| import tensorflow as tf |
| import tf_utils as tfu |
| from graph.base import GraphPartBase |
|
|
|
|
| class MainCls(GraphPartBase): |
| def __init__(self, for_usage, global_settings, current_settings, optimiser, reset_optimiser): |
| super().__init__(for_usage, global_settings, current_settings, optimiser, reset_optimiser, 'main', ['Loss', 'Accuracy']) |
| self.checks = [] |
| self.weights = [] |
| self.keep_drops = [] |
| self.losses = [] |
| self.probs = [] |
| self.results = [] |
| self.ys = [] |
| self.drops = [] |
| self.top_k = global_settings['main_class_k'] |
|
|
| def __build_graph_for_device__(self, x, seq_len, gram_probs, gram_drop): |
| self.xs.append(x) |
| self.x_seq_lens.append(seq_len) |
| self.drops.append(gram_drop) |
|
|
| y = tf.placeholder(dtype=tf.int32, shape=(None, self.main_classes_count), name='Y') |
| weights = tf.placeholder(dtype=tf.float32, shape=(None,), name='Weight') |
| x_emd_init = tf.random_normal((self.chars_count, self.settings['char_vector_size'])) |
| x_emb = tf.get_variable("Embeddings", initializer=x_emd_init) |
| rnn_input = tf.nn.embedding_lookup(x_emb, x) |
|
|
| if self.for_usage: |
| cls_keep_drop = tf.constant(1, dtype=tf.float32, name='KeepDrop') |
| else: |
| cls_keep_drop = tf.placeholder(dtype=tf.float32, name='KeepDrop') |
| self.keep_drops.append(cls_keep_drop) |
|
|
| init_state = tf.concat(gram_probs, 1) |
| with tf.variable_scope("InitRnnState", reuse=tf.AUTO_REUSE) as scope: |
| rez_size = self.settings['rnn_state_size'] |
| w_softmax = tf.get_variable("W", (init_state.shape[1], rez_size)) |
| b_softmax = tf.get_variable("b", [rez_size]) |
| init_state = tf.matmul(init_state, w_softmax) + b_softmax |
|
|
| rnn_logits = tfu.build_rnn(rnn_input, |
| cls_keep_drop, |
| seq_len, |
| self.settings, |
| init_state, |
| init_state, |
| self.for_usage) |
|
|
| logits = tfu.rnn_top('RnnTop', |
| rnn_logits, |
| self.settings, |
| self.main_classes_count) |
|
|
| float_y = tf.cast(y, tf.float32) |
| errors = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, |
| labels=float_y) |
| errors = tf.reduce_sum(errors, axis=1) |
| errors = errors * weights |
| if not self.for_usage: |
| self.checks.append(tf.check_numerics(errors, "ErrorNullCheck")) |
|
|
| probs = tf.nn.softmax(logits) |
| result = tf.math.top_k(probs, self.top_k, name="Results") |
| loss = tf.reduce_sum(errors) |
|
|
| if not self.for_usage: |
| self.checks.append(tf.check_numerics(errors, "LossNullCheck")) |
|
|
| vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.main_scope_name) |
| grads = self.optimiser.compute_gradients(loss, var_list=vars) |
|
|
| self.losses.append(loss) |
| self.probs.append(probs) |
| self.results.append(result) |
| self.ys.append(y) |
| self.dev_grads.append(grads) |
| self.weights.append(weights) |
|
|
| |
| self.create_mean_metric(0, loss) |
| labels = tf.math.argmax(y, axis=1) |
| predictions = tf.math.argmax(probs, axis=1) |
| self.create_accuracy_metric(1, labels, predictions) |
|
|
| def __update_feed_dict__(self, op_name, feed_dict, batch, dev_num): |
| for gram_drop in self.drops[dev_num]: |
| feed_dict[gram_drop] = 1 |
| feed_dict[self.keep_drops[dev_num]] = 1 if op_name == 'test' else self.settings['keep_drop'] |
| feed_dict[self.ys[dev_num]] = batch['y'] |
| feed_dict[self.weights[dev_num]] = batch['weight'] |
|
|
| def __load_dataset__(self, operation_name): |
| return list( |
| tfu.load_cls_dataset( |
| self.dataset_path, |
| self.devices_count, |
| operation_name, |
| self.settings['train_batch_size'], |
| self.settings['use_weights'] |
| ) |
| ) |
|
|
