def test_all(self):
rawData = test_data.rawData
idx = rawData.quantile(.99).sort_values(ascending=False).index[0:900]
rawData = rawData[idx]
hyperparams = {
"layers": [
{
"label": "dense",
"activation": "relu",
"nb_neurons": 150
},
{
"label": "dropout",
"activation": "dropout",
"rate": 0.2
},
{
"label": "dense",
"activation": "relu"
},
],
"loss":
"wMSE",
"optimizer":
"Adam",
"dims": [20, 500],
"preproc":
"log_or_exp",
"seed":
1,
"ncores":
4,
}
model = MultiNet(**hyperparams)
model.fit(rawData)
_ = model.predict(rawData)
print(model.score(rawData))
def test_all(self):
rawData = test_data.rawData
idx = rawData.quantile(.99).sort_values(ascending=False).index[0:900]
rawData = rawData[idx]
hyperparams = {
'layers': [{
'label': 'dense',
'activation': 'relu',
'nb_neurons': 150
}, {
'label': 'dropout',
'activation': 'dropout',
'rate': 0.2
}, {
'label': 'dense',
'activation': 'relu'
}],
'loss':
'mean_squared_error',
'optimizer':
'AdamOptimizer',
'dims': [20, 500],
'preproc':
'log_or_exp',
'seed':
1,
'ncores':
4
}
model = MultiNet(**hyperparams)
model.fit(rawData)
_ = model.predict(rawData)
print(model.score(rawData))