tf2crf 0.1.21

a crf layer for tensorflow 2 keras

tf2crf

• a simple CRF layer for tensorflow 2 keras
• support keras masking

Install

$ pip install tf2crf

Tips

tensorflow >= 2.1.0 Recommmend use the latest tensorflow-addons which is compatiable with your tf version.

Example

from tf2CRF import CRF
from tensorflow.keras.layers import Input, Embedding, Bidirectional, GRU, Dense
from tensorflow.keras.models import Model

inputs = Input(shape=(None,), dtype='int32')
output = Embedding(100, 40, trainable=True, mask_zero=True)(inputs)
output = Bidirectional(GRU(64, return_sequences=True))(output)
output = Dense(9, activation=None)(output)
crf = CRF(dtype='float32')
output = crf(output)
model = Model(inputs, output)
model.compile(loss=crf.loss, optimizer='adam', metrics=[crf.accuracy])

x = [[5, 2, 3] * 3] * 10
y = [[1, 2, 3] * 3] * 10

model.fit(x=x, y=y, epochs=2, batch_size=2)
model.save('model')

Supoort for tensorflow mixed precision training

Currently these is a bug in tensorflow-addons.text.crf, which causes a dtype error when using miex precision. This bug has been fixed in master branch, but is not released. so if you want to use mixed precision training. You need to pip install tfa-nighly instead.

Example

from tf2CRF import CRF
from tensorflow.keras.layers import Input, Embedding, Bidirectional, GRU, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.mixed_precision import experimental as mixed_precision
policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_policy(policy)

inputs = Input(shape=(None,), dtype='int32')
output = Embedding(100, 40, trainable=True, mask_zero=True)(inputs)
output = Bidirectional(GRU(64, return_sequences=True))(output)
output = Dense(9, activation=None)(output)
crf = CRF(dtype='float32')
output = crf(output)
model = Model(inputs, output)
model.compile(loss=crf.loss, optimizer='adam', metrics=[crf.accuracy])

x = [[5, 2, 3] * 3] * 10
y = [[1, 2, 3] * 3] * 10

model.fit(x=x, y=y, epochs=2, batch_size=2)
model.save('model')