def test_tf_reload(self):
"""Test that tensorflow models can overfit simple classification datasets."""
tasks = ["task0"]
task_types = {task: "classification" for task in tasks}
n_samples = 10
n_features = 3
n_tasks = len(tasks)
n_classes = 2
# Generate dummy dataset
np.random.seed(123)
ids = np.arange(n_samples)
X = np.random.rand(n_samples, n_features)
y = np.random.randint(n_classes, size=(n_samples, n_tasks))
w = np.ones((n_samples, n_tasks))
dataset = Dataset.from_numpy(self.train_dir, X, y, w, ids, tasks)
model_params = {
"layer_sizes": [1000],
"dropouts": [0.0],
"learning_rate": 0.003,
"momentum": 0.9,
"batch_size": n_samples,
"num_classification_tasks": 1,
"num_classes": n_classes,
"num_features": n_features,
"weight_init_stddevs": [1.0],
"bias_init_consts": [1.0],
"nb_epoch": 100,
"penalty": 0.0,
"optimizer": "adam",
"data_shape": dataset.get_data_shape(),
}
verbosity = "high"
classification_metric = Metric(metrics.accuracy_score, verbosity=verbosity)
model = TensorflowModel(
tasks, task_types, model_params, self.model_dir, tf_class=TensorflowMultiTaskClassifier, verbosity=verbosity
)
# Fit trained model
model.fit(dataset)
model.save()
# Load trained model
reloaded_model = TensorflowModel(
tasks, task_types, model_params, self.model_dir, tf_class=TensorflowMultiTaskClassifier, verbosity=verbosity
)
reloaded_model.reload()
assert reloaded_model.eval_model._restored_model
# Eval model on train
transformers = []
evaluator = Evaluator(reloaded_model, dataset, transformers, verbosity=verbosity)
scores = evaluator.compute_model_performance([classification_metric])
assert scores[classification_metric.name] > 0.9
def test_tf_reload(self):
"""Test that tensorflow models can overfit simple classification datasets."""
n_samples = 10
n_features = 3
n_tasks = 1
n_classes = 2
# Generate dummy dataset
np.random.seed(123)
ids = np.arange(n_samples)
X = np.random.rand(n_samples, n_features)
y = np.random.randint(n_classes, size=(n_samples, n_tasks))
w = np.ones((n_samples, n_tasks))
dataset = NumpyDataset(X, y, w, ids)
verbosity = "high"
classification_metric = Metric(metrics.accuracy_score,
verbosity=verbosity)
tensorflow_model = TensorflowMultiTaskClassifier(n_tasks,
n_features,
self.model_dir,
dropouts=[0.],
verbosity=verbosity)
model = TensorflowModel(tensorflow_model, self.model_dir)
# Fit trained model
model.fit(dataset)
model.save()
# Load trained model
reloaded_tensorflow_model = TensorflowMultiTaskClassifier(
n_tasks,
n_features,
self.model_dir,
dropouts=[0.],
verbosity=verbosity)
reloaded_model = TensorflowModel(reloaded_tensorflow_model,
self.model_dir)
reloaded_model.reload()
# Eval model on train
transformers = []
evaluator = Evaluator(reloaded_model,
dataset,
transformers,
verbosity=verbosity)
scores = evaluator.compute_model_performance([classification_metric])
assert scores[classification_metric.name] > .6
def test_tf_reload(self):
"""Test that tensorflow models can overfit simple classification datasets."""
tasks = ["task0"]
task_types = {task: "classification" for task in tasks}
n_samples = 10
n_features = 3
n_tasks = len(tasks)
n_classes = 2
# Generate dummy dataset
np.random.seed(123)
ids = np.arange(n_samples)
X = np.random.rand(n_samples, n_features)
y = np.random.randint(n_classes, size=(n_samples, n_tasks))
w = np.ones((n_samples, n_tasks))
dataset = Dataset.from_numpy(self.train_dir, X, y, w, ids, tasks)
model_params = {
"layer_sizes": [1000],
"dropouts": [0.],
"learning_rate": 0.003,
"momentum": .9,
"batch_size": n_samples,
"num_classification_tasks": 1,
"num_classes": n_classes,
"num_features": n_features,
"weight_init_stddevs": [1.],
"bias_init_consts": [1.],
"nb_epoch": 100,
"penalty": 0.0,
"optimizer": "adam",
"data_shape": dataset.get_data_shape()
}
verbosity = "high"
classification_metric = Metric(metrics.accuracy_score,
verbosity=verbosity)
model = TensorflowModel(tasks,
task_types,
model_params,
self.model_dir,
tf_class=TensorflowMultiTaskClassifier,
verbosity=verbosity)
# Fit trained model
model.fit(dataset)
model.save()
# Load trained model
reloaded_model = TensorflowModel(
tasks,
task_types,
model_params,
self.model_dir,
tf_class=TensorflowMultiTaskClassifier,
verbosity=verbosity)
reloaded_model.reload()
assert reloaded_model.eval_model._restored_model
# Eval model on train
transformers = []
evaluator = Evaluator(reloaded_model,
dataset,
transformers,
verbosity=verbosity)
scores = evaluator.compute_model_performance([classification_metric])
assert scores[classification_metric.name] > .9
