def test_cost_layer(self):
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.classification_cost(
input=inference, label=label, weight=weight)
cost3 = layer.cross_entropy_cost(input=inference, label=label)
cost4 = layer.cross_entropy_with_selfnorm_cost(
input=inference, label=label)
cost5 = layer.square_error_cost(input=inference, label=label)
cost6 = layer.square_error_cost(
input=inference, label=label, weight=weight)
cost7 = layer.multi_binary_label_cross_entropy_cost(
input=inference, label=label)
cost8 = layer.rank_cost(left=score, right=score, label=score)
cost9 = layer.lambda_cost(input=inference, score=score)
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_regression_cost(input=score, label=label)
cost12 = layer.huber_classification_cost(input=score, label=label)
print layer.parse_network([cost1, cost2])
print layer.parse_network([cost3, cost4])
print layer.parse_network([cost5, cost6])
print layer.parse_network([cost7, cost8, cost9, cost10, cost11, cost12])
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
ctc = layer.ctc(input=inference, label=label)
warp_ctc = layer.warp_ctc(input=pixel, label=label)
nce = layer.nce(input=inference, label=label, num_classes=3)
hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
print layer.parse_network(
[crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid])
def test_cost_layer(self):
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.classification_cost(input=inference,
label=label,
weight=weight)
cost3 = layer.cross_entropy_cost(input=inference, label=label)
cost4 = layer.cross_entropy_with_selfnorm_cost(input=inference,
label=label)
cost5 = layer.mse_cost(input=inference, label=label)
cost6 = layer.mse_cost(input=inference, label=label, weight=weight)
cost7 = layer.multi_binary_label_cross_entropy_cost(input=inference,
label=label)
cost8 = layer.rank_cost(left=score, right=score, label=score)
cost9 = layer.lambda_cost(input=inference, score=score)
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_cost(input=score, label=label)
print layer.parse_network(cost1, cost2)
print layer.parse_network(cost3, cost4)
print layer.parse_network(cost5, cost6)
print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
ctc = layer.ctc(input=inference, label=label)
warp_ctc = layer.warp_ctc(input=pixel, label=label)
nce = layer.nce(input=inference, label=label, num_classes=3)
hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
print layer.parse_network(crf, crf_decoding, ctc, warp_ctc, nce,
hsigmoid)
def parse_new_rnn():
reset_parser()
data = layer.data(
name="word", type=data_type.dense_vector(dict_dim))
label = layer.data(
name="label", type=data_type.dense_vector(label_dim))
emb = layer.embedding(input=data, size=word_dim)
boot_layer = layer.data(
name="boot", type=data_type.dense_vector(10))
boot_layer = layer.fc(name='boot_fc', input=boot_layer, size=10)
def step(y, wid):
z = layer.embedding(input=wid, size=word_dim)
mem = layer.memory(
name="rnn_state", size=hidden_dim, boot_layer=boot_layer)
out = layer.fc(input=[y, z, mem],
size=hidden_dim,
act=activation.Tanh(),
bias_attr=True,
name="rnn_state")
return out
out = layer.recurrent_group(
name="rnn", step=step, input=[emb, data])
rep = layer.last_seq(input=out)
prob = layer.fc(size=label_dim,
input=rep,
act=activation.Softmax(),
bias_attr=True)
cost = layer.classification_cost(input=prob, label=label)
return str(layer.parse_network(cost))
def parse_new_rnn():
data = layer.data(name="word",
type=data_type.dense_vector(dict_dim))
label = layer.data(name="label",
type=data_type.dense_vector(label_dim))
emb = layer.embedding(input=data, size=word_dim)
boot_layer = layer.data(name="boot",
type=data_type.dense_vector(10))
boot_layer = layer.fc(name='boot_fc', input=boot_layer, size=10)
def step(y, wid):
z = layer.embedding(input=wid, size=word_dim)
mem = layer.memory(name="rnn_state",
size=hidden_dim,
boot_layer=boot_layer)
out = layer.fc(input=[y, z, mem],
size=hidden_dim,
act=activation.Tanh(),
bias_attr=True,
name="rnn_state")
return out
out = layer.recurrent_group(name="rnn",
step=step,
input=[emb, data])
rep = layer.last_seq(input=out)
prob = layer.fc(size=label_dim,
input=rep,
act=activation.Softmax(),
bias_attr=True)
cost = layer.classification_cost(input=prob, label=label)
return str(layer.parse_network(cost))
def test_get_layer(self):
pixel = layer.data(name='pixel2', type=data_type.dense_vector(784))
label = layer.data(name='label2', type=data_type.integer_value(10))
hidden = layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
cost = layer.classification_cost(input=inference, label=label)
topo = topology.Topology(cost)
pixel_layer = topo.get_layer("pixel2")
label_layer = topo.get_layer("label2")
self.assertEqual(pixel_layer, pixel)
self.assertEqual(label_layer, label)
def test_parse(self):
pixel = layer.data(name='pixel3', type=data_type.dense_vector(784))
label = layer.data(name='label3', type=data_type.integer_value(10))
hidden = layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
maxid = layer.max_id(input=inference)
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.cross_entropy_cost(input=inference, label=label)
topology.Topology(cost2).proto()
topology.Topology([cost1]).proto()
topology.Topology([cost1, cost2]).proto()
topology.Topology([inference, maxid]).proto()
def test_data_type(self):
pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
label = layer.data(name='label', type=data_type.integer_value(10))
hidden = layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
cost = layer.classification_cost(input=inference, label=label)
topo = topology.Topology(cost)
data_types = topo.data_type()
self.assertEqual(len(data_types), 2)
pixel_data_type = filter(lambda type: type[0] == "pixel", data_types)
self.assertEqual(len(pixel_data_type), 1)
pixel_data_type = pixel_data_type[0]
self.assertEqual(pixel_data_type[1].type, pydp2.DataType.Dense)
self.assertEqual(pixel_data_type[1].dim, 784)
label_data_type = filter(lambda type: type[0] == "label", data_types)
self.assertEqual(len(label_data_type), 1)
label_data_type = label_data_type[0]
self.assertEqual(label_data_type[1].type, pydp2.DataType.Index)
self.assertEqual(label_data_type[1].dim, 10)
