Python SequenceGenerator.initialize方法代码示例

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def test_sequence_generator():
    # Disclaimer: here we only check shapes, not values.

    output_dim = 1
    dim = 20
    batch_size = 30
    n_steps = 10

    transition = GatedRecurrent(
        name="transition", activation=Tanh(), dim=dim,
        weights_init=Orthogonal())
    generator = SequenceGenerator(
        LinearReadout(readout_dim=output_dim, source_names=["states"],
                      emitter=TestEmitter(name="emitter"), name="readout"),
        transition,
        weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
        name="generator")
    generator.initialize()

    y = tensor.tensor3('y')
    mask = tensor.matrix('mask')
    costs = generator.cost(y, mask)
    assert costs.ndim == 2
    costs_val = theano.function([y, mask], [costs])(
        numpy.zeros((n_steps, batch_size, output_dim), dtype=floatX),
        numpy.ones((n_steps, batch_size), dtype=floatX))[0]
    assert costs_val.shape == (n_steps, batch_size)

    states, outputs, costs = [variable.eval() for variable in
                              generator.generate(
                                  iterate=True, batch_size=batch_size,
                                  n_steps=n_steps)]
    assert states.shape == (n_steps, batch_size, dim)
    assert outputs.shape == (n_steps, batch_size, output_dim)
    assert costs.shape == (n_steps, batch_size)

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def getRnnGenerator(vocab_size,hidden_dim,input_dim=512):
    """
    "Apply" the RNN to the input x
    For initializing the network, the vocab size needs to be known
    Default of the hidden layer is set tot 512 like Karpathy
    """
    generator = SequenceGenerator(
        Readout(readout_dim = vocab_size,
                source_names = ["states"], # transition.apply.states ???
                emitter = SoftmaxEmitter(name="emitter"),
                feedback_brick = LookupFeedback(
                    vocab_size,
                    input_dim,
                    name = 'feedback'
                ),
                name = "readout"
        ),
        MySimpleRecurrent(
            name = "transition",
            activation = Tanh(),
            dim = hidden_dim
        ),
        weights_init = IsotropicGaussian(0.01),
        biases_init  = Constant(0),
        name = "generator"
    )
    generator.push_initialization_config()
    generator.transition.weights_init = IsotropicGaussian(0.01)
    generator.initialize()
    
    return generator

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def build_model(alphabet_size, config):
    layers = config['lstm_layers']
    dimensions = [config['lstm_dim_' + str(i)] for i in range(layers)]
    uniform_width = config['lstm_init_width']
    stack = []
    for dim in dimensions:
        stack.append(LSTM(dim=dim, use_bias=True, 
                          weights_init = Uniform(width=uniform_width),
                          forget_init=Constant(1.)))
    recurrent_stack = RecurrentStack(stack, name='transition')

    readout = Readout(readout_dim=alphabet_size,
                      source_names=['states#' + str(layers - 1)],
                      emitter=SoftmaxEmitter(name='emitter'),
                      feedback_brick=LookupFeedback(alphabet_size,
                                                    feedback_dim=alphabet_size,
                                                    name='feedback'),
                      name='readout')

    generator = SequenceGenerator(readout=readout,
                                  transition=recurrent_stack,
                                  weights_init=Uniform(width=uniform_width),
                                  biases_init=Constant(0),
                                  name='generator')
    generator.push_initialization_config()
    generator.initialize()

    x = tensor.lmatrix('features')
    mask = tensor.fmatrix('features_mask')
    cost_matrix = generator.cost_matrix(x, mask=mask)

    log2e = math.log(math.e, 2)
    if 'batch_length' in config:
        length = config['batch_length'] - config['batch_overlap']

        cost = log2e * aggregation.mean(cost_matrix[:,-length:].sum(), 
                                    mask[:,-length:].sum())
    else:
        cost = log2e * aggregation.mean(cost_matrix[:,:].sum(), 
                                    mask[:,:].sum())
        
    cost.name = 'bits_per_character'

    return generator, cost

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def test_integer_sequence_generator():
    # Disclaimer: here we only check shapes, not values.

    readout_dim = 5
    feedback_dim = 3
    dim = 20
    batch_size = 30
    n_steps = 10

    transition = GatedRecurrent(
        name="transition", activation=Tanh(), dim=dim,
        weights_init=Orthogonal())
    generator = SequenceGenerator(
        LinearReadout(readout_dim=readout_dim, source_names=["states"],
                      emitter=SoftmaxEmitter(name="emitter"),
                      feedbacker=LookupFeedback(readout_dim, feedback_dim),
                      name="readout"),
        transition,
        weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
        name="generator")
    generator.initialize()

    y = tensor.lmatrix('y')
    mask = tensor.matrix('mask')
    costs = generator.cost(y, mask)
    assert costs.ndim == 2
    costs_val = theano.function([y, mask], [costs])(
        numpy.zeros((n_steps, batch_size), dtype='int64'),
        numpy.ones((n_steps, batch_size), dtype=floatX))[0]
    assert costs_val.shape == (n_steps, batch_size)

    states, outputs, costs = generator.generate(
        iterate=True, batch_size=batch_size, n_steps=n_steps)
    states_val, outputs_val, costs_val = theano.function(
        [], [states, outputs, costs],
        updates=costs.owner.inputs[0].owner.tag.updates)()
    assert states_val.shape == (n_steps, batch_size, dim)
    assert outputs_val.shape == (n_steps, batch_size)
    assert outputs_val.dtype == 'int64'
    assert costs_val.shape == (n_steps, batch_size)

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def test_recurrentstack_sequence_generator():
    """Test RecurrentStack behaviour inside a SequenceGenerator.

    """
    floatX = theano.config.floatX
    rng = numpy.random.RandomState(1234)

    output_dim = 1
    dim = 20
    batch_size = 30
    n_steps = 10

    depth=2
    transitions = [LSTM(dim=dim) for _ in range(depth)]
    transition = RecurrentStack(transitions,fast=True,
                                weights_init=Constant(2),
                                biases_init=Constant(0))
    generator = SequenceGenerator(
        Readout(readout_dim=output_dim, source_names=["states_%d"%(depth-1)],
                emitter=TestEmitter()),
        transition,
        weights_init=IsotropicGaussian(0.1), biases_init=Constant(0.0),
        seed=1234)
    generator.initialize()

    y = tensor.tensor3('y')

    cost = generator.cost(y)

    # Check that all states can be accessed and not just the state connected
    # to readout.
    cg = ComputationGraph(cost)
    from blocks.roles import INPUT, OUTPUT
    dropout_target = VariableFilter(roles=[INNER_OUTPUT],
                                    # bricks=transitions,
                                    # name_regex='*'
                                    )(cg.variables)
    assert_equal(len(dropout_target), depth)

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def test_sequence_generator():
    """Test a sequence generator with no contexts and continuous outputs.

    Such sequence generators can be used to model e.g. dynamical systems.

    """
    rng = numpy.random.RandomState(1234)

    output_dim = 1
    dim = 20
    batch_size = 30
    n_steps = 10

    transition = SimpleRecurrent(activation=Tanh(), dim=dim,
                                 weights_init=Orthogonal())
    generator = SequenceGenerator(
        Readout(readout_dim=output_dim, source_names=["states"],
                emitter=TestEmitter()),
        transition,
        weights_init=IsotropicGaussian(0.1), biases_init=Constant(0.0),
        seed=1234)
    generator.initialize()

    # Test 'cost_matrix' method
    y = tensor.tensor3('y')
    mask = tensor.matrix('mask')
    costs = generator.cost_matrix(y, mask)
    assert costs.ndim == 2
    y_test = rng.uniform(size=(n_steps, batch_size, output_dim)).astype(floatX)
    m_test = numpy.ones((n_steps, batch_size), dtype=floatX)
    costs_val = theano.function([y, mask], [costs])(y_test, m_test)[0]
    assert costs_val.shape == (n_steps, batch_size)
    assert_allclose(costs_val.sum(), 115.593, rtol=1e-5)

    # Test 'cost' method
    cost = generator.cost(y, mask)
    assert cost.ndim == 0
    cost_val = theano.function([y, mask], [cost])(y_test, m_test)
    assert_allclose(cost_val, 3.8531, rtol=1e-5)

    # Test 'AUXILIARY' variable 'per_sequence_element' in 'cost' method
    cg = ComputationGraph([cost])
    var_filter = VariableFilter(roles=[AUXILIARY])
    aux_var_name = '_'.join([generator.name, generator.cost.name,
                             'per_sequence_element'])
    cost_per_el = [el for el in var_filter(cg.variables)
                   if el.name == aux_var_name][0]
    assert cost_per_el.ndim == 0
    cost_per_el_val = theano.function([y, mask], [cost_per_el])(y_test, m_test)
    assert_allclose(cost_per_el_val, 0.38531, rtol=1e-5)

    # Test 'generate' method
    states, outputs, costs = [variable.eval() for variable in
                              generator.generate(
                                  states=rng.uniform(
                                      size=(batch_size, dim)).astype(floatX),
                                  iterate=True, batch_size=batch_size,
                                  n_steps=n_steps)]
    assert states.shape == (n_steps, batch_size, dim)
    assert outputs.shape == (n_steps, batch_size, output_dim)
    assert costs.shape == (n_steps, batch_size)
    assert_allclose(outputs.sum(), -0.33683, rtol=1e-5)
    assert_allclose(states.sum(), 15.7909, rtol=1e-5)
    # There is no generation cost in this case, since generation is
    # deterministic
    assert_allclose(costs.sum(), 0.0)

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def test_with_attention():
    """Test a sequence generator with continuous outputs and attention."""
    rng = numpy.random.RandomState(1234)

    inp_dim = 2
    inp_len = 10
    attended_dim = 3
    attended_len = 11
    batch_size = 4
    n_steps = 30

    # For values
    def rand(size):
        return rng.uniform(size=size).astype(floatX)

    # For masks
    def generate_mask(length, batch_size):
        mask = numpy.ones((length, batch_size), dtype=floatX)
        # To make it look like read data
        for i in range(batch_size):
            mask[1 + rng.randint(0, length - 1):, i] = 0.0
        return mask

    output_vals = rand((inp_len, batch_size, inp_dim))
    output_mask_vals = generate_mask(inp_len, batch_size)
    attended_vals = rand((attended_len, batch_size, attended_dim))
    attended_mask_vals = generate_mask(attended_len, batch_size)

    transition = TestTransition(
        dim=inp_dim, attended_dim=attended_dim, activation=Identity())
    attention = SequenceContentAttention(
        state_names=transition.apply.states, match_dim=inp_dim)
    generator = SequenceGenerator(
        Readout(
            readout_dim=inp_dim,
            source_names=[transition.apply.states[0],
                          attention.take_glimpses.outputs[0]],
            emitter=TestEmitter()),
        transition=transition,
        attention=attention,
        weights_init=IsotropicGaussian(0.1), biases_init=Constant(0),
        add_contexts=False, seed=1234)
    generator.initialize()

    # Test 'cost_matrix' method
    attended = tensor.tensor3("attended")
    attended_mask = tensor.matrix("attended_mask")
    outputs = tensor.tensor3('outputs')
    mask = tensor.matrix('mask')
    costs = generator.cost_matrix(outputs, mask,
                                  attended=attended,
                                  attended_mask=attended_mask)
    costs_vals = costs.eval({outputs: output_vals,
                             mask: output_mask_vals,
                             attended: attended_vals,
                             attended_mask: attended_mask_vals})
    assert costs_vals.shape == (inp_len, batch_size)
    assert_allclose(costs_vals.sum(), 13.5042, rtol=1e-5)

    # Test `generate` method
    results = (
        generator.generate(n_steps=n_steps, batch_size=attended.shape[1],
                           attended=attended, attended_mask=attended_mask))
    assert len(results) == 5
    states_vals, outputs_vals, glimpses_vals, weights_vals, costs_vals = (
        theano.function([attended, attended_mask], results)
        (attended_vals, attended_mask_vals))
    assert states_vals.shape == (n_steps, batch_size, inp_dim)
    assert states_vals.shape == outputs_vals.shape
    assert glimpses_vals.shape == (n_steps, batch_size, attended_dim)
    assert weights_vals.shape == (n_steps, batch_size, attended_len)
    assert costs_vals.shape == (n_steps, batch_size)
    assert_allclose(states_vals.sum(), 23.4172, rtol=1e-5)
    # There is no generation cost in this case, since generation is
    # deterministic
    assert_allclose(costs_vals.sum(), 0.0, rtol=1e-5)
    assert_allclose(weights_vals.sum(), 120.0, rtol=1e-5)
    assert_allclose(glimpses_vals.sum(), 199.2402, rtol=1e-5)
    assert_allclose(outputs_vals.sum(), -11.6008, rtol=1e-5)

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def main():
    logging.basicConfig(
        level=logging.DEBUG,
        format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")

    parser = argparse.ArgumentParser(
        "Case study of language modeling with RNN",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        "mode", choices=["train", "sample"],
        help="The mode to run. Use `train` to train a new model"
             " and `sample` to sample a sequence generated by an"
             " existing one.")
    parser.add_argument(
        "prefix", default="sine",
        help="The prefix for model, timing and state files")
    parser.add_argument(
        "state", nargs="?", default="",
        help="Changes to Groundhog state")
    parser.add_argument("--path", help="Path to a language dataset")
    parser.add_argument("--dict", help="Path to the dataset dictionary")
    parser.add_argument("--restart", help="Start anew")
    parser.add_argument(
        "--reset", action="store_true", default=False,
        help="Reset the hidden state between batches")
    parser.add_argument(
        "--steps", type=int, default=100,
        help="Number of steps to plot for the 'sample' mode"
             " OR training sequence length for the 'train' mode.")
    args = parser.parse_args()
    logger.debug("Args:\n" + str(args))

    dim = 200
    num_chars = 50

    transition = GatedRecurrent(
        name="transition", activation=Tanh(), dim=dim,
        weights_init=Orthogonal())
    generator = SequenceGenerator(
        LinearReadout(readout_dim=num_chars, source_names=["states"],
                      emitter=SoftmaxEmitter(name="emitter"),
                      feedbacker=LookupFeedback(
                          num_chars, dim, name='feedback'),
                      name="readout"),
        transition,
        weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
        name="generator")
    generator.allocate()
    logger.debug("Parameters:\n" +
                 pprint.pformat(
                     [(key, value.get_value().shape) for key, value
                      in Selector(generator).get_params().items()],
                     width=120))

    if args.mode == "train":
        batch_size = 1
        seq_len = args.steps

        generator.initialize()

        # Build cost computation graph that uses the saved hidden states.
        # An issue: for Groundhog this is completely transparent, that's
        # why it does not carry the hidden state over the period when
        # validation in done. We should find a way to fix in the future.
        x = tensor.lmatrix('x')
        init_states = shared_floatx_zeros((batch_size, dim),
                                          name='init_states')
        reset = tensor.scalar('reset')
        cost = ComputationGraph(
            generator.cost(x, states=init_states * reset).sum())
        # TODO: better search routine
        states = [v for v in cost.variables
                  if hasattr(v.tag, 'application_call')
                  and v.tag.application_call.brick == generator.transition
                  and (v.tag.application_call.application ==
                       generator.transition.apply)
                  and v.tag.role == VariableRole.OUTPUT
                  and v.tag.name == 'states']
        assert len(states) == 1
        states = states[0]

        gh_model = GroundhogModel(generator, cost)
        gh_model.properties.append(
            ('bpc', cost.outputs[0] * numpy.log(2) / seq_len))
        gh_model.properties.append(('mean_init_state', init_states.mean()))
        gh_model.properties.append(('reset', reset))
        if not args.reset:
            gh_model.updates.append((init_states, states[-1]))

        state = GroundhogState(args.prefix, batch_size,
                               learning_rate=0.0001).as_dict()
        changes = eval("dict({})".format(args.state))
        state.update(changes)

        def output_format(x, y, reset):
            return dict(x=x[:, None], reset=reset)
        train, valid, test = [
            LMIterator(batch_size=batch_size,
                       use_infinite_loop=mode == 'train',
                       path=args.path,
#.........这里部分代码省略.........

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def main():
    logging.basicConfig(
        level=logging.DEBUG,
        format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")

    parser = argparse.ArgumentParser(
        "Case study of generating a Markov chain with RNN.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        "mode", choices=["train", "sample"],
        help="The mode to run. Use `train` to train a new model"
             " and `sample` to sample a sequence generated by an"
             " existing one.")
    parser.add_argument(
        "prefix", default="sine",
        help="The prefix for model, timing and state files")
    parser.add_argument(
        "--steps", type=int, default=100,
        help="Number of steps to plot")
    args = parser.parse_args()

    dim = 10
    num_states = ChainIterator.num_states
    feedback_dim = 8

    transition = GatedRecurrent(name="transition", activation=Tanh(), dim=dim)
    generator = SequenceGenerator(
        LinearReadout(readout_dim=num_states, source_names=["states"],
                      emitter=SoftmaxEmitter(name="emitter"),
                      feedbacker=LookupFeedback(
                          num_states, feedback_dim, name='feedback'),
                      name="readout"),
        transition,
        weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
        name="generator")
    generator.allocate()
    logger.debug("Parameters:\n" +
                 pprint.pformat(
                     [(key, value.get_value().shape) for key, value
                      in Selector(generator).get_params().items()],
                     width=120))

    if args.mode == "train":
        rng = numpy.random.RandomState(1)
        batch_size = 50

        generator.push_initialization_config()
        transition.weights_init = Orthogonal()
        generator.initialize()
        logger.debug("transition.weights_init={}".format(
            transition.weights_init))

        cost = generator.cost(tensor.lmatrix('x')).sum()
        gh_model = GroundhogModel(generator, cost)
        state = GroundhogState(args.prefix, batch_size,
                               learning_rate=0.0001).as_dict()
        data = ChainIterator(rng, 100, batch_size)
        trainer = SGD(gh_model, state, data)
        main_loop = MainLoop(data, None, None, gh_model, trainer, state, None)
        main_loop.main()
    elif args.mode == "sample":
        load_params(generator,  args.prefix + "model.npz")

        sample = ComputationGraph(generator.generate(
            n_steps=args.steps, batch_size=1, iterate=True)).function()

        states, outputs, costs = [data[:, 0] for data in sample()]

        numpy.set_printoptions(precision=3, suppress=True)
        print("Generation cost:\n{}".format(costs.sum()))

        freqs = numpy.bincount(outputs).astype(floatX)
        freqs /= freqs.sum()
        print("Frequencies:\n {} vs {}".format(freqs,
                                               ChainIterator.equilibrium))

        trans_freqs = numpy.zeros((num_states, num_states), dtype=floatX)
        for a, b in zip(outputs, outputs[1:]):
            trans_freqs[a, b] += 1
        trans_freqs /= trans_freqs.sum(axis=1)[:, None]
        print("Transition frequencies:\n{}\nvs\n{}".format(
            trans_freqs, ChainIterator.trans_prob))
    else:
        assert False

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def main(mode, save_path, steps, num_batches):
    num_states = MarkovChainDataset.num_states

    if mode == "train":
        # Experiment configuration
        rng = numpy.random.RandomState(1)
        batch_size = 50
        seq_len = 100
        dim = 10
        feedback_dim = 8

        # Build the bricks and initialize them
        transition = GatedRecurrent(name="transition", dim=dim,
                                    activation=Tanh())
        generator = SequenceGenerator(
            Readout(readout_dim=num_states, source_names=["states"],
                    emitter=SoftmaxEmitter(name="emitter"),
                    feedback_brick=LookupFeedback(
                        num_states, feedback_dim, name='feedback'),
                    name="readout"),
            transition,
            weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
            name="generator")
        generator.push_initialization_config()
        transition.weights_init = Orthogonal()
        generator.initialize()

        # Give an idea of what's going on.
        logger.info("Parameters:\n" +
                    pprint.pformat(
                        [(key, value.get_value().shape) for key, value
                         in Selector(generator).get_params().items()],
                        width=120))
        logger.info("Markov chain entropy: {}".format(
            MarkovChainDataset.entropy))
        logger.info("Expected min error: {}".format(
            -MarkovChainDataset.entropy * seq_len))

        # Build the cost computation graph.
        x = tensor.lmatrix('data')
        cost = aggregation.mean(generator.cost_matrix(x[:, :]).sum(),
                                x.shape[1])
        cost.name = "sequence_log_likelihood"

        algorithm = GradientDescent(
            cost=cost, params=list(Selector(generator).get_params().values()),
            step_rule=Scale(0.001))
        main_loop = MainLoop(
            algorithm=algorithm,
            data_stream=DataStream(
                MarkovChainDataset(rng, seq_len),
                iteration_scheme=ConstantScheme(batch_size)),
            model=Model(cost),
            extensions=[FinishAfter(after_n_batches=num_batches),
                        TrainingDataMonitoring([cost], prefix="this_step",
                                               after_batch=True),
                        TrainingDataMonitoring([cost], prefix="average",
                                               every_n_batches=100),
                        Checkpoint(save_path, every_n_batches=500),
                        Printing(every_n_batches=100)])
        main_loop.run()
    elif mode == "sample":
        main_loop = cPickle.load(open(save_path, "rb"))
        generator = main_loop.model

        sample = ComputationGraph(generator.generate(
            n_steps=steps, batch_size=1, iterate=True)).get_theano_function()

        states, outputs, costs = [data[:, 0] for data in sample()]

        numpy.set_printoptions(precision=3, suppress=True)
        print("Generation cost:\n{}".format(costs.sum()))

        freqs = numpy.bincount(outputs).astype(theano.config.floatX)
        freqs /= freqs.sum()
        print("Frequencies:\n {} vs {}".format(freqs,
                                               MarkovChainDataset.equilibrium))

        trans_freqs = numpy.zeros((num_states, num_states),
                                  dtype=theano.config.floatX)
        for a, b in zip(outputs, outputs[1:]):
            trans_freqs[a, b] += 1
        trans_freqs /= trans_freqs.sum(axis=1)[:, None]
        print("Transition frequencies:\n{}\nvs\n{}".format(
            trans_freqs, MarkovChainDataset.trans_prob))
    else:
        assert False

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def main():
    logging.basicConfig(
        level=logging.DEBUG,
        format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")

    parser = argparse.ArgumentParser(
        "Case study of generating simple 1d sequences with RNN.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        "mode", choices=["train", "plot"],
        help="The mode to run. Use `train` to train a new model"
             " and `plot` to plot a sequence generated by an"
             " existing one.")
    parser.add_argument(
        "prefix", default="sine",
        help="The prefix for model, timing and state files")
    parser.add_argument(
        "--input-noise", type=float, default=0.0,
        help="Adds Gaussian noise of given intensity to the "
             " training sequences.")
    parser.add_argument(
        "--function", default="lambda a, x: numpy.sin(a * x)",
        help="An analytical description of the sequence family to learn."
             " The arguments before the last one are considered parameters.")
    parser.add_argument(
        "--steps", type=int, default=100,
        help="Number of steps to plot")
    parser.add_argument(
        "--params",
        help="Parameter values for plotting")
    args = parser.parse_args()

    function = eval(args.function)
    num_params = len(inspect.getargspec(function).args) - 1

    class Emitter(TrivialEmitter):
        @application
        def cost(self, readouts, outputs):
            """Compute MSE."""
            return ((readouts - outputs) ** 2).sum(axis=readouts.ndim - 1)

    transition = GatedRecurrent(
        name="transition", activation=Tanh(), dim=10,
        weights_init=Orthogonal())
    with_params = AddParameters(transition, num_params, "params",
                                name="with_params")
    generator = SequenceGenerator(
        LinearReadout(readout_dim=1, source_names=["states"],
                      emitter=Emitter(name="emitter"), name="readout"),
        with_params,
        weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
        name="generator")
    generator.allocate()
    logger.debug("Parameters:\n" +
                 pprint.pformat(
                     [(key, value.get_value().shape) for key, value
                      in Selector(generator).get_params().items()],
                     width=120))

    if args.mode == "train":
        seed = 1
        rng = numpy.random.RandomState(seed)
        batch_size = 10

        generator.initialize()

        cost = ComputationGraph(
            generator.cost(tensor.tensor3('x'),
                           params=tensor.matrix("params")).sum())
        cost = apply_noise(cost, cost.inputs, args.input_noise)

        gh_model = GroundhogModel(generator, cost)
        state = GroundhogState(args.prefix, batch_size,
                               learning_rate=0.0001).as_dict()
        data = SeriesIterator(rng, function, 100, batch_size)
        trainer = SGD(gh_model, state, data)
        main_loop = MainLoop(data, None, None, gh_model, trainer, state, None)
        main_loop.load()
        main_loop.main()
    elif args.mode == "plot":
        load_params(generator,  args.prefix + "model.npz")

        params = tensor.matrix("params")
        sample = theano.function([params], generator.generate(
            params=params, n_steps=args.steps, batch_size=1))

        param_values = numpy.array(map(float, args.params.split()),
                                   dtype=floatX)
        states, outputs, _ = sample(param_values[None, :])
        actual = outputs[:, 0, 0]
        desired = numpy.array([function(*(list(param_values) + [T]))
                               for T in range(args.steps)])
        print("MSE: {}".format(((actual - desired) ** 2).sum()))

        pyplot.plot(numpy.hstack([actual[:, None], desired[:, None]]))
        pyplot.show()
    else:
        assert False

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def train():

    if os.path.isfile('trainingdata.tar'):
        with open('trainingdata.tar', 'rb') as f:
            main = load(f)
    else:
        hidden_size = 512
        filename = 'warpeace.hdf5'

        encoder = HDF5CharEncoder('warpeace_input.txt', 1000)
        encoder.write(filename)
        alphabet_len = encoder.length

        x = theano.tensor.lmatrix('x')

        readout = Readout(
            readout_dim=alphabet_len,
            feedback_brick=LookupFeedback(alphabet_len, hidden_size, name='feedback'),
            source_names=['states'],
            emitter=RandomSoftmaxEmitter(),
            name='readout'
        )

        transition = GatedRecurrent(
            activation=Tanh(),
            dim=hidden_size)
        transition.weights_init = IsotropicGaussian(0.01)

        gen = SequenceGenerator(readout=readout,
                                transition=transition,
                                weights_init=IsotropicGaussian(0.01),
                                biases_init=Constant(0),
                                name='sequencegenerator')

        gen.push_initialization_config()
        gen.initialize()

        cost = gen.cost(outputs=x)
        cost.name = 'cost'

        cg = ComputationGraph(cost)

        algorithm = GradientDescent(cost=cost,
                                    parameters=cg.parameters,
                                    step_rule=Scale(0.5))

        train_set = encoder.get_dataset()
        train_stream = DataStream.default_stream(
            train_set, iteration_scheme=SequentialScheme(
                train_set.num_examples, batch_size=128))

        main = MainLoop(
            model=Model(cost),
            data_stream=train_stream,
            algorithm=algorithm,
            extensions=[
                FinishAfter(),
                Printing(),
                Checkpoint('trainingdata.tar', every_n_epochs=10),
                ShowOutput(every_n_epochs=10)
            ])

    main.run()

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
                  source_names=["states#2"],
                  emitter=SoftmaxEmitter(name="emitter"),
                  feedback_brick=LookupFeedback(alphabet_size,
                                                feedback_dim=alphabet_size,
                                                name="feedback"),
                  name="readout")

seq_gen = SequenceGenerator(readout=readout,
                            transition=rnn,
                            weights_init=IsotropicGaussian(0.01),
                            biases_init=Constant(0),
                            name="generator")

seq_gen.push_initialization_config()
rnn.weights_init = Orthogonal()
seq_gen.initialize()

# z markov_tutorial
x = tensor.lvector('features')
x = x.reshape( (x.shape[0], 1) )
cost = aggregation.mean(seq_gen.cost_matrix(x[:,:]).sum(), x.shape[1])
cost.name = "sequence_log_likelihood"
cost_cg = ComputationGraph(cost)

# theano.printing.pydotprint(cost, outfile="./pics/symbolic_graph_unopt.png", var_with_name_simple=True)

algorithm = GradientDescent(
                cost=cost,
                parameters=list(Selector(seq_gen).get_parameters().values()),
                step_rule=Scale(0.001))

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def main(mode, save_path, steps, time_budget, reset):

    num_states = ChainDataset.num_states

    if mode == "train":
        # Experiment configuration
        rng = numpy.random.RandomState(1)
        batch_size = 50
        seq_len = 100
        dim = 10
        feedback_dim = 8

        # Build the bricks and initialize them
        transition = GatedRecurrent(name="transition", activation=Tanh(),
                                    dim=dim)
        generator = SequenceGenerator(
            LinearReadout(readout_dim=num_states, source_names=["states"],
                          emitter=SoftmaxEmitter(name="emitter"),
                          feedbacker=LookupFeedback(
                              num_states, feedback_dim, name='feedback'),
                          name="readout"),
            transition,
            weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
            name="generator")
        generator.push_initialization_config()
        transition.weights_init = Orthogonal()
        generator.initialize()

        logger.info("Parameters:\n" +
                    pprint.pformat(
                        [(key, value.get_value().shape) for key, value
                         in Selector(generator).get_params().items()],
                        width=120))
        logger.info("Markov chain entropy: {}".format(
            ChainDataset.entropy))
        logger.info("Expected min error: {}".format(
            -ChainDataset.entropy * seq_len * batch_size))

        if os.path.isfile(save_path) and not reset:
            model = Pylearn2Model.load(save_path)
        else:
            model = Pylearn2Model(generator)

        # Build the cost computation graph.
        # Note: would be probably nicer to make cost part of the model.
        x = tensor.ltensor3('x')
        cost = Pylearn2Cost(model.brick.cost(x[:, :, 0]).sum())

        dataset = ChainDataset(rng, seq_len)
        sgd = SGD(learning_rate=0.0001, cost=cost,
                  batch_size=batch_size, batches_per_iter=10,
                  monitoring_dataset=dataset,
                  monitoring_batch_size=batch_size,
                  monitoring_batches=1,
                  learning_rule=Pylearn2LearningRule(
                      SGDLearningRule(),
                      dict(training_objective=cost.cost)))
        train = Pylearn2Train(dataset, model, algorithm=sgd,
                              save_path=save_path, save_freq=10)
        train.main_loop(time_budget=time_budget)
    elif mode == "sample":
        model = Pylearn2Model.load(save_path)
        generator = model.brick

        sample = ComputationGraph(generator.generate(
            n_steps=steps, batch_size=1, iterate=True)).function()

        states, outputs, costs = [data[:, 0] for data in sample()]

        numpy.set_printoptions(precision=3, suppress=True)
        print("Generation cost:\n{}".format(costs.sum()))

        freqs = numpy.bincount(outputs).astype(floatX)
        freqs /= freqs.sum()
        print("Frequencies:\n {} vs {}".format(freqs,
                                               ChainDataset.equilibrium))

        trans_freqs = numpy.zeros((num_states, num_states), dtype=floatX)
        for a, b in zip(outputs, outputs[1:]):
            trans_freqs[a, b] += 1
        trans_freqs /= trans_freqs.sum(axis=1)[:, None]
        print("Transition frequencies:\n{}\nvs\n{}".format(
            trans_freqs, ChainDataset.trans_prob))
    else:
        assert False

# 需要导入模块: from blocks.bricks.sequence_generators import SequenceGenerator [as 别名]
# 或者: from blocks.bricks.sequence_generators.SequenceGenerator import initialize [as 别名]
def test_integer_sequence_generator():
    """Test a sequence generator with integer outputs.

    Such sequence generators can be used to e.g. model language.

    """
    rng = numpy.random.RandomState(1234)

    readout_dim = 5
    feedback_dim = 3
    dim = 20
    batch_size = 30
    n_steps = 10

    transition = GatedRecurrent(dim=dim, activation=Tanh(),
                                weights_init=Orthogonal())
    generator = SequenceGenerator(
        Readout(readout_dim=readout_dim, source_names=["states"],
                emitter=SoftmaxEmitter(theano_seed=1234),
                feedback_brick=LookupFeedback(readout_dim,
                                              feedback_dim)),
        transition,
        weights_init=IsotropicGaussian(0.1), biases_init=Constant(0),
        seed=1234)
    generator.initialize()

    # Test 'cost_matrix' method
    y = tensor.lmatrix('y')
    mask = tensor.matrix('mask')
    costs = generator.cost_matrix(y, mask)
    assert costs.ndim == 2
    costs_fun = theano.function([y, mask], [costs])
    y_test = rng.randint(readout_dim, size=(n_steps, batch_size))
    m_test = numpy.ones((n_steps, batch_size), dtype=floatX)
    costs_val = costs_fun(y_test, m_test)[0]
    assert costs_val.shape == (n_steps, batch_size)
    assert_allclose(costs_val.sum(), 482.827, rtol=1e-5)

    # Test 'cost' method
    cost = generator.cost(y, mask)
    assert cost.ndim == 0
    cost_val = theano.function([y, mask], [cost])(y_test, m_test)
    assert_allclose(cost_val, 16.0942, rtol=1e-5)

    # Test 'AUXILIARY' variable 'per_sequence_element' in 'cost' method
    cg = ComputationGraph([cost])
    var_filter = VariableFilter(roles=[AUXILIARY])
    aux_var_name = '_'.join([generator.name, generator.cost.name,
                             'per_sequence_element'])
    cost_per_el = [el for el in var_filter(cg.variables)
                   if el.name == aux_var_name][0]
    assert cost_per_el.ndim == 0
    cost_per_el_val = theano.function([y, mask], [cost_per_el])(y_test, m_test)
    assert_allclose(cost_per_el_val, 1.60942, rtol=1e-5)

    # Test generate
    states, outputs, costs = generator.generate(
        iterate=True, batch_size=batch_size, n_steps=n_steps)
    cg = ComputationGraph(states + outputs + costs)
    states_val, outputs_val, costs_val = theano.function(
        [], [states, outputs, costs],
        updates=cg.updates)()
    assert states_val.shape == (n_steps, batch_size, dim)
    assert outputs_val.shape == (n_steps, batch_size)
    assert outputs_val.dtype == 'int64'
    assert costs_val.shape == (n_steps, batch_size)
    assert_allclose(states_val.sum(), -17.91811, rtol=1e-5)
    assert_allclose(costs_val.sum(), 482.863, rtol=1e-5)
    assert outputs_val.sum() == 630

    # Test masks agnostic results of cost
    cost1 = costs_fun([[1], [2]], [[1], [1]])[0]
    cost2 = costs_fun([[3, 1], [4, 2], [2, 0]],
                      [[1, 1], [1, 1], [1, 0]])[0]
    assert_allclose(cost1.sum(), cost2[:, 1].sum(), rtol=1e-5)