def call(self, inputs):
ternary_kernel = ternarize(self.kernel, H=self.H)
output = K.dot(inputs, ternary_kernel)
if self.use_bias:
output = K.bias_add(output, self.bias)
if self.activation is not None:
output = self.activation(output)
return output
def call(self, x, mask=None):
Wb = ternarize(self.W, H=self.H)
if self.bias:
output = self.activation(K.dot(x, Wb) + self.b)
else:
output = self.activation(K.dot(x, Wb))
return output
def call(self, inputs):
ternary_kernel = ternarize(self.kernel, H=self.H)
outputs = K.conv2d(inputs,
ternary_kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
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, x, mask=None):
Wt = ternarize(self.W, H=self.H)
conv_out = K.conv2d(x,
Wt,
strides=self.subsample,
border_mode=self.border_mode,
dim_ordering=self.dim_ordering,
filter_shape=self.W_shape)
if self.bias:
if self.dim_ordering == 'th':
conv_out = conv_out + K.reshape(self.b,
(1, self.nb_filter, 1, 1))
elif self.dim_ordering == 'tf':
conv_out = conv_out + K.reshape(self.b,
(1, 1, 1, self.nb_filter))
else:
raise ValueError('Invalid dim_ordering: ' +
str(self.dim_ordering))
output = self.activation(conv_out)
return output
def ternary_tanh(x):
x = K.clip(x, -1, 1)
return ternarize(x)
