def cervix_model(x_train, y_train, x_val, y_val, params):
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['loss'],
metrics=['acc',
fmeasure,
recall,
precision,
matthews_correlation])
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_fmeasure'))
return results, model
def iris_model(x_train, y_train, x_val, y_val, params):
# 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
def talos_model(x_train, y_train, x_val, y_val, parameters):
model = Sequential()
model.add(Dense(parameters['neurons_HL1'],
input_shape=(train_x.shape[1],),
activation=parameters['activation_1'],use_bias=True))
model.add(Dropout(parameters['dropout1']))
model.add(Dense(parameters['neurons_HL2'],
activation=parameters['activation_2'], use_bias=True))
model.add(Dropout(parameters['dropout1']))
if parameters['neurons_HL3']:
model.add(Dense(parameters['neurons_HL3'],
activation=parameters['activation_3'], use_bias=True))
model.add(Dense(1, activation='relu'))
model.compile(optimizer=parameters['optimizer'], loss=parameters['loss-functions'],
metrics=['mse', 'mae'])
history = model.fit(x_train, y_train,
batch_size=parameters['batch_size'],epochs=parameters['epochs'],
verbose=0,validation_data=[x_val, y_val],
callbacks=[early_stopper(epochs=parameters['epochs'],
mode='moderate',monitor='val_loss', patience=25)])
return history, model
def create_nn2_hyperas(x_train, x_test, y_train, y_test, params):
seq = Sequential()
seq.add(Dense(output_dim=params['n_nodes'], init="uniform", activation="relu", input_dim=11))
seq.add(Dense(output_dim=1, init="uniform", activation="sigmoid"))
seq.compile(optimizer=Adam(lr=params['learning_rate']), loss="binary_crossentropy", metrics=['accuracy']) # vs categorical_crossentropy
epochs = 200
history = seq.fit(x_train, y_train,
verbose=False,
batch_size=params["batch_size"],
epochs=epochs,
validation_data=[x_test, y_test],
callbacks=[early_stopper(epochs, mode=[0,50])]
)
return history, seq
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_correlation_acc, precision_acc
from talos.metrics.keras_metrics import recall_acc, fmeasure_acc
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', fmeasure_acc, recall_acc, precision_acc,
matthews_correlation_acc
])
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_fmeasure')
])
return results, model
