def test_load(self):
input = ZLayer.Input(shape=(5, ))
output = ZLayer.Dense(10)(input)
zmodel = ZModel(input, output, name="graph1")
tmp_path = create_tmp_path()
zmodel.saveModel(tmp_path, None, True)
model_reloaded = Net.load(tmp_path)
input_data = np.random.random([3, 5])
self.compare_output_and_grad_input(zmodel, model_reloaded, input_data)
def test_save_load_Model(self):
input = ZLayer.Input(shape=(5, ))
output = ZLayer.Dense(10)(input)
zmodel = ZModel(input, output, name="graph1")
tmp_path = create_tmp_path()
zmodel.saveModel(tmp_path, None, True)
model_reloaded = Net.load(tmp_path)
input_data = np.random.random([10, 5])
y = np.random.random([10, 10])
model_reloaded.compile(optimizer="adam", loss="mse")
model_reloaded.fit(x=input_data, y=y, batch_size=8, nb_epoch=2)
def test_save_load_Sequential(self):
zmodel = ZSequential()
dense = ZLayer.Dense(10, input_dim=5)
zmodel.add(dense)
tmp_path = create_tmp_path()
zmodel.saveModel(tmp_path, None, True)
model_reloaded = Net.load(tmp_path)
input_data = np.random.random([10, 5])
y = np.random.random([10, 10])
model_reloaded.compile(optimizer="adam", loss="mse")
model_reloaded.fit(x=input_data, y=y, batch_size=8, nb_epoch=1)
def load_model(model_path=MODEL_PATH, model_weights_path=MODEL_WEIGHTS_PATH):
model = Net.load(model_path, model_weights_path)
return model