def test_convkb_train_predict():
model = ConvKB(batches_count=2,
seed=22,
epochs=1,
k=10,
eta=1,
embedding_model_params={
'num_filters': 16,
'filter_sizes': [1],
'dropout': 0.0,
'is_trainable': True
},
optimizer='adam',
optimizer_params={'lr': 0.001},
loss='pairwise',
loss_params={},
verbose=True)
X = load_wn18()
model.fit(X['train'])
y = model.predict(X['test'][:10])
print(y)
def test_convkb_save_restore():
model = ConvKB(batches_count=2,
seed=22,
epochs=1,
k=10,
eta=1,
embedding_model_params={
'num_filters': 16,
'filter_sizes': [1],
'dropout': 0.0,
'is_trainable': True
},
optimizer='adam',
optimizer_params={'lr': 0.001},
loss='pairwise',
loss_params={},
verbose=True)
X = load_wn18()
model.fit(X['train'])
y1 = model.predict(X['test'][:10])
save_model(model, 'convkb.tmp')
del model
model = restore_model('convkb.tmp')
y2 = model.predict(X['test'][:10])
assert np.all(y1 == y2)
os.remove('convkb.tmp')
def test_convkb_save_restore():
X = np.array([['a', 'y', 'b'], ['b', 'y', 'a'], ['a', 'y', 'c'],
['c', 'y', 'a'], ['a', 'y', 'd'], ['c', 'y', 'd'],
['b', 'y', 'c'], ['f', 'y', 'e']])
X_test = np.array([['f', 'y', 'a'], ['f', 'y', 'b']])
model = ConvKB(batches_count=1,
seed=22,
epochs=1,
k=10,
eta=1,
embedding_model_params={
'num_filters': 16,
'filter_sizes': [1],
'dropout': 0.0,
'is_trainable': True
},
optimizer='adam',
optimizer_params={'lr': 0.001},
loss='pairwise',
loss_params={},
verbose=True)
model.fit(X)
y1 = model.predict(X_test)
save_model(model, 'convkb.tmp')
del model
model = restore_model('convkb.tmp')
y2 = model.predict(X_test)
assert np.all(y1 == y2)
os.remove('convkb.tmp')
for j in range(len(mdl)):
# Fit & Train model via ampliGraph library
log_key = mdl[j]+": "+graph_data[i]
log_file = open("eval_log.txt", "a")
print("\n\n----"+log_key+"----", file=log_file)
print("------------------------------------------------")
print("%d) Implementation Model: %s" % (1, mdl[j]))
print("------------------------------------------------")
start_time = time.time() # START: Training Time Tracker
K.clear_session() # Kills current TF comp-graph & creates a new one
if (mdl[j] == "ComplEx"):
model = ComplEx(verbose=True)
elif (mdl[j] == "ConvKB"):
model = ConvKB(verbose=True)
elif (mdl[j] == "DistMult"):
model = DistMult(verbose=True)
elif (mdl[j] == "HolE"):
model = HolE(verbose=True)
elif (mdl[j] == "TransE"):
model = TransE(verbose=True)
elif (mdl[j] == "RandomBaseline"):
model = RandomBaseline(verbose=True)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # TensorFlow will tell you all messages that have the label ERROR
model.fit(train_X)
# Save model at its best-performance point
save_model(model, 'best_ampliGraph_model.pkl')
del model # Delete older model
# Load recently save best-performance model