def test_repeat_elements(self):
reps = 3
for ndims in [1, 2, 3]:
shape = np.arange(2, 2 + ndims)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
for rep_axis in range(ndims):
np_rep = np.repeat(arr, reps, axis=rep_axis)
th_z = KTH.repeat_elements(arr_th, reps, axis=rep_axis)
th_rep = KTH.eval(th_z)
tf_rep = KTF.eval(
KTF.repeat_elements(arr_tf, reps, axis=rep_axis))
assert th_rep.shape == np_rep.shape
assert tf_rep.shape == np_rep.shape
assert_allclose(np_rep, th_rep, atol=1e-05)
assert_allclose(np_rep, tf_rep, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_rep.shape
# test theano shape inference when
# input shape has None entries
if K.backend() == 'theano':
shape = list(shape)
shape[rep_axis] = None
x = K.placeholder(shape=shape)
y = K.repeat_elements(x, reps, axis=rep_axis)
assert y._keras_shape == tuple(shape)
def test_repeat_elements(self):
reps = 3
for ndims in [1, 2, 3]:
shape = np.arange(2, 2 + ndims)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
for rep_axis in range(ndims):
np_rep = np.repeat(arr, reps, axis=rep_axis)
th_z = KTH.repeat_elements(arr_th, reps, axis=rep_axis)
th_rep = KTH.eval(th_z)
tf_rep = KTF.eval(
KTF.repeat_elements(arr_tf, reps, axis=rep_axis))
assert th_rep.shape == np_rep.shape
assert tf_rep.shape == np_rep.shape
assert_allclose(np_rep, th_rep, atol=1e-05)
assert_allclose(np_rep, tf_rep, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_rep.shape
# test theano shape inference when
# input shape has None entries
if K.backend() == 'theano':
shape = list(shape)
shape[rep_axis] = None
x = K.placeholder(shape=shape)
y = K.repeat_elements(x, reps, axis=rep_axis)
assert y._keras_shape == tuple(shape)
def social_attention(self, x):
self.user_embedding = Embedding(input_dim=self.max_user + 1,
output_dim=self.H,
trainable=True,
mask_zero=True)
all_u_i = self.user_embedding(x)
w_v = Dense(1, use_bias=False)
w_x = Dense(self.H, use_bias=False)
u_i = Lambda(lambda xin: xin[:, 0, :])(all_u_i)
e = []
w_all_u_i = []
u_i = w_x(u_i)
for j in range(self.walk_length):
u_j = Lambda(lambda xin: xin[:, j, :])(all_u_i)
u_j = w_x(u_j)
e_i_j = LeakyReLU(alpha=0.3)(w_v(concatenate([u_i, u_j])))
e.append(e_i_j)
w_all_u_i.append(u_j)
e = concatenate(e, axis=-1)
w_all_u_i = Reshape((self.walk_length, self.H))(concatenate(w_all_u_i,
axis=-1))
alpha = Lambda(lambda xin: K.repeat_elements(
K.expand_dims(K.softmax(xin), -1), rep=self.H, axis=-1))(e)
u_f_i = Lambda(lambda xin: K.sum(xin, axis=1))(multiply(
[alpha, w_all_u_i]))
return u_f_i
def personalized_attention(self, u_s, h_i):
dim = h_i.shape[-1]
u_i = Dense(self.H, activation="tanh", use_bias=True)(h_i)
dot_i_s = dot([u_i, u_s], axes=(2, 1))
alpha = Lambda(lambda xin: K.repeat_elements(
K.expand_dims(K.softmax(xin), -1), rep=dim, axis=-1))(dot_i_s)
s = Lambda(lambda xin: K.sum(xin, axis=1))(multiply([alpha, h_i]))
return s
def social_attention(self, x):
self.user_embedding = Embedding(
input_dim=self.max_user + 1,
output_dim=self.H,
trainable=True,
# embeddings_initializer='random_normal',
mask_zero=True)
all_u_i = self.user_embedding(x)
w_v = Dense(1, use_bias=False)
w_1 = Dense(self.H, use_bias=False)
w_2 = Dense(self.H, use_bias=False)
w_p = Dense(self.H, use_bias=False)
w_x = Dense(self.H, use_bias=False)
u_i = Lambda(lambda xin: xin[:, 0, :])(all_u_i)
if self.use_social:
e = []
w_all_u_i = []
u_i = w_x(u_i)
for j in range(self.walk_length):
u_j = Lambda(lambda xin: xin[:, j, :])(all_u_i)
u_j = w_x(u_j)
e_i_j = LeakyReLU(alpha=0.3)(w_v(concatenate([u_i, u_j])))
e.append(e_i_j)
w_all_u_i.append(u_j)
e = concatenate(e, axis=-1)
w_all_u_i = Reshape((self.walk_length, self.H))(concatenate(w_all_u_i, axis=-1))
alpha = Lambda(lambda xin: K.repeat_elements(K.expand_dims(
K.softmax(xin), -1), rep=self.H, axis=-1))(e)
u_f_i = Lambda(lambda xin: K.sum(xin, axis=1))(multiply([alpha, w_all_u_i]))
else:
u_f_i = w_x(u_i)
"""
if self.use_social:
u_i = Lambda(lambda xin: xin[:, 0, :])(u_i_i)
f_i_j = Lambda(lambda xin: xin[:, 1:])(u_i_i)
e_0 = w_v(Activation("tanh")(add([w_1(u_i), w_2(u_i)])))
e = [e_0]
num_max_friends = self.walk_length - 1
for j in range(num_max_friends):
f_i = Lambda(lambda xin: xin[:, j, :])(f_i_j)
e_j = w_v(Activation("tanh")(add([w_1(u_i), w_2(f_i)])))
e.append(e_j)
e_i_j = concatenate(e, axis=-1)
alpha = Lambda(lambda xin: K.repeat_elements(K.expand_dims(
K.softmax(xin), -1), rep=self.H, axis=-1))(e_i_j)
u_f_i = Lambda(lambda xin: K.sum(xin, axis=1))(multiply([alpha, u_i_i]))
"""
# u_f_i = Activation("tanh")(add([w_p(c_i), w_x(u_i)]))
return u_f_i
def test_repeat_elements(self):
reps = 3
for ndims in [1, 2, 3]:
shape = np.arange(2, 2 + ndims)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
for rep_axis in range(ndims):
np_rep = np.repeat(arr, reps, axis=rep_axis)
th_rep = KTH.eval(KTH.repeat_elements(arr_th, reps, axis=rep_axis))
tf_rep = KTF.eval(KTF.repeat_elements(arr_tf, reps, axis=rep_axis))
assert th_rep.shape == np_rep.shape
assert tf_rep.shape == np_rep.shape
assert_allclose(np_rep, th_rep, atol=1e-05)
assert_allclose(np_rep, tf_rep, atol=1e-05)
def test_repeat_elements(self):
reps = 3
for ndims in [1, 2, 3]:
shape = np.arange(2, 2 + ndims)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
for rep_axis in range(ndims):
np_rep = np.repeat(arr, reps, axis=rep_axis)
th_rep = KTH.eval(
KTH.repeat_elements(arr_th, reps, axis=rep_axis))
tf_rep = KTF.eval(
KTF.repeat_elements(arr_tf, reps, axis=rep_axis))
assert th_rep.shape == np_rep.shape
assert tf_rep.shape == np_rep.shape
assert_allclose(np_rep, th_rep, atol=1e-05)
assert_allclose(np_rep, tf_rep, atol=1e-05)
