def build_crf(self):
reg = tf.keras.regularizers.L2(1e-3)
input = Input(shape=(self.seq_len, self.feat_dim), dtype='float32')
mid = Dense(self.num_classes,
input_shape=(self.seq_len, self.feat_dim),
activation='linear',
kernel_regularizer=reg)(input)
crf = CRF(dtype='float32', sparse_target=True)
crf.sequence_lengths = self.seq_len
crf.output_dim = self.num_classes
output = crf(mid)
model = Model(input, output)
opt = tf.keras.optimizers.Adam(learning_rate=0.01)
model.compile(loss=crf.loss, optimizer=opt, metrics=[crf.accuracy])
model.load_weights(self.crf_weights)
self.crf = model
# Setting global network params
SEQ_LEN = 1024
NUM_CLASSES = 25
FEAT_DIM = 128
# Since tf2crf is a custom class, need to build the CRF model and load the weights (can not load the model directly)
reg = tf.keras.regularizers.L2(1e-3)
input = Input(shape=(SEQ_LEN, FEAT_DIM), dtype='float32')
mid = Dense(NUM_CLASSES,
input_shape=(SEQ_LEN, FEAT_DIM),
activation='linear',
kernel_regularizer=reg)(input)
crf = CRF(dtype='float32', sparse_target=True)
crf.sequence_lengths = SEQ_LEN
crf.output_dim = NUM_CLASSES
output = crf(mid)
model = Model(input, output)
opt = tf.keras.optimizers.Adam(learning_rate=0.01)
model.compile(loss=crf.loss, optimizer=opt, metrics=[crf.accuracy])
load_from = './model_01'
model.load_weights(load_from)
crf = model
# Loading FCNN feature extractor
cnn = load_model('cnn_extractor.h5')
CONTEXT_SIZE = 7
