/
models.py
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models.py
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def breast_cancer(x_train, y_train, x_val, y_val, params):
from keras.models import Sequential
from keras.layers import Dropout, Dense
from talos.model import lr_normalizer, early_stopper, hidden_layers
from talos.metrics.keras_metrics import matthews, precision, recall, f1score
model = Sequential()
model.add(Dense(params['first_neuron'],
input_dim=x_train.shape[1],
activation='relu'))
model.add(Dropout(params['dropout']))
hidden_layers(model, params, 1)
model.add(Dense(1, activation=params['last_activation']))
model.compile(optimizer=params['optimizer']
(lr=lr_normalizer(params['lr'],
params['optimizer'])),
loss=params['losses'],
metrics=['acc',
f1score,
recall,
precision,
matthews])
results = model.fit(x_train, y_train,
batch_size=params['batch_size'],
epochs=params['epochs'],
verbose=0,
validation_data=[x_val, y_val],
callbacks=[early_stopper(params['epochs'],
mode='moderate',
monitor='val_f1score')])
return results, model
def cervical_cancer(x_train, y_train, x_val, y_val, params):
from keras.models import Sequential
from keras.layers import Dropout, Dense
from talos.model import lr_normalizer, early_stopper, hidden_layers
from talos.metrics.keras_metrics import matthews, precision, recall, f1score
model = Sequential()
model.add(Dense(params['first_neuron'],
input_dim=x_train.shape[1],
activation='relu'))
model.add(Dropout(params['dropout']))
hidden_layers(model, params, 1)
model.add(Dense(1, activation=params['last_activation']))
model.compile(optimizer=params['optimizer']
(lr=lr_normalizer(params['lr'],
params['optimizer'])),
loss=params['losses'],
metrics=['acc',
f1score,
recall,
precision,
matthews])
results = model.fit(x_train, y_train,
batch_size=params['batch_size'],
epochs=params['epochs'],
verbose=0,
validation_data=[x_val, y_val],
callbacks=[early_stopper(params['epochs'],
mode='moderate',
monitor='val_f1score')])
return results, model
def titanic(x_train, y_train, x_val, y_val, params):
from keras.models import Sequential
from keras.layers import Dropout, Dense
# note how instead of passing the value, we pass a dictionary entry
model = Sequential()
model.add(Dense(params['first_neuron'],
input_dim=x_train.shape[1],
activation='relu'))
# same here, just passing a dictionary entry
model.add(Dropout(params['dropout']))
# again, instead of the activation name, we have a dictionary entry
model.add(Dense(1, activation=params['last_activation']))
# here are using a learning rate boundary
model.compile(optimizer=params['optimizer'],
loss=params['losses'],
metrics=['acc'])
# here we are also using the early_stopper function for a callback
out = model.fit(x_train, y_train,
batch_size=params['batch_size'],
epochs=2,
verbose=0,
validation_data=[x_val, y_val])
return out, model
def iris(x_train, y_train, x_val, y_val, params):
from keras.models import Sequential
from keras.layers import Dropout, Dense
from talos.model import lr_normalizer, early_stopper, hidden_layers
# note how instead of passing the value, we pass a dictionary entry
model = Sequential()
model.add(Dense(params['first_neuron'],
input_dim=x_train.shape[1],
activation='relu'))
# same here, just passing a dictionary entry
model.add(Dropout(params['dropout']))
# with this call we can create any number of hidden layers
hidden_layers(model, params, y_train.shape[1])
# again, instead of the activation name, we have a dictionary entry
model.add(Dense(y_train.shape[1],
activation=params['last_activation']))
# here are using a learning rate boundary
model.compile(optimizer=params['optimizer']
(lr=lr_normalizer(params['lr'],
params['optimizer'])),
loss=params['losses'],
metrics=['acc'])
# here we are also using the early_stopper function for a callback
out = model.fit(x_train, y_train,
batch_size=params['batch_size'],
epochs=params['epochs'],
verbose=0,
validation_data=[x_val, y_val],
callbacks=[early_stopper(params['epochs'], mode=[1, 1])])
return out, model