def __init__(self,
depth,
filter_size,
hidden_filter_size,
strides,
padding,
bias=blocks_lstm.LSTMBiasInit,
initializer=block_util.RsqrtInitializer(dims=(0, 1, 2)),
name=None):
super(RasterScanConv2DLSTM, self).__init__([None, None, depth], name)
with self._BlockScope():
self._input_conv = blocks_masked_conv2d.RasterScanConv2D(
4 * depth,
filter_size,
strides,
padding,
strict_order=False,
bias=None,
act=None,
initializer=initializer,
name='input_conv2d')
self._hidden_conv = blocks_std.Conv2D(4 * depth,
hidden_filter_size, [1, 1],
'SAME',
bias=None,
act=None,
initializer=initializer,
name='hidden_conv2d')
if bias is not None:
self._bias = blocks_std.BiasAdd(bias, name='biases')
else:
self._bias = blocks_std.PassThrough()
def __init__(self,
depth,
filter_size,
hidden_filter_size,
strides,
padding,
bias=LSTMBiasInit,
initializer=block_util.RsqrtInitializer(dims=(0, 1, 2)),
use_moving_average=False,
name=None):
super(Conv2DLSTM, self).__init__([None, None, depth], name)
self._iter = 0
with self._BlockScope():
self._input_conv = blocks_std.Conv2D(4 * depth,
filter_size,
strides,
padding,
bias=None,
act=None,
initializer=initializer,
name='input_conv2d')
self._hidden_conv = blocks_std.Conv2D(4 * depth,
hidden_filter_size, [1, 1],
'SAME',
bias=None,
act=None,
initializer=initializer,
name='hidden_conv2d')
if bias is not None:
self._bias = blocks_std.BiasAdd(bias, name='biases')
else:
self._bias = blocks_std.PassThrough()
def testPassThrough(self): p = blocks_std.PassThrough() x = tf.placeholder(dtype=tf.float32, shape=[1]) self.assertIs(p(x), x)