def train_rnn(): x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb')) x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb')) vocab_processer = learn.preprocessing.VocabularyProcessor.restore(FLAGS.vocab_path) train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) with tf.Graph().as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # 构建rnn 节点 rnns = RNNsClassification( embedding_mat=None, embedding_dims=FLAGS.embedding_dim, vocab_size=len(vocab_processer.vocabulary_), non_static=False, hidden_unit=FLAGS.hidden_unit, sequence_length=FLAGS.sequence_length, num_tags=FLAGS.num_tags, cell=FLAGS.cell, num_layers=FLAGS.num_layer, l2_reg_lambda=FLAGS.l2_reg_lambda) # Define Training procedure global_step = tf.Variable(0, name="global_step", trainable=False) # 优化算法 optimizer = tf.train.AdamOptimizer(1e-3) grads_and_vars = optimizer.compute_gradients(rnns.loss) train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step) # Keep track of gradient values and sparsity (optional) grad_summaries = [] for g, v in grads_and_vars: if g is not None: grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g) sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g)) grad_summaries.append(grad_hist_summary) grad_summaries.append(sparsity_summary) grad_summaries_merged = tf.summary.merge(grad_summaries) # Output directory for models and summaries timestamp = str(int(time.time())) out_dir = os.path.abspath(os.path.join(FLAGS.out_dir, "runs_rnn", timestamp)) print("Writing to {}\n".format(out_dir)) # Summaries for loss and accuracy loss_summary = tf.summary.scalar("loss", rnns.loss) acc_summary = tf.summary.scalar("accuracy", rnns.accuracy) # Train Summaries train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged]) train_summary_dir = os.path.join(out_dir, "summaries", "train") train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph) # Dev summaries dev_summary_op = tf.summary.merge([loss_summary, acc_summary]) dev_summary_dir = os.path.join(out_dir, "summaries", "dev") dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph) # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints")) checkpoint_prefix = os.path.join(checkpoint_dir, "model") if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) # Write vocabulary # vocab_proccesser.save(os.path.join(out_dir, "vocab")) # Initialize all variables sess.run(tf.global_variables_initializer()) def train_step(x_batch, y_batch): """ A single training step """ feed_dict = { rnns.input_x: x_batch, rnns.input_y: y_batch, rnns.dropout_keep_prob: FLAGS.dropout_keep_prob } # 执行 节点操作 _, step, summaries, loss, accuracy = sess.run( [train_op, global_step, train_summary_op, rnns.loss, rnns.accuracy], feed_dict) time_str = datetime.datetime.now().isoformat() if step % 20 == 0: print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy)) train_summary_writer.add_summary(summaries, step) def dev_step(x_batch, y_batch, writer=None): """ Evaluates model on a dev set """ feed_dict = { rnns.input_x: x_batch, rnns.input_y: y_batch, rnns.dropout_keep_prob: 1.0 } step, summaries, loss, accuracy, correct = sess.run( [global_step, dev_summary_op, rnns.loss, rnns.accuracy, rnns.num_correct], feed_dict) # time_str = datetime.datetime.now().isoformat() # print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy)) if writer: writer.add_summary(summaries, step) return loss, accuracy, correct # Generate batches # batches = data_helper.batch_iter( # list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) # Training loop. For each batch... best_acc = 0.0 best_step = 0 for batch in train_batches: x_batch, y_batch = zip(*batch) train_step(x_batch, y_batch) # 更新全局步数 current_step = tf.train.global_step(sess, global_step) # 计算评估结果 if current_step % FLAGS.evaluate_every == 0: print("\nEvaluation:") total_dev_correct = 0 dev_batches = batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1) for dev_batch in dev_batches: x_dev_batch, y_dev_batch = zip(*dev_batch) acc, loss, num_dev_correct = dev_step(x_dev_batch, y_dev_batch) total_dev_correct += num_dev_correct accuracy = float(total_dev_correct) / len(y_dev) print('Accuracy on dev set: {}'.format(accuracy)) # loss_, accuracy_ = dev_step(x_dev, y_dev, writer=dev_summary_writer) if accuracy > best_acc: best_acc = accuracy best_step = current_step # 保存模型计算结果 path = saver.save(sess, checkpoint_prefix, global_step=current_step) print("Saved model checkpoint to {}\n".format(path)) print("") print('\nBset dev at {}, accuray {:g}'.format(best_step, best_acc))
def train_cnns(): """ 训练CNNs模型用于文本分类 :return: """ # Parameters # ================================================== # Data loading params tf.flags.DEFINE_float( "train_size", .1, "Percentage of the training data to use for validation") tf.flags.DEFINE_string("train_path", r'../../../dataset/train', "Data source.") tf.flags.DEFINE_string("dev_path", r'../../../dataset/dev', "Data source.") tf.flags.DEFINE_string('vocab_path', r'../../../dataset/vocab', 'vocabulary path') tf.flags.DEFINE_integer('sequence_length', 500, 'length of each sequence') tf.flags.DEFINE_integer("num_tags", 14, "number classes of datasets.") tf.flags.DEFINE_string('out_dir', '../../models', 'output directory') # Model Hyperparameters tf.flags.DEFINE_integer( "embedding_dim", 200, "Dimensionality of character embedding (default: 128)") tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')") tf.flags.DEFINE_integer( "num_filters", 200, "Number of filters per filter size (default: 128)") tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)") tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)") tf.flags.DEFINE_float('learning_rate', 0.01, 'learning_rate of gradient') # Training parameters tf.flags.DEFINE_integer("batch_size", 32, "Batch Size (default: 64)") tf.flags.DEFINE_integer("num_epochs", 100, "Number of training epochs (default: 200)") tf.flags.DEFINE_integer( "evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)") tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)") tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)") # Misc Parameters tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") FLAGS = tf.flags.FLAGS FLAGS._parse_flags() print('\nParameters') for attr, value in sorted(FLAGS.__flags.items()): print("{}={}".format(attr.upper(), value)) print("") # 加载数据 # Generate batches # train_manager = batch_manager(FLAGS.train_path, FLAGS.sequence_length, FLAGS.batch_size, FLAGS.num_epochs) # dev_manager = batch_manager(FLAGS.dev_path, FLAGS.sequence_length, FLAGS.batch_size, 1) x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb')) x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb')) vocab_processer = learn.preprocessing.VocabularyProcessor.restore( FLAGS.vocab_path) train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) # dev_x, dev_y, _ = load_data(FLAGS.dev_path, FLAGS.sequence_length) # 构建图,进行训练 with tf.Graph().as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # 构建cnn 节点 cnn = CNNClassification( sequence_length=FLAGS.sequence_length, num_tags=FLAGS.num_tags, vocab_size=len(vocab_processer.vocabulary_), embedding_size=FLAGS.embedding_dim, filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))), num_filters=FLAGS.num_filters, l2_reg_lambda=FLAGS.l2_reg_lambda) # Define Training procedure global_step = tf.Variable(0, name="global_step", trainable=False) # 优化算法 optimizer = tf.train.AdamOptimizer(1e-3) grads_and_vars = optimizer.compute_gradients(cnn.loss) train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step) # Keep track of gradient values and sparsity (optional) grad_summaries = [] for g, v in grads_and_vars: if g is not None: grad_hist_summary = tf.summary.histogram( "{}/grad/hist".format(v.name), g) sparsity_summary = tf.summary.scalar( "{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g)) grad_summaries.append(grad_hist_summary) grad_summaries.append(sparsity_summary) grad_summaries_merged = tf.summary.merge(grad_summaries) # Output directory for models and summaries timestamp = str(int(time.time())) out_dir = os.path.abspath( os.path.join(FLAGS.out_dir, "runs_cnn", timestamp)) print("Writing to {}\n".format(out_dir)) # Summaries for loss and accuracy loss_summary = tf.summary.scalar("loss", cnn.loss) acc_summary = tf.summary.scalar("accuracy", cnn.accuracy) # Train Summaries train_summary_op = tf.summary.merge( [loss_summary, acc_summary, grad_summaries_merged]) train_summary_dir = os.path.join(out_dir, "summaries", "train") train_summary_writer = tf.summary.FileWriter( train_summary_dir, sess.graph) # Dev summaries dev_summary_op = tf.summary.merge([loss_summary, acc_summary]) dev_summary_dir = os.path.join(out_dir, "summaries", "dev") dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph) # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it checkpoint_dir = os.path.abspath( os.path.join(out_dir, "checkpoints")) checkpoint_prefix = os.path.join(checkpoint_dir, "model") if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) # Write vocabulary # vocab_processer.save(os.path.join(out_dir, "vocab")) # Initialize all variables sess.run(tf.global_variables_initializer()) def train_step(x_batch, y_batch): """ A single training step """ feed_dict = { cnn.input_x: x_batch, cnn.input_y: y_batch, cnn.dropout_keep_prob: FLAGS.dropout_keep_prob } # 执行 节点操作 _, step, summaries, loss, accuracy = sess.run([ train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy ], feed_dict) time_str = datetime.datetime.now().isoformat() if step % 20 == 0: print("{}: step {}, loss {:g}, acc {:g}".format( time_str, step, loss, accuracy)) train_summary_writer.add_summary(summaries, step) def dev_step(x_batch, y_batch, writer=None): """ Evaluates model on a dev set """ feed_dict = { cnn.input_x: x_batch, cnn.input_y: y_batch, cnn.dropout_keep_prob: 1.0 } step, summaries, loss, accuracy, correct = sess.run([ global_step, dev_summary_op, cnn.loss, cnn.accuracy, cnn.num_correct ], feed_dict) if writer: writer.add_summary(summaries, step) return loss, accuracy, correct # Training loop. For each batch... best_acc = 0.0 best_step = 0 for batch in train_batches: x_batch, y_batch = zip(*batch) train_step(x_batch, y_batch) # 更新全局步数 current_step = tf.train.global_step(sess, global_step) # 计算评估结果 if current_step % FLAGS.evaluate_every == 0: print("\nEvaluation:") dev_batches = batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1) correct = 0.0 for batch_dev in dev_batches: x_dev_batch, y_dev_batch = zip(*batch_dev) loss_, accuracy_, correct_ = dev_step( x_dev_batch, y_dev_batch, writer=dev_summary_writer) # print(correct_) correct += correct_ # print(dev_manager.length) accuracy_ = correct / len(y_dev) # loss_, accuracy_, correct_ = dev_step(x_dev, y_dev, writer=dev_summary_writer) time_str = datetime.datetime.now().isoformat() print("{}: acc {:g}".format(time_str, accuracy_)) if accuracy_ > best_acc: best_acc = accuracy_ best_step = current_step # 保存模型计算结果 path = saver.save(sess, checkpoint_prefix, global_step=current_step) print("Saved model checkpoint to {}\n".format(path)) print("") print('\nBset dev at {}, accuray {:g}'.format(best_step, best_acc))
def train_cnnrnn(): # Parameters # ================================================== # Data loading params tf.flags.DEFINE_float( "dev_sample_percentage", .1, "Percentage of the training data to use for validation") tf.flags.DEFINE_string("train_path", "thu_train", "Data source.") tf.flags.DEFINE_string("dev_path", "thu_dev", "Data source.") tf.flags.DEFINE_integer('sequence_length', 400, 'length of each sequence') tf.flags.DEFINE_integer("num_tags", 3, "number classes of datasets.") # Model Hyperparameters tf.flags.DEFINE_integer( "embedding_dim", 200, "Dimensionality of character embedding (default: 128)") tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')") tf.flags.DEFINE_boolean('non_static', True, 'non static train word embedding') tf.flags.DEFINE_string("celll", "lstm", "Comma-separated filter sizes (default: '3,4,5')") tf.flags.DEFINE_integer( "num_filters", 200, "Number of filters per filter size (default: 128)") tf.flags.DEFINE_integer("max_pool_size", 4, "max pool size") tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)") tf.flags.DEFINE_integer("hidden_units", 200, "number of RNN hidden cell") tf.flags.DEFINE_string("cell", 'lstm', "Which RNN cell will be used (dedault: lstm)") tf.flags.DEFINE_float("num_rnn_layer", 1, "RNN layers (default: 1)") tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)") # Training parameters tf.flags.DEFINE_integer("batch_size", 32, "Batch Size (default: 64)") tf.flags.DEFINE_integer("num_epochs", 100, "Number of training epochs (default: 200)") tf.flags.DEFINE_integer( "evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)") tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)") tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)") # Misc Parameters tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") FLAGS = tf.flags.FLAGS FLAGS._parse_flags() print('\nParameters') for attr, value in sorted(FLAGS.__flags.items()): print("{}={}".format(attr.upper(), value)) print("") # Generate batches x_train, y_train = pickle.load(open('train', 'rb')) x_dev, y_dev = pickle.load(open('dev', 'rb')) vocab_processer = learn.preprocessing.VocabularyProcessor.restore('vocab') train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) sess = tf.Session(config=session_conf) with sess.as_default(): cnn_rnn = CNNRNNsClassification( embedding_mat=None, vocab_size=len(vocab_processer.vocabulary_), sequence_length=FLAGS.sequence_length, num_tags=FLAGS.num_tags, non_static=FLAGS.non_static, hidden_unit=FLAGS.hidden_units, max_pool_size=FLAGS.max_pool_size, filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))), num_filters=FLAGS.num_filters, embedding_dim=FLAGS.embedding_dim, cell=FLAGS.cell, num_layers=1, l2_reg_lambda=FLAGS.l2_reg_lambda) global_step = tf.Variable(0, name='global_step', trainable=False) optimizer = tf.train.AdamOptimizer(1e-3) grads_and_vars = optimizer.compute_gradients(cnn_rnn.loss) train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step) # Keep track of gradient values and sparsity (optional) grad_summaries = [] for g, v in grads_and_vars: if g is not None: grad_hist_summary = tf.summary.histogram( "{}/grad/hist".format(v.name), g) sparsity_summary = tf.summary.scalar( "{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g)) grad_summaries.append(grad_hist_summary) grad_summaries.append(sparsity_summary) grad_summaries_merged = tf.summary.merge(grad_summaries) # Output directory for models and summaries timestamp = str(int(time.time())) out_dir = os.path.abspath( os.path.join(os.path.curdir, "runs_cnnrnn", timestamp)) print("Writing to {}\n".format(out_dir)) # Summaries for loss and accuracy loss_summary = tf.summary.scalar("loss", cnn_rnn.loss) acc_summary = tf.summary.scalar("accuracy", cnn_rnn.accuracy) # Train Summaries train_summary_op = tf.summary.merge( [loss_summary, acc_summary, grad_summaries_merged]) train_summary_dir = os.path.join(out_dir, "summaries", "train") train_summary_writer = tf.summary.FileWriter( train_summary_dir, sess.graph) # Dev summaries dev_summary_op = tf.summary.merge([loss_summary, acc_summary]) dev_summary_dir = os.path.join(out_dir, "summaries", "dev") dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph) # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it checkpoint_dir = os.path.abspath( os.path.join(out_dir, "checkpoints")) checkpoint_prefix = os.path.join(checkpoint_dir, "model") if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) # write vocabulary # pickle.dumps(word_index, open(os.path.join(out_dir, 'vocab'), 'wb')) # initlize all vraiables sess.run(tf.initialize_all_variables()) def real_len(batches): return [ np.ceil( np.argmin(batch + [0]) * 1.0 / FLAGS.max_pool_size) for batch in batches ] def train_step(x_batch, y_batch): feed_dict = { cnn_rnn.input_x: x_batch, cnn_rnn.input_y: y_batch, cnn_rnn.dropout_keep_prob: FLAGS.dropout_keep_prob, cnn_rnn.batch_size: len(x_batch), cnn_rnn.pad: np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]), cnn_rnn.real_len: real_len(x_batch), } _, step, loss, accuracy = sess.run( [train_op, global_step, cnn_rnn.loss, cnn_rnn.accuracy], feed_dict) time_str = datetime.datetime.now().isoformat() if step % 20 == 0: print("{}: step {}, loss {:g}, acc {:g}".format( time_str, step, loss, accuracy)) def dev_step(x_batch, y_batch): feed_dict = { cnn_rnn.input_x: x_batch, cnn_rnn.input_y: y_batch, cnn_rnn.dropout_keep_prob: 1.0, cnn_rnn.batch_size: len(x_batch), cnn_rnn.pad: np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]), cnn_rnn.real_len: real_len(x_batch), } step, loss, accuracy, num_correct, predictions = sess.run([ global_step, cnn_rnn.loss, cnn_rnn.accuracy, cnn_rnn.num_correct, cnn_rnn.predictions ], feed_dict) # time_str = datetime.datetime.now().isoformat() # print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy)) return accuracy, loss, num_correct, predictions # Training starts here best_accuracy, best_at_step = 0, 0 # Train the model with x_train and y_train for train_batch in train_batches: x_train_batch, y_train_batch = zip(*train_batch) train_step(x_train_batch, y_train_batch) current_step = tf.train.global_step(sess, global_step) # Evaluate the model with x_dev and y_dev if current_step % FLAGS.checkpoint_every == 0: total_dev_correct = 0 dev_batches = batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1) for dev_batch in dev_batches: x_dev_batch, y_dev_batch = zip(*dev_batch) acc, loss, num_dev_correct, predictions = dev_step( x_dev_batch, y_dev_batch) total_dev_correct += num_dev_correct print() accuracy = float(total_dev_correct) / len(y_dev) print('Accuracy on dev set: {}'.format(accuracy)) if accuracy >= best_accuracy: best_accuracy, best_at_step = accuracy, current_step path = saver.save(sess, checkpoint_prefix, global_step=current_step) print('Saved model {} at step {}'.format( path, best_at_step)) print('Best accuracy {} at step {}'.format( best_accuracy, best_at_step)) path = saver.save(sess, checkpoint_prefix, global_step=current_step) print("Saved model checkpoint to {}\n".format(path)) print( 'Training is complete, testing the best model on x_test and y_test' )
def train_cnnrnn(): # Generate batches x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb')) x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb')) vocab_processer = pickle.load(open(FLAGS.vocab_path, 'rb')) train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) session_conf.gpu_options.allow_growth = True sess = tf.Session(config=session_conf) with sess.as_default(): cnn_rnn = CNNRNNsClassification( embedding_mat=None, vocab_size=len(vocab_processer.vocabulary_), sequence_length=FLAGS.sequence_length, num_tags=FLAGS.num_tags, non_static=FLAGS.non_static, hidden_unit=FLAGS.hidden_units, max_pool_size=FLAGS.max_pool_size, filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))), num_filters=FLAGS.num_filters, embedding_dim=FLAGS.embedding_dim, cell=FLAGS.cell, num_layers=1, l2_reg_lambda=FLAGS.l2_reg_lambda) global_step = tf.Variable(0, name='global_step', trainable=False) optimizer = tf.train.AdamOptimizer(1e-3) grads_and_vars = optimizer.compute_gradients(cnn_rnn.loss) train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step) # Keep track of gradient values and sparsity (optional) grad_summaries = [] for g, v in grads_and_vars: if g is not None: grad_hist_summary = tf.summary.histogram( "{}/grad/hist".format(v.name), g) sparsity_summary = tf.summary.scalar( "{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g)) grad_summaries.append(grad_hist_summary) grad_summaries.append(sparsity_summary) grad_summaries_merged = tf.summary.merge(grad_summaries) # Output directory for models and summaries timestamp = str(int(time.time())) out_dir = os.path.abspath( os.path.join(FLAGS.out_dir, "runs_cnnrnn")) print("Writing to {}\n".format(out_dir)) # Summaries for loss and accuracy loss_summary = tf.summary.scalar("loss", cnn_rnn.loss) acc_summary = tf.summary.scalar("accuracy", cnn_rnn.accuracy) # Train Summaries train_summary_op = tf.summary.merge( [loss_summary, acc_summary, grad_summaries_merged]) train_summary_dir = os.path.join(out_dir, "summaries", "train") train_summary_writer = tf.summary.FileWriter( train_summary_dir, sess.graph) # Dev summaries dev_summary_op = tf.summary.merge([loss_summary, acc_summary]) dev_summary_dir = os.path.join(out_dir, "summaries", "dev") dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph) # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it checkpoint_dir = os.path.abspath( os.path.join(out_dir, "checkpoints")) checkpoint_prefix = os.path.join(checkpoint_dir, "model") if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) # write vocabulary # pickle.dumps(word_index, open(os.path.join(out_dir, 'vocab'), 'wb')) # initlize all vraiables # tf.global_variables_initializer sess.run(tf.initialize_all_variables()) def real_len(batches): return [ np.ceil( np.argmin(batch + [0]) * 1.0 / FLAGS.max_pool_size) for batch in batches ] def train_step(x_batch, y_batch): feed_dict = { cnn_rnn.input_x: x_batch, cnn_rnn.input_y: y_batch, cnn_rnn.dropout_keep_prob: FLAGS.dropout_keep_prob, cnn_rnn.batch_size: len(x_batch), cnn_rnn.pad: np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]), cnn_rnn.real_len: real_len(x_batch), } _, step, loss, accuracy = sess.run( [train_op, global_step, cnn_rnn.loss, cnn_rnn.accuracy], feed_dict) time_str = datetime.datetime.now().isoformat() if step % 20 == 0: print("{}: step {}, loss {:g}, acc {:g}".format( time_str, step, loss, accuracy)) def dev_step(x_batch, y_batch): feed_dict = { cnn_rnn.input_x: x_batch, cnn_rnn.input_y: y_batch, cnn_rnn.dropout_keep_prob: 1.0, cnn_rnn.batch_size: len(x_batch), cnn_rnn.pad: np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]), cnn_rnn.real_len: real_len(x_batch), } step, loss, accuracy, num_correct, predictions = sess.run([ global_step, cnn_rnn.loss, cnn_rnn.accuracy, cnn_rnn.num_correct, cnn_rnn.predictions ], feed_dict) # time_str = datetime.datetime.now().isoformat() # print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy)) return accuracy, loss, num_correct, predictions # Training starts here best_accuracy, best_at_step = 0, 0 # Train the model with x_train and y_train for train_batch in train_batches: x_train_batch, y_train_batch = zip(*train_batch) train_step(x_train_batch, y_train_batch) current_step = tf.train.global_step(sess, global_step) # Evaluate the model with x_dev and y_dev if current_step % FLAGS.checkpoint_every == 0: total_dev_correct = 0 dev_batches = batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1) for dev_batch in dev_batches: x_dev_batch, y_dev_batch = zip(*dev_batch) acc, loss, num_dev_correct, predictions = dev_step( x_dev_batch, y_dev_batch) total_dev_correct += num_dev_correct accuracy = float(total_dev_correct) / len(y_dev) print('Accuracy on dev set: {}'.format(accuracy)) if accuracy >= best_accuracy: best_accuracy, best_at_step = accuracy, current_step path = saver.save(sess, checkpoint_prefix, global_step=current_step) print('Saved model {} at step {}'.format( path, best_at_step)) print('Best accuracy {} at step {}'.format( best_accuracy, best_at_step)) path = saver.save(sess, checkpoint_prefix, global_step=current_step) print("Saved model checkpoint to {}\n".format(path)) print( 'Training is complete, testing the best model on x_test and y_test' )
def train_cnns(): """ 使用普通数据加载方式,进行训练,适用于小数据集 :return: """ # 加载数据 # Generate batches # train_manager = batch_manager(FLAGS.train_path, FLAGS.sequence_length, FLAGS.batch_size, FLAGS.num_epochs) # dev_manager = batch_manager(FLAGS.dev_path, FLAGS.sequence_length, FLAGS.batch_size, 1) x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb')) x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb')) term2id, id2term = pickle.load(open(FLAGS.vocab_path, 'rb')) train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) # dev_x, dev_y, _ = load_data(FLAGS.dev_path, FLAGS.sequence_length) # 构建图,进行训练 with tf.Graph().as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # 构建cnn 节点 cnn = CNNClassification(sequence_length=FLAGS.sequence_length, num_tags=FLAGS.num_tags, vocab_size=len(term2id), embedding_size=FLAGS.embedding_dim, filter_sizes=list( map(int, FLAGS.filter_sizes.split(","))), num_filters=FLAGS.num_filters, l2_reg_lambda=FLAGS.l2_reg_lambda) # Define Training procedure global_step = tf.Variable(0, name="global_step", trainable=False) # 优化算法 optimizer = tf.train.AdamOptimizer(1e-3) grads_and_vars = optimizer.compute_gradients(cnn.loss) train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step) # Keep track of gradient values and sparsity (optional) grad_summaries = [] for g, v in grads_and_vars: if g is not None: grad_hist_summary = tf.summary.histogram( "{}/grad/hist".format(v.name), g) sparsity_summary = tf.summary.scalar( "{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g)) grad_summaries.append(grad_hist_summary) grad_summaries.append(sparsity_summary) grad_summaries_merged = tf.summary.merge(grad_summaries) # Output directory for models and summaries timestamp = str(int(time.time())) out_dir = os.path.abspath(os.path.join(FLAGS.out_dir, "runs_cnn")) print("Writing to {}\n".format(out_dir)) # Summaries for loss and accuracy loss_summary = tf.summary.scalar("loss", cnn.loss) acc_summary = tf.summary.scalar("accuracy", cnn.accuracy) # Train Summaries train_summary_op = tf.summary.merge( [loss_summary, acc_summary, grad_summaries_merged]) train_summary_dir = os.path.join(out_dir, "summaries", "train") train_summary_writer = tf.summary.FileWriter( train_summary_dir, sess.graph) # Dev summaries dev_summary_op = tf.summary.merge([loss_summary, acc_summary]) dev_summary_dir = os.path.join(out_dir, "summaries", "dev") dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph) # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it checkpoint_dir = os.path.abspath( os.path.join(out_dir, "checkpoints")) checkpoint_prefix = os.path.join(checkpoint_dir, "model") if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) # Write vocabulary # vocab_processer.save(os.path.join(out_dir, "vocab")) # Initialize all variables sess.run(tf.global_variables_initializer()) def train_step(x_batch, y_batch): """ A single training step """ feed_dict = { cnn.input_x: x_batch, cnn.input_y: y_batch, cnn.dropout_keep_prob: FLAGS.dropout_keep_prob } # 执行 节点操作 _, step, summaries, loss, accuracy = sess.run([ train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy ], feed_dict) time_str = datetime.datetime.now().isoformat() if step % 20 == 0: print("{}: step {}, loss {:g}, acc {:g}".format( time_str, step, loss, accuracy)) train_summary_writer.add_summary(summaries, step) def dev_step(x_batch, y_batch, writer=None): """ Evaluates model on a dev set """ feed_dict = { cnn.input_x: x_batch, cnn.input_y: y_batch, cnn.dropout_keep_prob: 1.0 } step, summaries, loss, accuracy, correct = sess.run([ global_step, dev_summary_op, cnn.loss, cnn.accuracy, cnn.num_correct ], feed_dict) if writer: writer.add_summary(summaries, step) return loss, accuracy, correct # Training loop. For each batch... best_acc = 0.0 best_step = 0 for batch in train_batches: x_batch, y_batch = zip(*batch) train_step(x_batch, y_batch) # 更新全局步数 current_step = tf.train.global_step(sess, global_step) # 计算评估结果 if current_step % FLAGS.evaluate_every == 0: print("\nEvaluation:") dev_batches = batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1) correct = 0.0 for batch_dev in dev_batches: x_dev_batch, y_dev_batch = zip(*batch_dev) loss_, accuracy_, correct_ = dev_step( x_dev_batch, y_dev_batch, writer=dev_summary_writer) # print(correct_) correct += correct_ # print(dev_manager.length) accuracy_ = correct / len(y_dev) # loss_, accuracy_, correct_ = dev_step(x_dev, y_dev, writer=dev_summary_writer) time_str = datetime.datetime.now().isoformat() print("{}: acc {:g}".format(time_str, accuracy_)) if accuracy_ > best_acc: best_acc = accuracy_ best_step = current_step # 保存模型计算结果 path = saver.save(sess, checkpoint_prefix, global_step=current_step) print("Saved model checkpoint to {}\n".format(path)) print("") print('\nBset dev at {}, accuray {:g}'.format(best_step, best_acc))
allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement ) sess = tf.Session(config=session_conf) with sess.as_default(): # 从文件中加载模型 saver = tf.train.import_meta_graph('{}.meta'.format(model_path)) saver.restore(sess, model_path) # 获取变量 input_x = graph.get_operation_by_name('input_x').outputs[0] dropout_keep_prob = graph.get_operation_by_name('dropout_keep_prob').outputs[0] predictions = graph.get_operation_by_name('output/predictons').outputs[0] batches = batch_iter(list(test_x), FLAGS.batch_size, 1, shuffle=False) all_predictions = [] # prediction for x_test_batch in batches: feed_dict = {input_x:x_test_batch, dropout_keep_prob:1.0} batch_pre = sess.run(predictions, feed_dict=feed_dict) all_predictions = np.concatenate([all_predictions, batch_pre]) if test_y is not None: correct_prediction = float(sum(all_predictions == test_y)) print('Total number of test example : {}'.format(len(test_y))) print('Accuracy:{:g}'.format(correct_prediction/float(len(test_y))))