def get_logits(self, image, is_train, **kwargs): widths = tf.ones(tf.shape(image)[0], dtype=tf.int32) * tf.shape(image)[2] features, sequence_length = self._convnet_layers( image, widths, is_train) features = rnn_layers(features, sequence_length, self.rnn_size, use_projection=True) logits = dense_layer(features, len(self.out_charset) + 1, name='logits') return logits, sequence_length
def get_logits(self, image, is_train, **kwargs): """ """ widths = tf.ones(tf.shape(image)[0], dtype=tf.int32) * tf.shape(image)[2] features, sequence_length = self._convnet_layers( image, widths, is_train) attention_states = rnn_layers(features, sequence_length, self.rnn_size, use_projection=True) attention_states = dense_layer(attention_states, self.rnn_size, name='att_state_dense') logits, weights = attention_decoder(attention_states, kwargs['label'], len(self.out_charset), self.rnn_size, is_train, self.FLAGS.label_maxlen, cell_type='gru') return logits, sequence_length