def _get_image_info(features, mode):
"""Calculates the logits and sequence length"""
image = features['image']
width = features['width']
conv_features, sequence_length = model.convnet_layers(image, width, mode)
logits = model.rnn_layers(conv_features, sequence_length,
charset.num_classes())
return logits, sequence_length
def _get_output(rnn_logits, sequence_length, lexicon):
"""Create ops for validation
predictions: Results of CTC beam search decoding
log_prob: Score of predictions
"""
with tf.name_scope("test"):
if lexicon:
dict_tensor = _get_dictionary_tensor(lexicon, charset.out_charset)
predictions, log_prob = tf.nn.ctc_beam_search_decoder_trie(
rnn_logits,
sequence_length,
alphabet_size=charset.num_classes(),
dictionary=dict_tensor,
beam_width=128,
top_paths=1,
merge_repeated=True)
else:
predictions, log_prob = tf.nn.ctc_beam_search_decoder(
rnn_logits,
sequence_length,
beam_width=128,
top_paths=1,
merge_repeated=True)
return predictions, log_prob