Esempio n. 1
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 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)
Esempio n. 2
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 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)
Esempio n. 3
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 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)
Esempio n. 4
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def load_model(model_path=MODEL_PATH, model_weights_path=MODEL_WEIGHTS_PATH):
    model = Net.load(model_path, model_weights_path)
    return model