32, 64],
'hidden_layers': [0, 1, 2],
'batch_size': (2, 30, 10),
'epochs': [50, 100, 150],
'dropout': (0, 0.5, 5),
'shapes': ['brick', 'triangle', 'funnel'],
'optimizer': [Adam, Nadam, RMSprop],
'losses': [logcosh, binary_crossentropy],
'activation': [relu, elu],
'last_activation': [sigmoid]}
return p
def cervical_cancer():
return breast_cancer()
