def train_triplets_model(self):
X1, X2, X3 = self.load_triplets_data()
n_triplets = len(X1)
print('loaded')
model, inter_model = self.create_triplet_model()
# print(model.summary())
X_anchor, X_pos, X_neg = X1, X2, X3
X = {'anchor_input': X_anchor, 'pos_input': X_pos, 'neg_input': X_neg}
model.fit(X, np.ones((n_triplets, 2)), batch_size=64, epochs=5, shuffle=True, validation_split=0.2)
model_json = model.to_json()
model_dir = join(settings.OUT_DIR, 'model')
os.makedirs(model_dir, exist_ok=True)
with open(join(model_dir, 'model-triplets-{}.json'.format(self.data_scale)), 'w') as wf:
wf.write(model_json)
model.save_weights(join(model_dir, 'model-triplets-{}.h5'.format(self.data_scale)))
test_triplets = self.load_triplets_data(role='test')
auc_score = eval_utils.full_auc(model, test_triplets)
# print('AUC', auc_score)
loaded_model = self.load_triplets_model()
print('triplets model loaded')
auc_score = eval_utils.full_auc(loaded_model, test_triplets)
def evaluate_triplet_model(self):
self.init_read(path="")
test_triplets = self.batch_triplets_data()
loaded_model = self.load_triplets_model()
print('triplets model loaded')
auc_score = eval_utils.full_auc(loaded_model, test_triplets)
print('test AUC', auc_score)
def train_triplets_model(self, train_prop=0.85):
X1, X2, X3 = self.retrieve_data()
n_triplets = len(X1)
n_train = int(n_triplets * train_prop)
self.model, self.inter_model = self.create_triplet_model()
X_anchor, X_pos, X_neg = X1[:n_train], X2[:n_train], X3[:n_train]
X = {'anchor_input': X_anchor, 'pos_input': X_pos, 'neg_input': X_neg}
self.model.fit(X,
np.ones((n_train, 2)),
batch_size=64,
epochs=15,
shuffle=True,
validation_split=0.1)
test_triplets = (X1[n_train:], X2[n_train:], X3[n_train:])
eval_utils.full_auc(self.model, test_triplets)
def evaluate_triplet_model(self):
test_triplets = self.load_triplets_data(role='test')
loaded_model = self.load_triplets_model()
print('triplets model loaded')
auc_score = eval_utils.full_auc(loaded_model, test_triplets)
def train_triplets_model(self, step_learning=False):
if step_learning:
model = self.load_triplets_model()
model.compile(loss=triplet_loss, optimizer=Adam(lr=0.01), metrics=[accuracy])
# read data
self.init_read(self.train_triplets_dir)
chuck_num = 0
for X1, X2, X3 in self.batch_triplets_data():
n_triplets = len(X1)
# print(model.summary())
X_anchor, X_pos, X_neg = X1, X2, X3
X = {'anchor_input': X_anchor, 'pos_input': X_pos, 'neg_input': X_neg}
# 模型训练
print("chunk num:===={}".format(chuck_num))
model.fit(X, np.ones((n_triplets, 2)), batch_size=64, shuffle=True, epochs=20)
chuck_num += 1
model_json = model.to_json()
model_dir = MODEL_DIR
os.makedirs(model_dir, exist_ok=True)
with open(os.path.join(model_dir, '{}.json'.format(NEW_MODEL_NAME)), 'w') as wf:
wf.write(model_json)
model.save_weights(os.path.join(model_dir, '{}.h5'.format(NEW_MODEL_NAME)))
# val
self.init_read(path=self.test_triplets_dir)
test_triplets = self.test_triplets_data()
auc_score = eval_utils.full_auc(model, test_triplets)
print('val AUC', auc_score)
self.init_read(path=self.test_triplets_dir)
loaded_model = self.load_triplets_model()
print('triplets model loaded')
auc_score = eval_utils.full_auc(loaded_model, test_triplets)
print('val AUC', auc_score)
else:
model, inter_model = self.create_triplet_model()
# read data
self.init_read(self.train_triplets_dir)
chuck_num = 0
for X1, X2, X3 in self.batch_triplets_data():
n_triplets = len(X1)
# print(model.summary())
X_anchor, X_pos, X_neg = X1, X2, X3
X = {'anchor_input': X_anchor, 'pos_input': X_pos, 'neg_input': X_neg}
# 模型训练
print("chunk num:===={}".format(chuck_num))
model.fit(X, np.ones((n_triplets, 2)), batch_size=64, shuffle=True, epochs=5)
chuck_num += 1
model_json = model.to_json()
model_dir = MODEL_DIR
os.makedirs(model_dir, exist_ok=True)
with open(os.path.join(model_dir, '{}.json'.format(NEW_MODEL_NAME)), 'w') as wf:
wf.write(model_json)
model.save_weights(os.path.join(model_dir, '{}.h5'.format(NEW_MODEL_NAME)))
# val
self.init_read(path=self.test_triplets_dir)
test_triplets = self.test_triplets_data()
auc_score = eval_utils.full_auc(model, test_triplets)
print('val AUC', auc_score)
self.init_read(path=self.test_triplets_dir)
loaded_model = self.load_triplets_model()
print('triplets model loaded')
auc_score = eval_utils.full_auc(loaded_model, test_triplets)
print('val AUC', auc_score)
