def test_add_metric(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.metrics == ['loss']
tnt_model.add_metric(tnt_model.output,
aggregation='mean',
name='metric_name')
assert len(tnt_model.metrics) == 2
assert tnt_model.metrics_names == ['loss', 'metric_name']
def test_add_metric(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.metrics == []
tnt_model.add_metric(tnt_model.output,
aggregation='mean',
name='metric_name') # deprecated after Tf2.2
assert len(tnt_model.metrics) == 1
assert tnt_model.metrics_names == ['metric_name']
def test_optimizer_with_name(self, optimizer_name, optimizer_type):
tnt_model = tnt.Model(mnist.lenet5_model_generator(),
parallel_strategy=tnt.ParallelStrategy.DATA)
tnt_model.compile(
optimizer=optimizer_name,
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
tnt_optimizer = tnt_model.dist_optimizer
assert isinstance(tnt_optimizer,
tnt.distributed_optimizers.SynchDistributedOptimizer)
assert isinstance(tnt_optimizer.underlying_optimizer, optimizer_type)
def test_metrics_names_after_fit(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
tnt_model.compile(optimizer=tf.keras.optimizers.Adam(),
loss="sparse_categorical_crossentropy",
metrics=["sparse_categorical_accuracy"])
train_dataset, _, _ = util.load_dataset(mnist.load_mnist_dataset,
train_size=24,
train_batch_size=24)
tnt_model.fit(train_dataset)
assert tnt_model.metrics_names == [
"loss", "sparse_categorical_accuracy"
]
def test_reset_metrics(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
tnt_model.compile(optimizer=tf.keras.optimizers.Adam(),
loss="sparse_categorical_crossentropy",
metrics=["sparse_categorical_accuracy"])
train_dataset, _, _ = util.load_dataset(mnist.load_mnist_dataset,
train_size=60,
train_batch_size=60)
tnt_model.fit(train_dataset)
assert all(float(m.result()) != 0 for m in tnt_model.metrics)
tnt_model.reset_metrics()
assert all(float(m.result()) == 0 for m in tnt_model.metrics)
def test_metrics_names(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.metrics_names == []
def test_losses(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.losses == []
tnt_model.add_loss(tf.abs(tnt_model.output))
assert len(tnt_model.losses) == 1
def test_layers(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert len(tnt_model.layers) == 8
assert tnt_model.layers[1].name == 'conv1'
def test_stateful(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.stateful == False
def test_run_eagerly(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.run_eagerly == False
def test_reset_states(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
try:
tnt_model.reset_states()
except Exception as exc:
assert False, f"`tnt_model.reset_states()` raised an exception {exc}"
def test_distribute_strategy(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.distribute_strategy == tf.distribute.get_strategy()
def test_get_layer(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.get_layer(index=0) == tnt_model.layers[0]
assert tnt_model.get_layer(index=0).name == 'input'
assert tnt_model.get_layer(index=6) == tnt_model.layers[6]
def test_compute_output_shape(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
output_shape = tnt_model.compute_output_shape((None, 28, 28, 1))
assert output_shape[0] == None
assert output_shape[1] == 10
def test_non_trainable_weights(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.non_trainable_weights == []
def test_output(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.output.shape[0] == None
assert tnt_model.output.shape[1] == 10
def test_dynamic(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.dynamic == False
def test_state_updates(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.state_updates == []
def test_input_spec(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.input_spec == None
def test_weights(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert len(tnt_model.weights) == 8 # 2 convs, 2 dense + biases
def test_input_spec(self):
tnt_model = tnt.Model(mnist.lenet5_model_generator())
assert tnt_model.input_spec[0].shape == (None, 28, 28, 1)
