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Python feedforward.FullyConnectedLayer类代码示例

本文整理汇总了Python中cle.cle.layers.feedforward.FullyConnectedLayer类的典型用法代码示例。如果您正苦于以下问题：Python FullyConnectedLayer类的具体用法？Python FullyConnectedLayer怎么用？Python FullyConnectedLayer使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。


在下文中一共展示了FullyConnectedLayer类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: main

def main(args):

    trial = int(args['trial'])
    pkl_name = 'rnn_gauss_%d' % trial
    channel_name = 'valid_nll'

    data_path = args['data_path']
    save_path = args['save_path']

    monitoring_freq = int(args['monitoring_freq'])
    epoch = int(args['epoch'])
    batch_size = int(args['batch_size'])
    x_dim = int(args['x_dim'])
    z_dim = int(args['z_dim'])
    rnn_dim = int(args['rnn_dim'])
    lr = float(args['lr'])
    debug = int(args['debug'])

    print "trial no. %d" % trial
    print "batch size %d" % batch_size
    print "learning rate %f" % lr
    print "saving pkl file '%s'" % pkl_name
    print "to the save path '%s'" % save_path

    x2s_dim = 340
    s2x_dim = 340
    target_dim = x_dim - 1

    model = Model()
    train_data = IAMOnDB(name='train',
                         prep='normalize',
                         cond=False,
                         path=data_path)

    X_mean = train_data.X_mean
    X_std = train_data.X_std

    valid_data = IAMOnDB(name='valid',
                         prep='normalize',
                         cond=False,
                         path=data_path,
                         X_mean=X_mean,
                         X_std=X_std)

    init_W = InitCell('rand')
    init_U = InitCell('ortho')
    init_b = InitCell('zeros')
    init_b_sig = InitCell('const', mean=0.6)

    x, mask = train_data.theano_vars()

    if debug:
        x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
        temp = np.ones((15, batch_size), dtype=np.float32)
        temp[:, -2:] = 0.
        mask.tag.test_value = temp


    x_1 = FullyConnectedLayer(name='x_1',
                              parent=['x_t'],
                              parent_dim=[x_dim],
                              nout=x2s_dim,
                              unit='relu',
                              init_W=init_W,
                              init_b=init_b)

    rnn = LSTM(name='rnn',
               parent=['x_1'],
               parent_dim=[x2s_dim],
               nout=rnn_dim,
               unit='tanh',
               init_W=init_W,
               init_U=init_U,
               init_b=init_b)

    theta_1 = FullyConnectedLayer(name='theta_1',
                                  parent=['s_tm1'],
                                  parent_dim=[rnn_dim],
                                  nout=s2x_dim,
                                  unit='relu',
                                  init_W=init_W,
                                  init_b=init_b)

    theta_mu = FullyConnectedLayer(name='theta_mu',
                                   parent=['theta_1'],
                                   parent_dim=[s2x_dim],
                                   nout=target_dim,
                                   unit='linear',
                                   init_W=init_W,
                                   init_b=init_b)

    theta_sig = FullyConnectedLayer(name='theta_sig',
                                    parent=['theta_1'],
                                    parent_dim=[s2x_dim],
                                    nout=target_dim,
                                    unit='softplus',
                                    cons=1e-4,
                                    init_W=init_W,
                                    init_b=init_b_sig)

#.........这里部分代码省略.........
开发者ID:xzhang311，项目名称:nips2015_vrnn，代码行数:101，代码来源:rnn_gauss.py

示例2: main

def main(args):

    trial = int(args['trial'])
    pkl_name = 'vrnn_gmm_%d' % trial
    channel_name = 'valid_nll_upper_bound'

    data_path = args['data_path']
    save_path = args['save_path']

    monitoring_freq = int(args['monitoring_freq'])
    force_saving_freq = int(args['force_saving_freq'])
    reset_freq = int(args['reset_freq'])
    epoch = int(args['epoch'])
    batch_size = int(args['batch_size'])
    m_batch_size = int(args['m_batch_size'])
    x_dim = int(args['x_dim'])
    z_dim = int(args['z_dim'])
    rnn_dim = int(args['rnn_dim'])
    k = int(args['num_k'])
    lr = float(args['lr'])
    debug = int(args['debug'])

    print "trial no. %d" % trial
    print "batch size %d" % batch_size
    print "learning rate %f" % lr
    print "saving pkl file '%s'" % pkl_name
    print "to the save path '%s'" % save_path

    q_z_dim = 500
    p_z_dim = 500
    p_x_dim = 500
    x2s_dim = 500
    z2s_dim = 500
    target_dim = x_dim * k

    file_name = 'blizzard_unseg_tbptt'
    normal_params = np.load(data_path + file_name + '_normal.npz')
    X_mean = normal_params['X_mean']
    X_std = normal_params['X_std']

    model = Model()
    train_data = Blizzard_tbptt(name='train',
                                path=data_path,
                                frame_size=x_dim,
                                file_name=file_name,
                                X_mean=X_mean,
                                X_std=X_std)

    valid_data = Blizzard_tbptt(name='valid',
                                path=data_path,
                                frame_size=x_dim,
                                file_name=file_name,
                                X_mean=X_mean,
                                X_std=X_std)

    x = train_data.theano_vars()
    m_x = valid_data.theano_vars()

    if debug:
        x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=theano.config.floatX)
        m_x.tag.test_value = np.zeros((15, m_batch_size, x_dim), dtype=theano.config.floatX)

    init_W = InitCell('rand')
    init_U = InitCell('ortho')
    init_b = InitCell('zeros')
    init_b_sig = InitCell('const', mean=0.6)

    x_1 = FullyConnectedLayer(name='x_1',
                              parent=['x_t'],
                              parent_dim=[x_dim],
                              nout=x2s_dim,
                              unit='relu',
                              init_W=init_W,
                              init_b=init_b)

    x_2 = FullyConnectedLayer(name='x_2',
                              parent=['x_1'],
                              parent_dim=[x2s_dim],
                              nout=x2s_dim,
                              unit='relu',
                              init_W=init_W,
                              init_b=init_b)

    x_3 = FullyConnectedLayer(name='x_3',
                              parent=['x_2'],
                              parent_dim=[x2s_dim],
                              nout=x2s_dim,
                              unit='relu',
                              init_W=init_W,
                              init_b=init_b)

    x_4 = FullyConnectedLayer(name='x_4',
                              parent=['x_3'],
                              parent_dim=[x2s_dim],
                              nout=x2s_dim,
                              unit='relu',
                              init_W=init_W,
                              init_b=init_b)

    z_1 = FullyConnectedLayer(name='z_1',
#.........这里部分代码省略.........
开发者ID:kastnerkyle，项目名称:nips2015_vrnn，代码行数:101，代码来源:vrnn_gmm.py

示例3: InitCell

init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)

x = train_data.theano_vars()
mn_x = valid_data.theano_vars()
if debug:
    x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=theano.config.floatX)
    mn_x.tag.test_value = np.zeros((15, mn_batch_size, frame_size), dtype=theano.config.floatX)

x_1 = FullyConnectedLayer(name='x_1',
                          parent=['x_t'],
                          parent_dim=[frame_size],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_2 = FullyConnectedLayer(name='x_2',
                          parent=['x_1'],
                          parent_dim=[x2s_dim],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_3 = FullyConnectedLayer(name='x_3',
                          parent=['x_2'],
                          parent_dim=[x2s_dim],
开发者ID:anirudh9119，项目名称:SpeechSyn，代码行数:30，代码来源:m2.py

示例4: main

def main(args):

    trial = int(args["trial"])
    pkl_name = "vrnn_gauss_%d" % trial
    channel_name = "valid_nll_upper_bound"

    data_path = args["data_path"]
    save_path = args["save_path"]

    monitoring_freq = int(args["monitoring_freq"])
    epoch = int(args["epoch"])
    batch_size = int(args["batch_size"])
    x_dim = int(args["x_dim"])
    z_dim = int(args["z_dim"])
    rnn_dim = int(args["rnn_dim"])
    lr = float(args["lr"])
    debug = int(args["debug"])

    print "trial no. %d" % trial
    print "batch size %d" % batch_size
    print "learning rate %f" % lr
    print "saving pkl file '%s'" % pkl_name
    print "to the save path '%s'" % save_path

    q_z_dim = 150
    p_z_dim = 150
    p_x_dim = 250
    x2s_dim = 250
    z2s_dim = 150
    target_dim = x_dim - 1

    model = Model()
    train_data = IAMOnDB(name="train", prep="normalize", cond=False, path=data_path)

    X_mean = train_data.X_mean
    X_std = train_data.X_std

    valid_data = IAMOnDB(name="valid", prep="normalize", cond=False, path=data_path, X_mean=X_mean, X_std=X_std)

    init_W = InitCell("rand")
    init_U = InitCell("ortho")
    init_b = InitCell("zeros")
    init_b_sig = InitCell("const", mean=0.6)

    x, mask = train_data.theano_vars()

    if debug:
        x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
        temp = np.ones((15, batch_size), dtype=np.float32)
        temp[:, -2:] = 0.0
        mask.tag.test_value = temp

    x_1 = FullyConnectedLayer(
        name="x_1", parent=["x_t"], parent_dim=[x_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    z_1 = FullyConnectedLayer(
        name="z_1", parent=["z_t"], parent_dim=[z_dim], nout=z2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    rnn = LSTM(
        name="rnn",
        parent=["x_1", "z_1"],
        parent_dim=[x2s_dim, z2s_dim],
        nout=rnn_dim,
        unit="tanh",
        init_W=init_W,
        init_U=init_U,
        init_b=init_b,
    )

    phi_1 = FullyConnectedLayer(
        name="phi_1",
        parent=["x_1", "s_tm1"],
        parent_dim=[x2s_dim, rnn_dim],
        nout=q_z_dim,
        unit="relu",
        init_W=init_W,
        init_b=init_b,
    )

    phi_mu = FullyConnectedLayer(
        name="phi_mu", parent=["phi_1"], parent_dim=[q_z_dim], nout=z_dim, unit="linear", init_W=init_W, init_b=init_b
    )

    phi_sig = FullyConnectedLayer(
        name="phi_sig",
        parent=["phi_1"],
        parent_dim=[q_z_dim],
        nout=z_dim,
        unit="softplus",
        cons=1e-4,
        init_W=init_W,
        init_b=init_b_sig,
    )

    prior_1 = FullyConnectedLayer(
        name="prior_1", parent=["s_tm1"], parent_dim=[rnn_dim], nout=p_z_dim, unit="relu", init_W=init_W, init_b=init_b
    )

#.........这里部分代码省略.........
开发者ID:vseledkin，项目名称:nips2015_vrnn，代码行数:101，代码来源:vrnn_gauss.py

示例5: InitCell

init_b = InitCell('zeros')

# Define nodes: objects
x, y = train_data.theano_vars()
mn_x, mn_y = valid_data.theano_vars()
# You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb
if debug:
    x.tag.test_value = np.zeros((batch_size, 784), dtype=np.float32)
    y.tag.test_value = np.zeros((batch_size, 1), dtype=np.float32)
    mn_x.tag.test_value = np.zeros((batch_size, 784), dtype=np.float32)
    mn_y.tag.test_value = np.zeros((batch_size, 1), dtype=np.float32)

h1 = FullyConnectedLayer(name='h1',
                         parent=['x'],
                         parent_dim=[784],
                         nout=1000,
                         unit='relu',
                         init_W=init_W,
                         init_b=init_b)

d1 = DropoutLayer(name='d1', parent=['h1'], nout=1000)

h2 = FullyConnectedLayer(name='h2',
                         parent=['d1'],
                         parent_dim=[1000],
                         nout=1000,
                         unit='relu',
                         init_W=init_W,
                         init_b=init_b)

d2 = DropoutLayer(name='d2', parent=['h2'], nout=1000)
开发者ID:npow，项目名称:cle，代码行数:31，代码来源:mnist_dropout.py

示例6: InitCell

                            X_std=X_std)

x = train_data.theano_vars()

if debug:
    x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=theano.config.floatX)

init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)

x_1 = FullyConnectedLayer(name='x_1',
                          parent=['x_t'],
                          parent_dim=[frame_size],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_2 = FullyConnectedLayer(name='x_2',
                          parent=['x_1'],
                          parent_dim=[x2s_dim],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_3 = FullyConnectedLayer(name='x_3',
                          parent=['x_2'],
                          parent_dim=[x2s_dim],
开发者ID:LEONOB2014，项目名称:nips2015_vrnn，代码行数:31，代码来源:vrnn_gmm.py

示例7: PriorLayer

                   parent=['phi_mu', 'phi_sig'],
                   parent_dim=[latent_size, latent_size],
                   use_sample=1,
                   num_sample=1,
                   nout=latent_size)

kl = PriorLayer(name='kl',
                parent=['phi_mu', 'phi_sig'],
                parent_dim=[latent_size, latent_size],
                use_sample=0,
                nout=latent_size)

theta_mu = FullyConnectedLayer(name='theta_mu',
                               parent=['dec_t'],
                               parent_dim=[decoder_dim],
                               nout=target_size,
                               unit='linear',
                               init_W=init_W,
                               init_b=init_b)

theta_sig = FullyConnectedLayer(name='theta_sig',
                                parent=['dec_t'],
                                parent_dim=[decoder_dim],
                                nout=target_size,
                                unit='softplus',
                                cons=1e-4,
                                init_W=init_W,
                                init_b=init_b_sig)

nodes = [encoder, decoder, prior, kl,
         phi_mu, phi_sig, theta_mu, theta_sig]
开发者ID:anirudh9119，项目名称:SpeechSyn，代码行数:31，代码来源:storn0_orig.py

示例8: InitCell

init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
#init_b_sig = InitCell('const', mean=0.6)
init_b_sig = InitCell('const')

x = train_data.theano_vars()
mn_x = valid_data.theano_vars()
if debug:
    x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=np.float32)
    mn_x.tag.test_value = np.zeros((15, mn_batch_size, frame_size), dtype=np.float32)

x_1 = FullyConnectedLayer(name='x_1',
                          parent=['x_t'],
                          parent_dim=[frame_size],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_2 = FullyConnectedLayer(name='x_2',
                          parent=['x_1'],
                          parent_dim=[x2s_dim],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_3 = FullyConnectedLayer(name='x_3',
                          parent=['x_2'],
                          parent_dim=[x2s_dim],
开发者ID:anirudh9119，项目名称:SpeechSyn，代码行数:31，代码来源:deep_m3.py

示例9: InitCell

init_W = InitCell("rand")
init_U = InitCell("ortho")
init_b = InitCell("zeros")
init_b_sig = InitCell("const", mean=0.6)

x, x_mask = train_data.theano_vars()
if debug:
    x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=np.float32)
    temp = np.ones((15, batch_size), dtype=np.float32)
    temp[:, -2:] = 0.0
    x_mask.tag.test_value = temp
x_tm1 = T.concatenate([T.zeros((1, x.shape[1], x.shape[2])), x[:-1]], axis=0)
x_tm1.name = "x_tm1"

x_1 = FullyConnectedLayer(
    name="x_1", parent=["x_t"], parent_dim=[frame_size], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
)

x_2 = FullyConnectedLayer(
    name="x_2", parent=["x_1"], parent_dim=[x2s_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
)

x_3 = FullyConnectedLayer(
    name="x_3", parent=["x_2"], parent_dim=[x2s_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
)

x_4 = FullyConnectedLayer(
    name="x_4", parent=["x_3"], parent_dim=[x2s_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
)

z_1 = FullyConnectedLayer(
开发者ID:anirudh9119，项目名称:SpeechSyn，代码行数:31，代码来源:storn0_2.py

示例10: InitCell

# Define nodes: objects
x, y = train_data.theano_vars()

# You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb
if debug:
    x.tag.test_value = np.zeros((batch_size, 784), dtype=np.float32)
    y.tag.test_value = np.zeros((batch_size, 1), dtype=np.float32)

# Choose the random initialization method
init_W = InitCell('rand')
init_b = InitCell('zeros')

h1 = FullyConnectedLayer(name='h1',
                         parent=['x'],
                         parent_dim=[784],
                         nout=1000,
                         unit='relu',
                         init_W=init_W,
                         init_b=init_b)

output = FullyConnectedLayer(name='output',
                             parent=['h1'],
                             parent_dim=[1000],
                             nout=10,
                             unit='softmax',
                             init_W=init_W,
                             init_b=init_b)


# You will fill in a list of nodes
nodes = [h1, output]
开发者ID:Beronx86，项目名称:cle，代码行数:31，代码来源:mnist_dropout.py

示例11: InitCell

batch_size = mn_batch_size = 2

init_W = InitCell('rand', low=-0.5, high=0.5)
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)

x = T.tensor3('x', dtype=theano.config.floatX)

x.tag.test_value = np.random.rand(2, batch_size, frame_size).astype(theano.config.floatX)

epsilonij = 0.0001
x_1 = FullyConnectedLayer(name='x_1',
                          parent=['x'],
                          parent_dim=[frame_size],
                          nout=150,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

theta_mu = FullyConnectedLayer(name='theta_mu',
                               parent=['x_1'],
                               parent_dim=[150],
                               nout=200,
                               unit='linear',
                               init_W=init_W,
                               init_b=init_b)

nodes = [x_1, theta_mu]

for node in nodes:
开发者ID:soroushmehr，项目名称:BP-FFT，代码行数:31，代码来源:test_cufft.py

示例12: main

def main(args):

    trial = int(args["trial"])
    pkl_name = "vrnn_gauss_%d" % trial
    channel_name = "valid_nll_upper_bound"

    data_path = args["data_path"]
    save_path = args["save_path"]
    data_path = os.path.expanduser(args["data_path"])
    save_path = os.path.expanduser(args["save_path"])
    monitoring_freq = int(args["monitoring_freq"])
    force_saving_freq = int(args["force_saving_freq"])
    reset_freq = int(args["reset_freq"])
    epoch = int(args["epoch"])
    batch_size = int(args["batch_size"])
    m_batch_size = int(args["m_batch_size"])
    x_dim = int(args["x_dim"])
    z_dim = int(args["z_dim"])
    rnn_dim = int(args["rnn_dim"])
    lr = float(args["lr"])
    debug = int(args["debug"])

    print "trial no. %d" % trial
    print "batch size %d" % batch_size
    print "learning rate %f" % lr
    print "saving pkl file '%s'" % pkl_name
    print "to the save path '%s'" % save_path

    q_z_dim = 500
    p_z_dim = 500
    p_x_dim = 600
    x2s_dim = 600
    z2s_dim = 500
    target_dim = x_dim

    file_name = "blizzard_tbptt"
    normal_params = np.load(data_path + file_name + "_normal.npz")
    X_mean = normal_params["X_mean"]
    X_std = normal_params["X_std"]

    model = Model()
    train_data = Blizzard_tbptt(
        name="train", path=data_path, frame_size=x_dim, file_name=file_name, X_mean=X_mean, X_std=X_std
    )

    valid_data = Blizzard_tbptt(
        name="valid", path=data_path, frame_size=x_dim, file_name=file_name, X_mean=X_mean, X_std=X_std
    )

    x = train_data.theano_vars()
    m_x = valid_data.theano_vars()

    if debug:
        x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=theano.config.floatX)
        m_x.tag.test_value = np.zeros((15, m_batch_size, x_dim), dtype=theano.config.floatX)

    init_W = InitCell("rand")
    init_U = InitCell("ortho")
    init_b = InitCell("zeros")
    init_b_sig = InitCell("const", mean=0.6)

    x_1 = FullyConnectedLayer(
        name="x_1", parent=["x_t"], parent_dim=[x_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    x_2 = FullyConnectedLayer(
        name="x_2", parent=["x_1"], parent_dim=[x2s_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    x_3 = FullyConnectedLayer(
        name="x_3", parent=["x_2"], parent_dim=[x2s_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    x_4 = FullyConnectedLayer(
        name="x_4", parent=["x_3"], parent_dim=[x2s_dim], nout=x2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    z_1 = FullyConnectedLayer(
        name="z_1", parent=["z_t"], parent_dim=[z_dim], nout=z2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    z_2 = FullyConnectedLayer(
        name="z_2", parent=["z_1"], parent_dim=[z2s_dim], nout=z2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    z_3 = FullyConnectedLayer(
        name="z_3", parent=["z_2"], parent_dim=[z2s_dim], nout=z2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    z_4 = FullyConnectedLayer(
        name="z_4", parent=["z_3"], parent_dim=[z2s_dim], nout=z2s_dim, unit="relu", init_W=init_W, init_b=init_b
    )

    rnn = LSTM(
        name="rnn",
        parent=["x_4", "z_4"],
        parent_dim=[x2s_dim, z2s_dim],
        nout=rnn_dim,
        unit="tanh",
        init_W=init_W,
#.........这里部分代码省略.........
开发者ID:szcom，项目名称:nips2015_vrnn，代码行数:101，代码来源:vrnn_gauss_alt_nll.py

示例13: InitCell

init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)

x, x_mask = train_data.theano_vars()
if debug:
    x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=np.float32)
    temp = np.ones((15, batch_size), dtype=np.float32)
    temp[:, -2:] = 0.
    x_mask.tag.test_value = temp

x_1 = FullyConnectedLayer(name='x_1',
                          parent=['x_t'],
                          parent_dim=[frame_size],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_2 = FullyConnectedLayer(name='x_2',
                          parent=['x_1'],
                          parent_dim=[x2s_dim],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_3 = FullyConnectedLayer(name='x_3',
                          parent=['x_2'],
                          parent_dim=[x2s_dim],
开发者ID:anirudh9119，项目名称:SpeechSyn，代码行数:31，代码来源:m1.py

示例14: InitCell

init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)

x, x_mask = train_data.theano_vars()
if debug:
    x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=np.float32)
    temp = np.ones((15, batch_size), dtype=np.float32)
    temp[:, -2:] = 0.
    x_mask.tag.test_value = temp
x_tm1 = T.concatenate([T.zeros((1, x.shape[1], x.shape[2])), x[:-1]], axis=0)
x_tm1.name = 'x_tm1'

x_1 = FullyConnectedLayer(name='x_1',
                          parent=['x_t'],
                          parent_dim=[frame_size],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_2 = FullyConnectedLayer(name='x_2',
                          parent=['x_1'],
                          parent_dim=[x2s_dim],
                          nout=x2s_dim,
                          unit='relu',
                          init_W=init_W,
                          init_b=init_b)

x_3 = FullyConnectedLayer(name='x_3',
                          parent=['x_2'],
                          parent_dim=[x2s_dim],
开发者ID:anirudh9119，项目名称:SpeechSyn，代码行数:31，代码来源:storn0_3.py

示例15: GFLSTM

h3 = GFLSTM(name='h3',
            parent=['x', 'h2'],
            parent_dim=[205, 200],
            recurrent=['h1', 'h2'],
            recurrent_dim=[200, 200],
            nout=200,
            unit='tanh',
            init_W=init_W,
            init_U=init_U,
            init_b=init_b)

output = FullyConnectedLayer(name='output',
                             parent=['h1', 'h2', 'h3'],
                             parent_dim=[200, 200, 200],
                             nout=205,
                             unit='softmax',
                             init_W=init_W,
                             init_b=init_b)

nodes = [h1, h2, h3, output]

for node in nodes:
    node.initialize()

params = flatten([node.get_params().values() for node in nodes])

step_count = sharedX(0, name='step_count')
last_h = np.zeros((batch_size, 400), dtype=np.float32)
h1_tm1 = sharedX(last_h, name='h1_tm1')
h2_tm1 = sharedX(last_h, name='h2_tm1')
开发者ID:Beronx86，项目名称:cle，代码行数:30，代码来源:enwiki.py


注：本文中的cle.cle.layers.feedforward.FullyConnectedLayer类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。