def def_model(): qnd.add_flag("hidden_layer_size", type=int, default=64, help="Hidden layer size") def model(image, number=None, mode=None): h = tf.contrib.layers.fully_connected(image, qnd.FLAGS.hidden_layer_size) h = tf.contrib.layers.fully_connected(h, 10, activation_fn=None) predictions = tf.argmax(h, axis=1) if mode == tf.contrib.learn.ModeKeys.INFER: return predictions loss = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(labels=number, logits=h)) return predictions, loss, minimize(loss), { "accuracy": tf.contrib.metrics.streaming_accuracy(predictions, number)[1], } return model
def add_flags(): qnd.add_flag("regularization_scale", type=float, default=1e-8) adder = qnd.FlagAdder() adder.add_flag("word_embedding_size", type=int, default=100) adder.add_flag("sentence_embedding_size", type=int, default=100) adder.add_flag("document_embedding_size", type=int, default=100) adder.add_flag("context_vector_size", type=int, default=100) return adder
def add_flags(): qnd.add_flag("regularization_scale", type=float, default=1e-8) adder = qnd.FlagAdder() adder.add_flag("word_embedding_size", type=int, default=100) adder.add_flag("sentence_embedding_size", type=int, default=100) adder.add_flag("document_embedding_size", type=int, default=100) adder.add_flag("context_vector_size", type=int, default=100) return adder
def def_ar_lm(): qnd.add_flag('cell_size', type=int, default=128) qnd.add_flag('num_unroll', type=int, default=16) qnd.add_flag('batch_size', type=int, default=64) qnd.add_flag('num_batch_threads', type=int, default=os.cpu_count()) qnd.add_flag('batch_queue_capacity', type=int, default=1024) def ar_lm(key, sentence, labels, *, char_embeddings): cell = tf.contrib.rnn.LayerNormBasicLSTMCell(qnd.FLAGS.cell_size) batch = tf.contrib.training.batch_sequences_with_states( key, input_sequences={ 'sentence': tf.gather(char_embeddings, sentence), 'labels': labels, }, input_context={}, input_length=None, initial_states={ 'c': tf.zeros([cell.state_size.c], tf.float32), 'h': tf.zeros([cell.state_size.h], tf.float32), }, num_unroll=qnd.FLAGS.num_unroll, batch_size=qnd.FLAGS.batch_size, num_threads=qnd.FLAGS.num_batch_threads, capacity=qnd.FLAGS.batch_queue_capacity) outputs, _ = tf.nn.state_saving_rnn(cell, tf.unstack( batch.sequences['sentence'], axis=1), sequence_length=batch.length, state_saver=batch, state_name=('c', 'h')) logits = batch_linear(outputs, ex.static_shape(char_embeddings)[0]) labels = batch.sequences['labels'] loss = sequence_labeling_loss(logits, labels, batch.length) return ( { 'key': key, 'labels': (tf.argmax(logits, axis=2) * tf.sequence_mask(batch.length, dtype=tf.int64)), }, loss, ex.minimize(loss), ) return ar_lm
def def_char_lm(): get_chars = qndex.nlp.def_chars() qnd.add_flag('char_embedding_size', type=int, default=100) ar_lm = def_ar_lm() def char_lm(key, sentence, labels): return ar_lm(key, sentence, labels, char_embeddings=ex.embeddings( id_space_size=len(get_chars()), embedding_size=qnd.FLAGS.char_embedding_size, name='char_embeddings')) return char_lm
def add_flags(): adder = add_child_flags() adder.add_flag("dropout_keep_prob", type=float, default=0.5) adder.add_flag("nums_of_cnn_channels", type=argtyp.int_list, default=[32] * 4) adder.add_flag("nums_of_attention_cnn_channels", type=argtyp.int_list, default=[32] * 3) qnd.add_required_flag("font_file") qnd.add_flag("font_size", type=int, default=32) qnd.add_flag("save_font_array_file") return adder
def add_flags(): adder = add_child_flags() adder.add_flag("dropout_keep_prob", type=float, default=0.5) adder.add_flag("nums_of_cnn_channels", type=argtyp.int_list, default=[32] * 4) adder.add_flag("nums_of_attention_cnn_channels", type=argtyp.int_list, default=[32] * 3) qnd.add_required_flag("font_file") qnd.add_flag("font_size", type=int, default=32) qnd.add_flag("save_font_array_file") return adder
def add_flags(): adder = add_child_flags() adder.add_flag("dropout_keep_prob", type=float, default=0.5) qnd.add_required_flag("font_file") qnd.add_flag("font_size", type=int, default=32) qnd.add_flag("save_font_array_file") qnd.add_flag("regularization_scale", type=float, default=1e-8) return adder