class Conv1DTranspose(Layer):
def __init__(self, filters, kernel_size, strides=1, *args, **kwargs):
self._filters = filters
self._kernel_size = (1, kernel_size)
self._strides = (1, strides)
self._args, self._kwargs = args, kwargs
super(Conv1DTranspose, self).__init__()
def build(self, input_shape):
#print("build", input_shape)
self._model = Sequential()
self._model.add(
Lambda(lambda x: K.expand_dims(x, axis=1),
batch_input_shape=input_shape))
self._model.add(
Conv2DTranspose(self._filters,
kernel_size=self._kernel_size,
strides=self._strides,
*self._args,
**self._kwargs))
self._model.add(Lambda(lambda x: x[:, 0]))
self._model.summary()
super(Conv1DTranspose, self).build(input_shape)
def call(self, x):
return self._model(x)
def compute_output_shape(self, input_shape):
return self._model.compute_output_shape(input_shape)
class Conv1DTranspose(Layer):
"""Source: https://github.com/tensorflow/tensorflow/issues/6724#issuecomment-357023018
"""
def __init__(self, filters, kernel_size, strides=1, *args, **kwargs):
self._filters = filters
self._kernel_size = (1, kernel_size)
self._strides = (1, strides)
self._args, self._kwargs = args, kwargs
super(Conv1DTranspose, self).__init__()
def build(self, input_shape):
print("build", input_shape)
self._model = Sequential()
self._model.add(Lambda(lambda x: K.expand_dims(x,axis=1), batch_input_shape=input_shape))
self._model.add(Conv2DTranspose(self._filters,
kernel_size=self._kernel_size,
strides=self._strides,
*self._args, **self._kwargs))
self._model.add(Lambda(lambda x: x[:,0]))
self._model.summary()
super(Conv1DTranspose, self).build(input_shape)
def call(self, x):
return self._model(x)
def compute_output_shape(self, input_shape):
return self._model.compute_output_shape(input_shape)