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)