def test_forward(self):
x = ad.placeholder(shape=(2, 3))
feed_dict = {x: np.random.random((2, 3))}
actual = ad.Session().run(x, feed_dict=feed_dict)
expect = feed_dict[x]
self.assertTrue(np.allclose(expect, actual), (expect, actual))
feed_dict = {x: np.random.random((2, 3))}
actual = ad.Session().run(x, feed_dict=feed_dict)
expect = feed_dict[x]
self.assertTrue(np.allclose(expect, actual), (expect, actual))
def train_model(loss: ad.Operation,
placeholders: list,
variables: list,
config: dict,
verbose=False) -> None:
"""Train the linear model with SGD.
:param loss: Loss operation.
:param placeholders: Placeholders of inputs.
:param variables: Trainable variables.
:param config: Configuration.
:param verbose: Whether to show the losses.
"""
sess = ad.Session()
x, y = placeholders
learning_rate = config['learning_rate']
for step, (batch_x, batch_y) in enumerate(data_generator(config)):
if step > 50000:
break
sess.prepare()
feed_dict = {x: batch_x, y: batch_y}
loss_val = sess.run(loss, feed_dict=feed_dict)
loss.backward()
lr = learning_rate / (1.0 + 1e-5 * step)
for var in variables:
var.update_add(-lr * var.gradient)
if verbose:
print('\rStep %d - Loss %.4f' % (step, loss_val), end='')
if loss_val < 1e-4:
break
if verbose:
print('')
def check_result(model: ad.Operation,
placeholders: list,
config: dict,
verbose=False):
"""Check the trained model.
:param model: The linear model.
:param placeholders: Placeholders of inputs.
:param config: Configuration.
:param verbose: Whether to show the sample outputs.
"""
sess = ad.Session()
x, y_true = placeholders
for batch_x, batch_y in data_generator(config):
sess.prepare()
feed_dict = {x: batch_x, y_true: batch_y}
y_pred_val = sess.run(model, feed_dict=feed_dict)
y_true_cls = np.argmax(batch_y, axis=-1)
y_pred_cls = np.argmax(y_pred_val, axis=-1)
accuracy = (y_true_cls == y_pred_cls).astype(
np.float64).sum() / config['batch_size']
if verbose:
print('Expected: ', y_true_cls[:5])
print('Actual: ', y_pred_cls[:5])
print('Accuracy: ', accuracy)
assert accuracy > 0.8
break
def test_forward(self):
sess = ad.Session()
val = np.random.random((2, 3))
w = ad.variable(val)
actual = sess.run(w)
expect = val
self.assertTrue(np.allclose(expect, actual), (expect, actual))
sess.prepare()
val = np.random.random((2, 3))
w.update(val)
actual = sess.run(w)
expect = val
self.assertTrue(np.allclose(expect, actual), (expect, actual))
def check_result(model: ad.Operation,
placeholders: list,
config: dict,
verbose=False):
"""Check the trained model.
:param model: The linear model.
:param placeholders: Placeholders of inputs.
:param config: Configuration.
:param verbose: Whether to show the sample outputs.
"""
sess = ad.Session()
x, y = placeholders
for batch_x, batch_y in data_generator(config):
sess.prepare()
feed_dict = {x: batch_x, y: batch_y}
y_pred_val = sess.run(model, feed_dict=feed_dict)
if verbose:
print('Expected: ', batch_y[:5])
print('Actual: ', y_pred_val[:5])
assert np.alltrue(batch_y - y_pred_val < 1.0)
break
def test_twice(self):
sess = ad.Session()
op = ad.constant(np.array(1.0))
sess.run(op)
sess.run(op)