def test_exp_reverse():
ad.set_mode('reverse')
# =============================================================================
# define the input variable
# =============================================================================
data = np.random.random(5)
x = Variable(data)
# =============================================================================
# define custom block
# =============================================================================
exp_block = exp()
# =============================================================================
# compute output of custom block
# =============================================================================
y_block = exp_block(x)
y_block.compute_gradients()
# =============================================================================
# define expected output
# =============================================================================
data_true = np.exp(data)
gradient_true = np.diag(np.exp(data))
# =============================================================================
# assert data pass
# =============================================================================
assert np.equal(data_true, y_block.data).all(
), 'wrong exp data pass. expected {}, given{}'.format(
data_true, y_block.data)
# =============================================================================
# assert gradient forward pass
# =============================================================================
assert np.equal(gradient_true, y_block.gradient).all(
), 'wrong exp gradient forward pass. expected {}, given{}'.format(
gradient_true, y_block.gradient)
ad.set_mode('forward')
def test_comp_forward():
ad.set_mode('forward')
# =============================================================================
# define the input variable
# =============================================================================
data = np.random.random(5)
x = Variable(data)
# =============================================================================
# define custom block
# =============================================================================
sin_block = sin()
exp_block = exp()
# =============================================================================
# compute output of custom block
# =============================================================================
y_block = sin_block(exp_block(x))
y_block.compute_gradients()
# =============================================================================
# define expected output
# =============================================================================
data_true = np.sin(np.exp(data))
gradient_true = np.exp(data) * np.cos(np.exp(data)) * np.identity(5)
# =============================================================================
# assert data pass
# =============================================================================
assert np.equal(data_true, y_block.data).all(
), 'wrong exp data pass. expected {}, given{}'.format(
data_true, y_block.data)
# =============================================================================
# assert gradient forward pass
# =============================================================================
assert np.equal(gradient_true, y_block.gradient).all(
), 'wrong exp gradient forward pass. expected {}, given{}'.format(
gradient_true, y_block.gradient)
def set_mode(new_mode):
global mode, c_graph
mode = new_mode
if new_mode == 'reverse':
reset_graph()
# =============================================================================
# shortcuts for better user interface
# =============================================================================
sin_ = sin()
cos_ = cos()
tan_ = tan()
exp_ = exp()
log_ = log()
sqrt_ = sqrt()
sinh_ = sinh()
cosh_ = cosh()
tanh_ = tanh()
arcsin_ = arcsin()
arccos_ = arccos()
arctan_ = arctan()
add_ = add()
subtract_ = subtract()
multiply_ = multiply()
divide_ = divide()
power_ = power()
sum_elts_ = sum_elts()
def test_multiple_forward():
"""
assert that the package works well when we use it repetively
"""
ad.set_mode('forward')
# =============================================================================
# define the input variable
# =============================================================================
data = np.random.random(5)
x = Variable(data)
# =============================================================================
# define custom block
# =============================================================================
sin_block = sin()
exp_block = exp()
# =============================================================================
# compute output of custom block
# =============================================================================
y_block = sin_block(exp_block(x))
y_block.compute_gradients()
# =============================================================================
# define expected output
# =============================================================================
data_true = np.sin(np.exp(data))
gradient_true = np.exp(data) * np.cos(np.exp(data)) * np.identity(5)
# =============================================================================
# assert data pass
# =============================================================================
assert np.equal(data_true, y_block.data).all(
), 'wrong exp data pass. expected {}, given{}'.format(
data_true, y_block.data)
# =============================================================================
# assert gradient forward pass
# =============================================================================
assert np.equal(gradient_true, y_block.gradient).all(
), 'wrong exp gradient forward pass. expected {}, given{}'.format(
gradient_true, y_block.gradient)
# =============================================================================
# assert multiple gradient computes work
# =============================================================================
for _ in range(5):
y_block.compute_gradients()
assert np.equal(data_true, y_block.data).all(
), 'wrong exp data pass. expected {}, given{}'.format(
data_true, y_block.data)
assert np.equal(gradient_true, y_block.gradient).all(
), 'wrong exp gradient forward pass. expected {}, given{}'.format(
gradient_true, y_block.gradient)
# =============================================================================
# assert multiple passes work
# =============================================================================
for _ in range(5):
y_block = sin_block(exp_block(x))
y_block.compute_gradients()
assert np.equal(data_true, y_block.data).all(
), 'wrong exp data pass. expected {}, given{}'.format(
data_true, y_block.data)
assert np.equal(gradient_true, y_block.gradient).all(
), 'wrong exp gradient forward pass. expected {}, given{}'.format(
gradient_true, y_block.gradient)
# =============================================================================
# assert multiple definitions work
# =============================================================================
for _ in range(5):
data = np.random.random(5)
x = Variable(data)
sin_block = sin()
exp_block = exp()
y_block = sin_block(exp_block(x))
y_block.compute_gradients()
data_true = np.sin(np.exp(data))
gradient_true = np.exp(data) * np.cos(np.exp(data)) * np.identity(5)
assert np.equal(data_true, y_block.data).all(
), 'wrong exp data pass. expected {}, given{}'.format(
data_true, y_block.data)
assert np.equal(gradient_true, y_block.gradient).all(
), 'wrong exp gradient forward pass. expected {}, given{}'.format(
gradient_true, y_block.gradient)