Python keras.backend 模块，prod() 实例源码
我们从Python开源项目中，提取了以下2个代码示例，用于说明如何使用keras.backend.prod()。
def tf_normal(y, mu, sigma):
oneDivSqrtTwoPI = 1 / math.sqrt(2*math.pi)
result = y - mu
result = K.permute_dimensions(result, [2,1,0])
result = result * (1 / (sigma + 1e-8))
result = -K.square(result)/2
result = K.exp(result) * (1/(sigma + 1e-8))*oneDivSqrtTwoPI
result = K.prod(result, axis=[0])
return result
def call(self, x, mask=None):
input_vector = x[0]
target_classes = x[1]
nb_req_classes = self.input_spec[1].shape[1]
if nb_req_classes is None:
nb_req_classes = K.shape(target_classes)
if K.dtype(target_classes) != 'int32':
target_classes = K.cast(target_classes, 'int32')
if self.mode == 0:
# One giant matrix mul
input_dim = self.input_spec[0].shape[1]
nb_req_classes = self.input_spec[1].shape[1]
path_lengths = map(len, self.paths)
huffman_codes = K.variable(np.array(self.huffman_codes))
req_nodes = K.gather(self.class_path_map, target_classes)
req_W = K.gather(self.W, req_nodes)
y = K.batch_dot(input_vector, req_W, axes=(1, 3))
if self.bias:
req_b = K.gather(self.b, req_nodes)
y += req_b
y = K.sigmoid(y[:, :, 0])
req_huffman_codes = K.gather(huffman_codes, target_classes)
return K.prod(req_huffman_codes + y - 2 * req_huffman_codes * y, axis=-1) # Thug life
elif self.mode == 1:
# Many tiny matrix muls
probs = []
for i in range(len(self.paths)):
huffman_code = self.huffman_codes[i]
path = self.paths[i]
prob = 1.
for j in range(len(path)):
node = path[j]
node_index = self.node_indices[node]
p = K.dot(input_vector, self.W[node_index, :])[:, 0]
if self.bias:
p += self.b[node_index, :][0]
h = huffman_code[j]
p = K.sigmoid(p)
prob *= h + p - 2 * p * h
probs += [prob]
probs = K.pack(probs)
req_probs = K.gather(probs, target_classes)
req_probs = K.permute_dimensions(req_probs, (0, 2, 1))
req_probs = K.reshape(req_probs, (-1, nb_req_classes))
batch_size = K.shape(input_vector)[0]
indices = arange(batch_size * batch_size, batch_size + 1)
req_probs = K.gather(req_probs, indices)
return req_probs
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