def call(self, inputs):
#unstacked = K.tf.unstack(inputs, axis=1)
unstacked = tf.split(inputs, self.split, axis=1)
W = tf.split(self.kernel, len(unstacked), axis=1)
b = tf.split(self.bias, len(unstacked), axis=0)
#kernels = K.tf.unstack(self.kernels, axis=1)
#biases = K.tf.unstack(self.biases, axis=0)
outputs = []
for i, j in enumerate(unstacked):
output = K.dot(j, W[i])
if self.use_bias:
output = K.bias_add(output, b[i])
if self.activation is not None:
output = self.activation(output)
outputs.append(output)
outputs = K.concatenate(outputs, axis=1)
#outputs = K.stack(outputs, axis=1)
return outputs
def call(self, inputs):
x = tf.split(inputs, self.filters, axis=2)
W = tf.split(self.kernel, self.filters, axis=2)
outputs = []
for i in range(self.filters):
output = K.conv1d(x[i],
W[i],
strides=self.strides[0],
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate[0])
outputs.append(output)
outputs = K.concatenate(outputs, axis=-1)
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
x = tf.split(inputs, 2, axis=2)
y = tf.split(x[0], self.filters, axis=-1)
w = tf.split(x[1], self.filters, axis=-1)
outputs = []
for j in range(self.batch):
outputs_ = []
for i in range(self.filters):
X = y[i][j]
X = tf.reshape(X, (1, X.shape[0], 1))
W = w[i][j][:]
W = tf.reshape(W, (W.shape[0], 1, 1))
output = K.conv1d(X,
W,
strides=self.strides[0],
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate[0])
outputs_.append(output)
outputs_ = K.concatenate(outputs_, axis=-1)
outputs.append(outputs_)
outputs = K.concatenate(outputs, axis=0)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
N = self.input_length
ps = self.pulse_sec
x = tf.split(inputs, 2, axis=2)
sgn = tf.split(x[0], self.input_dim, axis=-1)
idxs = tf.split(x[1], self.input_dim, axis=-1)
t = self.t
output = []
for j in range(self.batch_size):
output_ = []
for i in range(self.input_dim):
s = sgn[i][j][:, 0]
s = tf.reshape(s, [-1])
idx = idxs[i][j]
idx = (N / ps - 1) * idx #
idx = castIntSoftMax(idx)
idx = t + idx
zeros = tf.zeros((ps, 1), dtype=tf.int64)
idx = tf.concat([idx, zeros], axis=-1)
sparse_tensor = tf.SparseTensor(values=s,
indices=idx,
dense_shape=[N, 1])
sparse_tensor = tf.sparse_add(tf.zeros([N, 1]), sparse_tensor)
output_.append(sparse_tensor)
output.append(K.concatenate(output_, axis=1))
output = tf.stack(output)
return output