def fit(self,
train_X,
val_X,
nb_epoch=50,
batch_size=100,
contractive=None,
optimizer=None,
lr=None,
double=False):
if (double):
model = self._model_double
else:
model = self.autoencoder
if (optimizer == None or optimizer.lower() == "adadelta"):
# optimizer = Adadelta(lr=2.)
optimizer = Adadelta(lr=lr)
elif (optimizer.lower() == "adam"):
optimizer = Adam(lr=lr)
elif (optimizer.lower() == "adagrad"):
optimizer = Adagrad(lr=lr)
if contractive:
print('Using contractive loss, lambda: %s' % contractive)
model.compile(optimizer=optimizer,
loss=contractive_loss(self, contractive))
else:
print('Using binary crossentropy')
model.compile(
optimizer=optimizer,
loss='binary_crossentropy') # kld, binary_crossentropy, mse
# herasCallback = HeraCallback(
# 'model-key',
# 'localhost',
# 4000
# )
history = model.fit(
train_X[0],
train_X[1],
epochs=nb_epoch,
batch_size=batch_size,
shuffle=True,
validation_data=(val_X[0], val_X[1]),
callbacks=[
# herasCallback,
ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=3,
min_lr=0.01),
EarlyStopping(monitor='val_loss',
min_delta=1e-5,
patience=5,
verbose=1,
mode='auto'),
CustomModelCheckpoint(self.encoder,
self.save_model,
monitor='val_loss',
save_best_only=True,
mode='auto')
]).history
n_iters = len(history["loss"])
return self, n_iters
def fit_quadruple(self,
model,
train_data,
val_split,
nb_epoch=50,
batch_size=100,
contractive=None,
optimizer=None,
lr=None):
def loss_structure(y_true, y_pred):
''' Hack: This fn doesn't accept additional arguments. We use y_true to pass them.
y_pred: Contains ys2-ys1
y_true: Contains (yt2-yt1) difference struct vector
'''
min_batch_size = KBack.shape(y_true)[0]
return KBack.sqrt(
KBack.square(
KBack.reshape(KBack.sum(KBack.square(y_pred), axis=-1),
[min_batch_size, 1]) - y_true))
# return KBack.sqrt(KBack.square(KBack.reshape(KBack.sum(KBack.sqrt(KBack.square(y_pred)), axis=-1),[min_batch_size, 1]) - y_true))
# model = self._model_quadruple
train_data_temp = copy.copy(train_data)
train_input = train_data[0]
train_reconstructed = train_data[1]
graph_vector_input = train_data_temp[2]
graph_embedding = np.sum(np.square(graph_vector_input[0] -
graph_vector_input[1]),
axis=-1)
train_output = train_reconstructed + [graph_embedding]
# train_output = train_data_temp[1].append(graph_embedding)
if (optimizer == None or optimizer.lower() == "adadelta"):
# optimizer = Adadelta(lr=2.)
if (lr is None):
optimizer = Adadelta()
else:
optimizer = Adadelta(lr=lr)
elif (optimizer.lower() == "adam"):
if (lr is None):
optimizer = Adam()
else:
optimizer = Adam(lr=lr)
elif (optimizer.lower() == "adagrad"):
if (lr is None):
optimizer = Adagrad()
else:
optimizer = Adagrad(lr=lr)
elif (optimizer.lower() == "rmsprop"):
if (lr is None):
optimizer = RMSprop()
else:
optimizer = RMSprop(lr=lr)
if contractive:
print('Using contractive loss, lambda: %s' % contractive)
model.compile(optimizer=optimizer,
loss=contractive_loss(self, contractive))
else:
print('Using binary crossentropy')
model.compile(optimizer=optimizer,
loss=[
'binary_crossentropy', 'binary_crossentropy',
loss_structure
],
loss_weights=[1., 1., self.alpha
]) # kld, binary_crossentropy, mse
# herasCallback = HeraCallback(
# 'model-key',
# 'localhost',
# 4000
# )
# print(train_data_temp[0])
# print(len(train_input))
# print(len(train_output))
history = model.fit(
train_input,
train_output,
epochs=nb_epoch,
batch_size=batch_size,
shuffle=True,
validation_split=val_split,
callbacks=[
# herasCallback,
ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=3 * 2,
min_lr=0.01),
EarlyStopping(monitor='val_loss',
min_delta=1e-5,
patience=5 * 2,
verbose=1,
mode='auto'),
CustomModelCheckpoint(self.encoder,
self.save_model,
monitor='val_loss',
save_best_only=True,
mode='auto')
]).history
n_iters = len(history["loss"])
return self, history
