本文整理汇总了Python中cle.cle.layers.recurrent.LSTM类的典型用法代码示例。如果您正苦于以下问题：Python LSTM类的具体用法？Python LSTM怎么用？Python LSTM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LSTM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
#.........这里部分代码省略.........
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,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_4', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_2 = FullyConnectedLayer(name='phi_2',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_3 = FullyConnectedLayer(name='phi_3',
parent=['phi_2'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
示例2: FullyConnectedLayer
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)
main_lstm = LSTM(name='main_lstm',
parent=['x_4', 'z_4'],
parent_dim=[x2s_dim, z2s_dim],
batch_size=batch_size,
nout=main_lstm_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_4', 's_tm1'],
parent_dim=[x2s_dim, main_lstm_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_2 = FullyConnectedLayer(name='phi_2',
parent=['phi_1'],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()
if debug:
x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=np.float32)
x_tm1 = T.concatenate([T.zeros((1, x.shape[1], x.shape[2])), x[:-1]], axis=0)
x_tm1.name = 'x_tm1'
encoder = LSTM(name='encoder',
parent=['x_t'],
parent_dim=[frame_size],
batch_size=batch_size,
nout=encoder_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
decoder = LSTM(name='decoder',
parent=['x_tm1', 'z_t'],
parent_dim=[frame_size, latent_size],
batch_size=batch_size,
nout=decoder_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
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.........
示例5: FullyConnectedLayer
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)
main_lstm = LSTM(name='main_lstm',
parent=['x_4'],
parent_dim=[x2s_dim],
batch_size=batch_size,
nout=main_lstm_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=[main_lstm_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_2 = FullyConnectedLayer(name='theta_2',
parent=['theta_1'],示例6: FullyConnectedLayer
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)
lstm_1 = LSTM(name='lstm_1',
parent=['x_4', 'z_4'],
parent_dim=[x2s_dim, z2s_dim],
batch_size=batch_size,
nout=lstm_1_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
lstm_2 = LSTM(name='lstm_2',
parent=['lstm_1'],
parent_dim=[lstm_1_dim],
batch_size=batch_size,
nout=lstm_2_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
示例7: 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.........
示例8: LSTM
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_tm1 = T.concatenate([T.zeros((1, x.shape[1], x.shape[2])), x[:-1]], axis=0)
x_tm1.name = 'x_tm1'
mn_x_tm1 = T.concatenate([T.zeros((1, mn_x.shape[1], mn_x.shape[2])), mn_x[:-1]], axis=0)
mn_x_tm1.name = 'mn_x_tm1'
encoder = LSTM(name='encoder',
parent=['x_t'],
parent_dim=[frame_size],
batch_size=batch_size,
nout=encoder_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
decoder = LSTM(name='decoder',
parent=['x_tm1', 'z_t'],
parent_dim=[frame_size, latent_size],
batch_size=batch_size,
nout=decoder_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
示例9: InitCell
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask = trdata.theano_vars()
if debug:
x.tag.test_value = np.zeros((15, batch_size, inpsz), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp [:, -2:] = 0.
mask.tag.test_value = temp
coder_1 = LSTM(name='coder_1',
parent=['x_t', 'z_1_t', 'z_2_t'],
parent_dim=[inpsz, latsz_1, latsz_2],
batch_size=batch_size,
nout=shared_nout_1,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_emb_1 = FullyConnectedLayer(name='phi_emb_1',
parent=['x_t', 's_1_tm1', 's_2_tm1'],
parent_dim=[inpsz, shared_nout_1, shared_nout_2],
nout=lat_emb,
#unit='tanh',
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu_1 = FullyConnectedLayer(name='phi_mu_1',示例10: InitCell
init_b_sig = InitCell('const', mean=0.6)
x, mask = trdata.theano_vars()
if debug:
x.tag.test_value = np.zeros((15, batch_size, inpsz), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp [:, -2:] = 0.
mask.tag.test_value = temp
x2 = T.concatenate([T.zeros((1, x.shape[1], x.shape[2])), x[:-1]], axis=0)
x2.name = 'x2'
encoder = LSTM(name='encoder',
parent=['x_t'],
parent_dim=[inpsz],
batch_size=batch_size,
nout=enc_nout,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
decoder = LSTM(name='decoder',
parent=['z_t'],
parent_dim=[latsz],
batch_size=batch_size,
nout=dec_nout,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
示例11: FullyConnectedLayer
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)
rnn = LSTM(name='rnn',
parent=['x_4'],
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=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_2 = FullyConnectedLayer(name='theta_2',
parent=['theta_1'],
parent_dim=[p_x_dim],示例12: InitCell
# Choose the random initialization method
init_W = InitCell('randn')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
x, y, mask = train_data.theano_vars()
# You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb
if debug:
x.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
y.tag.test_value = np.zeros((10, batch_size, nlabel), dtype=np.float32)
mask.tag.test_value = np.ones((10, batch_size), dtype=np.float32)
h1 = LSTM(name='h1',
parent=['x'],
parent_dim=[105],
nout=50,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
h2 = LSTM(name='h2',
parent=['h1'],
parent_dim=[50],
nout=50,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
h3 = LSTM(name='h3',
parent=['h2'],示例13: ReadLayer
nout=5,
unit='linear',
init_W=init_W,
init_b=init_b)
read = ReadLayer(name='read',
parent=['x', 'error', 'read_param'],
glimpse_shape=(batch_size, 1, 2, 2),
input_shape=(batch_size, 1, 28, 28))
enc = LSTM(name='enc',
parent=['read'],
parent_dim=[8],
recurrent=['dec'],
recurrent_dim=[256],
batch_size=batch_size,
nout=256,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['enc'],
parent_dim=[256],
nout=latsz,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',示例14: InitCell
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask = trdata.theano_vars()
if debug:
x.tag.test_value = np.zeros((15, batch_size, inpsz), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp [:, -2:] = 0.
mask.tag.test_value = temp
coder = LSTM(name='coder',
parent=['x_t', 'z_t'],
parent_dim=[inpsz, latsz],
batch_size=batch_size,
nout=shared_nout,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['x_t', 's_tm1'],
parent_dim=[inpsz, shared_nout],
nout=latsz,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['x_t', 's_tm1'],示例15: FullyConnectedLayer
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)
encoder = LSTM(name='encoder',
parent=['x_4'],
parent_dim=[x2s_dim],
batch_size=batch_size,
nout=encoder_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
decoder = LSTM(name='decoder',
parent=['x_4_tm1', 'z_4'],
parent_dim=[x2s_dim, z2s_dim],
batch_size=batch_size,
nout=decoder_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)