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 model(dummyXtrain, dummyYtrain, dummyXval, dummyYval, params):
conv_shape = _create_conv_shape_(params)
model = Sequential()
for i, (depth, size) in enumerate(conv_shape):
if i == 0:
model.add(
Conv2D(depth, size, input_shape=params['input_shape']))
else:
model.add(Conv2D(depth, size))
model.add(Activation('relu'))
model.add(Flatten())
hidden_layers(model, params, params['last_neuron'])
model.add(Dense(4)) #4 is the shape of the data
model.add(Activation('softmax'))
global config
optimizer = config.optimizer(**config.optimizer_parameters)
model.compile(loss=config.loss,
optimizer=optimizer,
metrics=config.metric)
training_set.batch_size = params['batch_size']
validation_set.batch_size = params['batch_size']
model.summary()
n_train = My_Custom_Generator.getNumber(training_set)
n_test = My_Custom_Generator.getNumber(validation_set)
print('number of training images: ', n_train)
print('number of val images: ', n_test)
history = model.fit_generator(
training_set,
validation_data=validation_set,
epochs=params['epoch'],
verbose=int(params['verbose']),
)
#TODO: save model as h5 and history using round as naming index? maybe...
return history, 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_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
def fake_news_model(self, x_train, y_train, x_val, y_val, params):
model = Sequential()
model.add(Dense(10, input_dim=(len(self.HEADERS) - 1),
activation=params['activation'],
kernel_initializer='normal'))
model.add(Dropout(params['dropout']))
hidden_layers(model, params, 1)
model.add(Dense(1, activation=params['last_activation'],
kernel_initializer='normal'))
model.compile(loss=params['losses'],
optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])),
metrics=['acc'])
history = model.fit(x_train, y_train,
validation_data=[x_val, y_val],
batch_size=params['batch_size'],
epochs=params['epochs'],
verbose=0)
return history, model
