"Dense_0": {
'units': 64,
'activation': 'relu'
},
"Flatten": {},
"Dense_3": {
'units': 1
},
}
}
df = arg_beach()
input_features = list(df.columns)[0:-1]
# column in dataframe to bse used as output/target
outputs = list(df.columns)[-1]
model = Model(data=df,
batch_size=16,
lookback=1,
model=mlp_model,
inputs=input_features,
outputs=[outputs],
lr=0.0001)
history = model.fit(indices='random')
y, obs = model.predict()
model.view_model(st=0)
#How to use AI4Water for classification problems
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from AI4Water import Model
data_class = load_breast_cancer()
cols = data_class['feature_names'].tolist() + ['target']
df = pd.DataFrame(np.concatenate(
[data_class['data'], data_class['target'].reshape(-1, 1)], axis=1),
columns=cols)
model = Model(
data=df,
inputs=data_class['feature_names'].tolist(),
outputs=['target'],
val_fraction=0.0,
model={"DecisionTreeClassifier": {
"max_depth": 4,
"random_state": 313
}},
transformation=None,
problem="classification")
h = model.fit()
model.view_model()
