def test_batch_dot_shape(self):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=0)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
# making sure swapping axes when ndim == 2 works
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(20, 32))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(0, 1))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(1, 0))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
def test_batch_dot_shape(self):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=0)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
# making sure swapping axes when ndim == 2 works
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(20, 32))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(0, 1))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(1, 0))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
def get_weightnorm_params_and_grads(p, g):
ps = K.get_variable_shape(p)
# construct weight scaler: V_scaler = g/||V||
V_scaler_shape = (ps[-1], ) # assumes we're using tensorflow!
V_scaler = K.ones(
V_scaler_shape) # init to ones, so effective parameters don't change
# get V parameters = ||V||/g * W
norm_axes = [i for i in range(len(ps) - 1)]
V = p / tf.reshape(V_scaler, [1] * len(norm_axes) + [-1])
# split V_scaler into ||V|| and g parameters
V_norm = tf.sqrt(tf.reduce_sum(tf.square(V), norm_axes))
g_param = V_scaler * V_norm
# get grad in V,g parameters
grad_g = tf.reduce_sum(g * V, norm_axes) / V_norm
grad_V = tf.reshape(V_scaler, [1] * len(norm_axes) + [-1]) * \
(g - tf.reshape(grad_g / V_norm, [1] * len(norm_axes) + [-1]) * V)
return V, V_norm, V_scaler, g_param, grad_g, grad_V
def test_batch_dot_shape(self):
with pytest.raises(ValueError):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)
def test_batch_dot_shape(self):
with pytest.raises(ValueError):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)
