Python keras.backend.batch_dot() Examples
The following are 30
code examples of keras.backend.batch_dot().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
keras.backend
, or try the search function
.
.
Example #1
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #2
| Source File: capsule.py From CapsNet with MIT License | 6 votes |
|
def call(self, inputs, **kwargs):
# (batch_size, 1, input_num_capsule, input_dim_capsule)
expand_inputs = K.expand_dims(inputs, axis=1)
# (batch_size, num_capsule, input_num_capsule, input_dim_capsule)
expand_inputs = K.tile(expand_inputs, (1, self.num_capsule, 1, 1))
# (batch_size, num_capsule, input_num_capsule, dim_capsule)
u_hat = K.map_fn(lambda x: K.batch_dot(x, self.W, axes=[2, 3]), expand_inputs)
if self.num_routing <= 0:
self.num_routing = 3
# (batch_size, num_capsule, input_num_capsule)
b = K.zeros((K.shape(u_hat)[0], self.num_capsule, self.input_num_capsule))
for i in xrange(self.num_routing):
# (batch_size, num_capsule, input_num_capsule)
c = softmax(b, axis=1)
# (batch_size, num_capsule, dim_capsule)
s = K.batch_dot(c, u_hat, axes=[2, 2])
squashed_s = squash(s)
if i < self.num_routing - 1:
# (batch_size, num_capsule, input_num_capsule)
b += K.batch_dot(squashed_s, u_hat, axes=[2, 3])
return squashed_s
Example #3
| Source File: layers.py From DeepLearn with MIT License | 6 votes |
|
def call(self , x, mask=None):
e1=x[0].T
e2=x[1].T
batch_size = K.shape(x[0])[0]
sim = []
V_out = K.dot(self.V, K.concatenate([e1,e2],axis=0))
for i in range(self.k):
temp = K.batch_dot(K.dot(e1.T,self.W[i,:,:]),e2.T,axes=1)
sim.append(temp)
sim=K.reshape(sim,(self.k,batch_size))
tensor_bi_product = self.activation(V_out+sim)
tensor_bi_product = K.dot(self.U.T, tensor_bi_product).T
return tensor_bi_product
Example #4
| Source File: capsule.py From nlp_research with MIT License | 6 votes |
|
def routing(u_hat_vecs, beta_a, iterations, output_capsule_num, i_activations):
b = keras.backend.zeros_like(u_hat_vecs[:,:,:,0])
if i_activations is not None:
i_activations = i_activations[...,tf.newaxis]
for i in range(iterations):
if False:
leak = tf.zeros_like(b, optimize=True)
leak = tf.reduce_sum(leak, axis=1, keep_dims=True)
leaky_logits = tf.concat([leak, b], axis=1)
leaky_routing = tf.nn.softmax(leaky_logits, dim=1)
c = tf.split(leaky_routing, [1, output_capsule_num], axis=1)[1]
else:
c = softmax(b, 1)
# if i_activations is not None:
# tf.transpose(tf.transpose(c, perm=[0,2,1]) * i_activations, perm=[0,2,1])
outputs = squash_v1(K.batch_dot(c, u_hat_vecs, [2, 2]))
if i < iterations - 1:
b = b + K.batch_dot(outputs, u_hat_vecs, [2, 3])
poses = outputs
activations = K.sqrt(K.sum(K.square(poses), 2))
return poses, activations
Example #5
| Source File: capsulelayers.py From textcaps with MIT License | 6 votes |
|
def call(self, inputs, training=None):
inputs_expand = K.expand_dims(inputs, 1)
inputs_tiled = K.tile(inputs_expand, [1, self.num_capsule, 1, 1])
if(self.channels!=0):
W2 = K.repeat_elements(self.W,int(self.input_num_capsule/self.channels),1)
else:
W2 = self.W
inputs_hat = K.map_fn(lambda x: K.batch_dot(x, W2, [2, 3]) , elems=inputs_tiled)
b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.num_capsule, self.input_num_capsule])
assert self.routings > 0, 'The routings should be > 0.'
for i in range(self.routings):
c = tf.nn.softmax(b, dim=1)
outputs = squash(K.batch_dot(c, inputs_hat, [2, 2])+ self.B)
if i < self.routings - 1:
b += K.batch_dot(outputs, inputs_hat, [2, 3])
return outputs
Example #6
| Source File: models.py From DigiX_HuaWei_Population_Age_Attribution_Predict with MIT License | 6 votes |
|
def call(self, u_vecs):
if self.share_weights:
u_hat_vecs = K.conv1d(u_vecs, self.W)
else:
u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])
batch_size = K.shape(u_vecs)[0]
input_num_capsule = K.shape(u_vecs)[1]
u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
b = K.zeros_like(u_hat_vecs[:, :, :, 0]) # shape = [None, num_capsule, input_num_capsule]
for i in range(self.routings):
b = K.permute_dimensions(b, (0, 2, 1)) # shape = [None, input_num_capsule, num_capsule]
c = K.softmax(b)
c = K.permute_dimensions(c, (0, 2, 1))
b = K.permute_dimensions(b, (0, 2, 1))
outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(outputs, u_hat_vecs, [2, 3])
return outputs
Example #7
| Source File: scale_dot_product_attention.py From Keras-TextClassification with MIT License | 6 votes |
|
def call(self, inputs, mask=None, **kwargs):
if isinstance(inputs, list):
query, key, value = inputs
else:
query = key = value = inputs
if isinstance(mask, list):
mask = mask[1]
feature_dim = K.shape(query)[-1]
e = K.batch_dot(query, key, axes=2) / K.sqrt(K.cast(feature_dim, dtype=K.floatx()))
e = K.exp(e - K.max(e, axis=-1, keepdims=True))
if self.history_only:
query_len, key_len = K.shape(query)[1], K.shape(key)[1]
indices = K.tile(K.expand_dims(K.arange(key_len), axis=0), [query_len, 1])
upper = K.expand_dims(K.arange(key_len), axis=-1)
e *= K.expand_dims(K.cast(indices <= upper, K.floatx()), axis=0)
if mask is not None:
e *= K.cast(K.expand_dims(mask, axis=-2), K.floatx())
a = e / (K.sum(e, axis=-1, keepdims=True) + K.epsilon())
v = K.batch_dot(a, value)
if self.return_attention:
return [v, a]
return v
Example #8
| Source File: multi_dim_attention.py From nlp_toolkit with MIT License | 6 votes |
|
def call(self, x, mask=None):
uit = K.tanh(K.dot(x, self.Ws1))
ait = K.dot(uit, self.Ws2)
ait = K.permute_dimensions(ait, (0, 2, 1))
A = K.softmax(ait, axis=1)
M = K.batch_dot(A, x)
if self.punish:
A_T = K.permute_dimensions(A, (0, 2, 1))
tile_eye = K.tile(K.eye(self.weight_ws2), [self.batch_size, 1])
tile_eye = K.reshape(
tile_eye, shape=[-1, self.weight_ws2, self.weight_ws2])
AA_T = K.batch_dot(A, A_T) - tile_eye
P = K.l2_normalize(AA_T, axis=(1, 2))
return M, P
else:
return M
Example #9
| Source File: capsule.py From Keras-TextClassification with MIT License | 6 votes |
|
def call(self, u_vecs):
if self.share_weights:
u_hat_vecs = K.conv1d(u_vecs, self.W)
else:
u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])
batch_size = K.shape(u_vecs)[0]
input_num_capsule = K.shape(u_vecs)[1]
u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
# final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule]
b = K.zeros_like(u_hat_vecs[:, :, :, 0]) # shape = [None, num_capsule, input_num_capsule]
outputs = None
for i in range(self.routings):
b = K.permute_dimensions(b, (0, 2, 1)) # shape = [None, input_num_capsule, num_capsule]
c = K.softmax(b)
c = K.permute_dimensions(c, (0, 2, 1))
b = K.permute_dimensions(b, (0, 2, 1))
outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(outputs, u_hat_vecs, [2, 3])
return outputs
Example #10
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #11
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #12
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #13
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #14
| Source File: ntm.py From ntm_keras with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def _get_weight_vector(self, M, w_tm1, k, beta, g, s, gamma):
# M = tf.Print(M, [M, w_tm1, k], message='get weights beg1: ')
# M = tf.Print(M, [beta, g, s, gamma], message='get weights beg2: ')
# Content adressing, see Chapter 3.3.1:
num = beta * _cosine_distance(M, k)
w_c = K.softmax(num) # It turns out that equation (5) is just softmax.
# Location adressing, see Chapter 3.3.2:
# Equation 7:
w_g = (g * w_c) + (1-g)*w_tm1
# C_s is the circular convolution
#C_w = K.sum((self.C[None, :, :, :] * w_g[:, None, None, :]),axis=3)
# Equation 8:
# TODO: Explain
C_s = K.sum(K.repeat_elements(self.C[None, :, :, :], self.batch_size, axis=0) * s[:,:,None,None], axis=1)
w_tilda = K.batch_dot(C_s, w_g)
# Equation 9:
w_out = _renorm(w_tilda ** gamma)
return w_out
Example #15
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #16
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #17
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def test_linear_operations(self):
check_two_tensor_operation('dot', (4, 2), (2, 4), BACKENDS)
check_two_tensor_operation('dot', (4, 2), (5, 2, 3), BACKENDS)
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 5, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 2))
check_two_tensor_operation('batch_dot', (4, 2, 3), (4, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(2, 1))
check_two_tensor_operation('batch_dot', (4, 2), (4, 2, 3),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=1)
check_two_tensor_operation('batch_dot', (32, 20), (32, 20),
BACKENDS, cntk_two_dynamicity=True, axes=(1, 1))
check_single_tensor_operation('transpose', (4, 2), BACKENDS)
check_single_tensor_operation('reverse', (4, 3, 2), BACKENDS, axes=1)
check_single_tensor_operation('reverse', (4, 3, 2), [KTH, KTF], axes=(1, 2))
Example #18
| Source File: QnARecurAtteLatest3Attenenhance.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 5 votes |
|
def bd2(inputs):
x,y = inputs
result = K.batch_dot(x,y,axes=[2,1])
return result
Example #19
| Source File: QnARecurAtteLatest3Attenenhance.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 5 votes |
|
def bd(inputs):
x,y = inputs
result = K.batch_dot(x,y,axes=[1,1])
return result
Example #20
| Source File: QnARecurAtteLatest3Attenenhance.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 5 votes |
|
def bd3(inputs):
x,y = inputs
result = K.batch_dot(x,y,axes=[1,2])
return result
Example #21
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_batch_dot_shape(self):
x_batch = K.ones(shape=(32, 20))
y_batch = K.ones(shape=(32, 20))
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=1)
assert_allclose(K.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=0)
assert_allclose(K.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
# making sure swapping axes when ndim == 2 works
x_batch = K.ones(shape=(32, 20))
y_batch = K.ones(shape=(20, 32))
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=(0, 1))
assert_allclose(K.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=(1, 0))
assert_allclose(K.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
Example #22
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_batch_dot_shape(self):
x_batch = K.ones(shape=(32, 20))
y_batch = K.ones(shape=(32, 20))
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=1)
assert_allclose(K.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=0)
assert_allclose(K.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
# making sure swapping axes when ndim == 2 works
x_batch = K.ones(shape=(32, 20))
y_batch = K.ones(shape=(20, 32))
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=(0, 1))
assert_allclose(K.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=(1, 0))
assert_allclose(K.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
Example #23
| Source File: QnARecurAtteLatest3Attenenhance.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 5 votes |
|
def bd4(inputs):
x,y = inputs
result = K.batch_dot(x,y,axes=[2,2])
return result
Example #24
| Source File: cifar10_cnn_capsule.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def call(self, inputs):
"""Following the routing algorithm from Hinton's paper,
but replace b = b + <u,v> with b = <u,v>.
This change can improve the feature representation of Capsule.
However, you can replace
b = K.batch_dot(outputs, hat_inputs, [2, 3])
with
b += K.batch_dot(outputs, hat_inputs, [2, 3])
to realize a standard routing.
"""
if self.share_weights:
hat_inputs = K.conv1d(inputs, self.kernel)
else:
hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])
batch_size = K.shape(inputs)[0]
input_num_capsule = K.shape(inputs)[1]
hat_inputs = K.reshape(hat_inputs,
(batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))
b = K.zeros_like(hat_inputs[:, :, :, 0])
for i in range(self.routings):
c = softmax(b, 1)
if K.backend() == 'theano':
o = K.sum(o, axis=1)
o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(o, hat_inputs, [2, 3])
if K.backend() == 'theano':
o = K.sum(o, axis=1)
return o
Example #25
| Source File: QnARecurAtteLatest.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 5 votes |
|
def bd3(inputs):
x,y = inputs
result = K.batch_dot(x,y,axes=[1,2])
return result
Example #26
| Source File: backend_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_batch_dot_shape(self):
x_batch = K.ones(shape=(32, 20))
y_batch = K.ones(shape=(32, 20))
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=1)
assert_allclose(K.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=0)
assert_allclose(K.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
# making sure swapping axes when ndim == 2 works
x_batch = K.ones(shape=(32, 20))
y_batch = K.ones(shape=(20, 32))
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=(0, 1))
assert_allclose(K.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=(1, 0))
assert_allclose(K.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
Example #27
| Source File: cifar10_cnn_capsule.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def call(self, inputs):
"""Following the routing algorithm from Hinton's paper,
but replace b = b + <u,v> with b = <u,v>.
This change can improve the feature representation of Capsule.
However, you can replace
b = K.batch_dot(outputs, hat_inputs, [2, 3])
with
b += K.batch_dot(outputs, hat_inputs, [2, 3])
to realize a standard routing.
"""
if self.share_weights:
hat_inputs = K.conv1d(inputs, self.kernel)
else:
hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])
batch_size = K.shape(inputs)[0]
input_num_capsule = K.shape(inputs)[1]
hat_inputs = K.reshape(hat_inputs,
(batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))
b = K.zeros_like(hat_inputs[:, :, :, 0])
for i in range(self.routings):
c = softmax(b, 1)
if K.backend() == 'theano':
o = K.sum(o, axis=1)
o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(o, hat_inputs, [2, 3])
if K.backend() == 'theano':
o = K.sum(o, axis=1)
return o
Example #28
| Source File: QnARecurAtteLatest.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 5 votes |
|
def bd2(inputs):
x,y = inputs
result = K.batch_dot(x,y,axes=[2,1])
return result
Example #29
| Source File: cifar10_cnn_capsule.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def call(self, inputs):
"""Following the routing algorithm from Hinton's paper,
but replace b = b + <u,v> with b = <u,v>.
This change can improve the feature representation of Capsule.
However, you can replace
b = K.batch_dot(outputs, hat_inputs, [2, 3])
with
b += K.batch_dot(outputs, hat_inputs, [2, 3])
to realize a standard routing.
"""
if self.share_weights:
hat_inputs = K.conv1d(inputs, self.kernel)
else:
hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])
batch_size = K.shape(inputs)[0]
input_num_capsule = K.shape(inputs)[1]
hat_inputs = K.reshape(hat_inputs,
(batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))
b = K.zeros_like(hat_inputs[:, :, :, 0])
for i in range(self.routings):
c = softmax(b, 1)
if K.backend() == 'theano':
o = K.sum(o, axis=1)
o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(o, hat_inputs, [2, 3])
if K.backend() == 'theano':
o = K.sum(o, axis=1)
return o
Example #30
| Source File: cifar10_cnn_capsule.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def call(self, inputs):
"""Following the routing algorithm from Hinton's paper,
but replace b = b + <u,v> with b = <u,v>.
This change can improve the feature representation of Capsule.
However, you can replace
b = K.batch_dot(outputs, hat_inputs, [2, 3])
with
b += K.batch_dot(outputs, hat_inputs, [2, 3])
to realize a standard routing.
"""
if self.share_weights:
hat_inputs = K.conv1d(inputs, self.kernel)
else:
hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])
batch_size = K.shape(inputs)[0]
input_num_capsule = K.shape(inputs)[1]
hat_inputs = K.reshape(hat_inputs,
(batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))
b = K.zeros_like(hat_inputs[:, :, :, 0])
for i in range(self.routings):
c = softmax(b, 1)
if K.backend() == 'theano':
o = K.sum(o, axis=1)
o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(o, hat_inputs, [2, 3])
if K.backend() == 'theano':
o = K.sum(o, axis=1)
return o
