def test_generator_model_config(self):
config_dict = {
'bridge': NoOpBridgeConfig().to_schema(),
'encoder': {
'input_layers': ['image'],
'output_layers': ['encoded'],
'layers': [
{'Dense': {'units': 1, 'name': 'encoded'}}
]
},
'decoder': {
'input_layers': ['image'],
'output_layers': ['encoded'],
'layers': [
{'Dense': {'units': 1, 'name': 'decoded'}}
]
},
'loss': MeanSquaredErrorConfig(input_layer=['image', 0, 0],
output_layer=['decoded', 0, 0]).to_schema(),
'optimizer': AdamConfig(learning_rate=0.01).to_schema(),
'metrics': [],
'summaries': ['loss', 'gradients'],
'clip_gradients': 0.5,
'clip_embed_gradients': 0.,
'name': 'model'}
config = GeneratorConfig.from_dict(config_dict)
config_to_dict = config.to_dict()
assert config_dict.pop('bridge') == config_to_dict.pop('bridge')
assert_equal_graphs(config_dict.pop('encoder'), config_to_dict.pop('encoder'))
assert_equal_graphs(config_dict.pop('decoder'), config_to_dict.pop('decoder'))
assert_equal_dict(config_dict, config_to_dict)
def test_graph_config(self):
config_dict = {
'input_layers': ['image'],
'output_layers': [['dense_0', 0, 0]],
'layers': [
{
'Conv2D': {
'filters': 64,
'strides': [1, 1],
'kernel_size': [2, 2],
'activation': 'relu',
'name': 'convolution_1',
}
},
{'Dense': {'units': 17, 'name': 'dense_0'}}
]
}
config = GraphConfig.from_dict(config_dict)
config_to_dict = config.to_dict()
assert_equal_graphs(config_dict, config_to_dict)
def assert_equal_models(result_model, expected_model):
assert_equal_graphs(expected_model.pop('graph'), result_model.pop('graph'))
assert_equal_dict(result_model, expected_model)