Python blocks.bricks.recurrent.SimpleRecurrent() Examples

The following are 3 code examples of blocks.bricks.recurrent.SimpleRecurrent(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module blocks.bricks.recurrent , or try the search function .
Example #1
Source File: test_search.py    From attention-lvcsr with MIT License 6 votes vote down vote up 
def __init__(self, dimension, alphabet_size, **kwargs):
        super(SimpleGenerator, self).__init__(**kwargs)
        lookup = LookupTable(alphabet_size, dimension)
        transition = SimpleRecurrent(
            activation=Tanh(),
            dim=dimension, name="transition")
        attention = SequenceContentAttention(
            state_names=transition.apply.states,
            attended_dim=dimension, match_dim=dimension, name="attention")
        readout = Readout(
            readout_dim=alphabet_size,
            source_names=[transition.apply.states[0],
                          attention.take_glimpses.outputs[0]],
            emitter=SoftmaxEmitter(name="emitter"),
            feedback_brick=LookupFeedback(alphabet_size, dimension),
            name="readout")
        generator = SequenceGenerator(
            readout=readout, transition=transition, attention=attention,
            name="generator")

        self.lookup = lookup
        self.generator = generator
        self.children = [lookup, generator]
Example #2
Source File: model.py    From blocks-char-rnn with MIT License 5 votes vote down vote up 
def rnn_layer(dim, h, n):
    linear = Linear(input_dim=dim, output_dim=dim, name='linear' + str(n))
    rnn = SimpleRecurrent(dim=dim, activation=Tanh(), name='rnn' + str(n))
    initialize([linear, rnn])
    return rnn.apply(linear.apply(h))
Example #3
Source File: __init__.py    From blocks-examples with MIT License 5 votes vote down vote up 
def __init__(self, dimension, alphabet_size, **kwargs):
        super(WordReverser, self).__init__(**kwargs)
        encoder = Bidirectional(
            SimpleRecurrent(dim=dimension, activation=Tanh()))
        fork = Fork([name for name in encoder.prototype.apply.sequences
                    if name != 'mask'])
        fork.input_dim = dimension
        fork.output_dims = [encoder.prototype.get_dim(name) for name in fork.input_names]
        lookup = LookupTable(alphabet_size, dimension)
        transition = SimpleRecurrent(
            activation=Tanh(),
            dim=dimension, name="transition")
        attention = SequenceContentAttention(
            state_names=transition.apply.states,
            attended_dim=2 * dimension, match_dim=dimension, name="attention")
        readout = Readout(
            readout_dim=alphabet_size,
            source_names=[transition.apply.states[0],
                          attention.take_glimpses.outputs[0]],
            emitter=SoftmaxEmitter(name="emitter"),
            feedback_brick=LookupFeedback(alphabet_size, dimension),
            name="readout")
        generator = SequenceGenerator(
            readout=readout, transition=transition, attention=attention,
            name="generator")

        self.lookup = lookup
        self.fork = fork
        self.encoder = encoder
        self.generator = generator
        self.children = [lookup, fork, encoder, generator]