def load_and_run(model_config, root_dir):
"""An example usage of loading and running a model from the dataset.
Args:
model_config: A ModelConfig object that contains relevant hyperparameters of
a model.
root_dir: Directory containing the dataset
"""
model_path = model_config.get_checkpoint_path(root_dir)
model_fn = model_config.get_model_fn()
with tf.Session() as sess:
input_fn = data_util.get_input(data=model_config.dataset,
data_format=model_config.data_format)
image, _ = input_fn()
logits = model_fn(image, is_training=False)
sess.run(tf.global_variables_initializer())
model_config.load_parameters(model_path, sess)
sess.run(logits)
def evaluate_model(model_config, root_dir):
"""Example for evalutate a model."""
model_path = model_config.get_checkpoint_path(root_dir)
model_fn = model_config.get_model_fn()
with tf.Session() as sess:
input_fn = data_util.get_input(batch_size=500,
data=model_config.dataset,
data_format=model_config.data_format,
mode=tf.estimator.ModeKeys.EVAL)
images, labels = input_fn()
logits = model_fn(images, is_training=False)
predictions = tf.argmax(logits, axis=-1)
true_labels = tf.argmax(labels, axis=-1)
sess.run(tf.global_variables_initializer())
model_config.load_parameters(model_path, sess)
correct_prediction = 0
for _ in range(20):
batch_prediction, batch_label = sess.run(
[predictions, true_labels])
correct_prediction += np.sum(
np.int32(np.equal(batch_prediction, batch_label)))
return correct_prediction / 10000.
results, 'r') as f:
dd = json.load(f)
eval_loss = dd['loss']
eval_cross_entropy = dd['CrossEntropy']
eval_global_step = dd['global_step']
eval_accuracy = dd['Accuracy']
except tf.errors.NotFoundError as e:
print('Failed to load model results')
print(e)
continue
try:
# Other information
input_fn = data_util.get_input(
data_dir,
data=model_config.dataset,
data_format=model_config.
data_format,
repeat_num=1)
all_activations, samples_per_object, layer_names, layer_indices, layer_n_neurons = elu.extract_layers(
input_fn, root_dir, model_config)
except:
print('Failed to load model')
raise
#except tf.errors.InvalidArgumentError:
# failures += [filename]
# print('Failed reading %s'%filename)
# continue
#except tf.errors.NotFoundError:
# failures += [filename]
# print('Failed reading %s'%filename)
# continue
