def __init__(self, params):
self.e_layer = WordEmbeddingLayer(embeddings=params.embeddings)
self.c_layers = []
for i in range(params.conv_layer_n):
self.c_layers.append(
ConvFoldingPoolLayer(params.ks[i],
params.fold[i],
W=params.W[i],
b=params.b[i]))
self.l_layer = LogisticRegression(params.logreg_W, params.logreg_b)
def load_from_theano_model(cls, model, word2id):
return RNTN(embedding=model.embedding.get_value(),
rntn_layer=RNTNLayer(model.rntn_layer.V.get_value(),
model.rntn_layer.W.get_value()),
logreg_layer=LogisticRegression(
model.logreg_layer.W.get_value(),
model.logreg_layer.b.get_value()),
word2id=word2id)
class DCNN(object):
def __init__(self, params):
self.e_layer = WordEmbeddingLayer(embeddings = params.embeddings)
self.c_layers = []
for i in range(params.conv_layer_n):
self.c_layers.append(ConvFoldingPoolLayer(params.ks[i],
params.fold[i],
W = params.W[i],
b = params.b[i])
)
self.l_layer = LogisticRegression(
params.logreg_W,
params.logreg_b
)
def _p_y_given_x(self, x):
output = self.e_layer.output(x)
for l in self.c_layers:
output = l.output(output)
assert output.ndim == 4
output = output.reshape(
(output.shape[0],
np.prod(output.shape[1:]))
)
return self.l_layer._p_y_given_x(output)
def predict(self, x):
return np.argmax(self._p_y_given_x(x), axis = 1)
# The following functions are
# FOR TESTING PURPOSE
#
def _nnl(self, x, y):
p_y_given_x = self._p_y_given_x(x)
return np.mean(
-np.log(p_y_given_x[np.arange(y.shape[0]), y])
)
def _errors(self, x, y):
assert y.dtype == np.int32, "%r != %r" %(y.dtype, np.int32)
pred_y = self.predict(x)
return np.sum(pred_y != y) / float(pred_y.shape[0])
def _c_layer_output(self, x):
output = self.e_layer.output(x)
for l in self.c_layers:
output = l.output(output)
return output
class DCNN(object):
def __init__(self, params):
self.e_layer = WordEmbeddingLayer(embeddings = params.embeddings)
self.c_layers = []
for i in xrange(params.conv_layer_n):
self.c_layers.append(ConvFoldingPoolLayer(params.ks[i],
params.fold[i],
W = params.W[i],
b = params.b[i])
)
self.l_layer = LogisticRegression(
params.logreg_W,
params.logreg_b
)
def _p_y_given_x(self, x):
output = self.e_layer.output(x)
for l in self.c_layers:
output = l.output(output)
assert output.ndim == 4
output = output.reshape(
(output.shape[0],
np.prod(output.shape[1:]))
)
return self.l_layer._p_y_given_x(output)
def predict(self, x):
return np.argmax(self._p_y_given_x(x), axis = 1)
# The following functions are
# FOR TESTING PURPOSE
#
def _nnl(self, x, y):
p_y_given_x = self._p_y_given_x(x)
return np.mean(
-np.log(p_y_given_x[np.arange(y.shape[0]), y])
)
def _errors(self, x, y):
assert y.dtype == np.int32, "%r != %r" %(y.dtype, np.int32)
pred_y = self.predict(x)
return np.sum(pred_y != y) / float(pred_y.shape[0])
def _c_layer_output(self, x):
output = self.e_layer.output(x)
for l in self.c_layers:
output = l.output(output)
return output
def __init__(self, params):
self.e_layer = WordEmbeddingLayer(embeddings = params.embeddings)
self.c_layers = []
for i in xrange(params.conv_layer_n):
self.c_layers.append(ConvFoldingPoolLayer(params.ks[i],
params.fold[i],
W = params.W[i],
b = params.b[i])
)
self.l_layer = LogisticRegression(
params.logreg_W,
params.logreg_b
)
from logreg import LogisticRegression as TheanoLogisticRegression
from test_util import assert_matrix_eq
#########################
# NUMPY PART
#########################
# 5 labels and 10 inputs
W = np.random.rand(10, 5)
b = np.random.rand(5)
x = np.random.rand(3, 10)
y = np.asarray(np.random.randint(5, size=3), dtype=np.int32)
np_l = LogisticRegression(W, b)
#########################
# THEANO PART
#########################
x_symbol = theano.tensor.dmatrix('x')
y_symbol = theano.tensor.ivector('y')
th_l = TheanoLogisticRegression(rng=np.random.RandomState(1234),
input=x_symbol,
n_in=10,
n_out=5,
W=theano.shared(value=W, name="W"),
b=theano.shared(value=b, name="b"))
from test_util import assert_matrix_eq
#########################
# NUMPY PART
#########################
# 5 labels and 10 inputs
W = np.random.rand(10, 5)
b = np.random.rand(5)
x = np.random.rand(3, 10)
y = np.asarray(np.random.randint(5, size = 3),
dtype=np.int32
)
np_l = LogisticRegression(W, b)
#########################
# THEANO PART
#########################
x_symbol = theano.tensor.dmatrix('x')
y_symbol = theano.tensor.ivector('y')
th_l = TheanoLogisticRegression(rng = np.random.RandomState(1234),
input = x_symbol,
n_in = 10,
n_out = 5,
W = theano.shared(value = W,
name = "W"),
b = theano.shared(value = b,