def _test_multi_bleu(self, hypotheses, references, lowercase, expected_bleu): #pylint: disable=R0201 """Runs a multi-bleu test.""" result = bleu.moses_multi_bleu(hypotheses=hypotheses, references=references, lowercase=lowercase) np.testing.assert_almost_equal(result, expected_bleu, decimal=2)
def metric_fn(self, hypotheses, references): return bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
def metric_fn(self, hypotheses, references): return bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
def metric_fn(self, hypotheses, references): score = bleu.moses_multi_bleu(hypotheses, references, lowercase=False) print ("Done calculating BLEU " + str(score)) return score
def _test_multi_bleu(self, hypotheses, references, lowercase, expected_bleu): #pylint: disable=R0201 """Runs a multi-bleu test.""" result = bleu.moses_multi_bleu( hypotheses=hypotheses, references=references, lowercase=lowercase) np.testing.assert_almost_equal(result, expected_bleu, decimal=2)
def metric_fn(self, hypotheses, references): print("hypotheses={}, references={}".format(hypotheses, references)) return bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
def train(self, X, Y, batch_size, no_of_epochs, checkpoint_factor, model_save_path, model_name, mem_fraction=0.5): ''' The training_function for the model. ''' # Setup a tensorboard_writer: tensorboard_writer = self.__get_tensorboard_writer(model_save_path) # setup the data for training: # obtain the padded training data: train_X_field = X[0]; train_X_content = X[1] train_Y = Y; no_of_total_examples = len(train_X_field) # print len(train_X_field), len(train_X_content), len(train_Y) assert len(train_X_field) == len(train_X_content) and len(train_X_field) == len(train_Y), "input data lengths incompatible" ''' Start the actual Training loop: ''' print("\n\nStarting the Training ... ") # use the given memory usage fraction for this training process tensor_config = tf.ConfigProto() tensor_config.gpu_options.allow_growth = False tensor_config.gpu_options.per_process_gpu_memory_fraction = mem_fraction print("tensorflow configuration user:"******"gpu memory fraction used:", mem_fraction) with tf.Session(config=tensor_config, graph=self.graph) as sess: # create a saver object: saver = tf.train.Saver(max_to_keep=3) # create the checkpoint file: checkpoint_file = os.path.join(model_save_path, "checkpoint") # If old weights found, restart the training from there: if(os.path.isfile(checkpoint_file)): # load the saved weights: saver.restore(sess, tf.train.latest_checkpoint(model_save_path)) # load the global_step value from the checkpoint file with open(checkpoint_file, 'r') as checkpoint: path = checkpoint.readline().strip() global_step = int((path.split(':')[1]).split('-')[1][:-1]) # otherwise initialize all the weights else: global_step = 0 sess.run(self.init) # print("uninitialized variables:") # print(sess.run(tf.report_uninitialized_variables())) # run a loop for no_of_epochs iterations: for epoch in range(no_of_epochs): print("------------------------------------------------------------------------------------------------------------") print("current_epoch: ", (epoch + 1)) # Iterate over the batches of the given train data: for batch_no in range(int(np.ceil(float(no_of_total_examples) / batch_size))): # obtain the current batch of data: start = (batch_no * batch_size); end = start + batch_size batch_inp_field = train_X_field[start: end] batch_inp_conte = train_X_content[start: end] batch_inp_label = train_Y[start: end] # pad the current batch of data: inp_field = pad_sequences(batch_inp_field) inp_conte = pad_sequences(batch_inp_conte) inp_label = pad_sequences(batch_inp_label) # extract the sequence lengths of examples in this batch inp_lengths = get_lengths(batch_inp_field) lab_lengths = get_lengths(batch_inp_label) # execute the cost and the train_step _, cost = sess.run([self.train_step, self.loss], feed_dict = { self.inp_field_encodings: inp_field, self.inp_content_encodings: inp_conte, self.inp_label_encodings: inp_label, self.inp_sequence_lengths: inp_lengths, self.lab_sequence_lengths: lab_lengths }) print("Range: ", "[", start, "-", (start + len(inp_field)), "]", " Cost: ", cost) if((global_step + 1) % checkpoint_factor == 0 or global_step == 0): # generate the summary for this batch: sums, predicts = sess.run([self.all_summaries, self.outputs], feed_dict = { self.inp_field_encodings: inp_field, self.inp_content_encodings: inp_conte, self.inp_label_encodings: inp_label, self.inp_sequence_lengths: inp_lengths, self.lab_sequence_lengths: lab_lengths }) # save this generated summary to the summary file tensorboard_writer.add_summary(sums, global_step=global_step) # also save the model saver.save(sess, os.path.join(model_save_path, model_name), global_step=global_step) # print a random sample from this batch: random_index = np.random.randint(len(inp_field)) random_label_sample = inp_label[random_index] random_predicts_sample = np.argmax(predicts, axis = -1)[random_index] hypotheses = reduce(lambda x,y: x + " " + y, [self.content_label_vocabulary[label] for label in random_predicts_sample]) references = reduce(lambda x,y: x + " " + y, [self.content_label_vocabulary[label] for label in random_label_sample]) # print the extracted sample in meaningful format print("\nOriginal Summary: ") print(references) print("\nPredicted Summary: ") print(hypotheses) # calculate the Bleu score print("\nCalculating the Blue score ...") print("Bleu_Score =", moses_multi_bleu(np.array([hypotheses]), np.array([references]))) global_step += 1 print("------------------------------------------------------------------------------------------------------------") print("Training complete ...\n\n")
def train(self, X, Y, batch_size, no_of_epochs, checkpoint_factor, model_save_path, model_name, mem_fraction=0.5): ''' The training_function for the model. ''' # Setup a tensorboard_writer: tensorboard_writer = self.__get_tensorboard_writer(model_save_path) # setup the data for training: # obtain the padded training data: train_X_field = X[0] train_X_content = X[1] train_Y = Y no_of_total_examples = len(train_X_field) # print len(train_X_field), len(train_X_content), len(train_Y) assert len(train_X_field) == len(train_X_content) and len( train_X_field) == len(train_Y), "input data lengths incompatible" ''' Start the actual Training loop: ''' print("\n\nStarting the Training ... ") # use the given memory usage fraction for this training process tensor_config = tf.ConfigProto() tensor_config.gpu_options.allow_growth = False tensor_config.gpu_options.per_process_gpu_memory_fraction = mem_fraction print("tensorflow configuration user:"******"gpu memory fraction used:", mem_fraction) with tf.Session(config=tensor_config, graph=self.graph) as sess: # create a saver object: saver = tf.train.Saver(max_to_keep=3) # create the checkpoint file: checkpoint_file = os.path.join(model_save_path, "checkpoint") # If old weights found, restart the training from there: if (os.path.isfile(checkpoint_file)): # load the saved weights: saver.restore(sess, tf.train.latest_checkpoint(model_save_path)) # load the global_step value from the checkpoint file with open(checkpoint_file, 'r') as checkpoint: path = checkpoint.readline().strip() global_step = int((path.split(':')[1]).split('-')[1][:-1]) # otherwise initialize all the weights else: global_step = 0 sess.run(self.init) # print("uninitialized variables:") # print(sess.run(tf.report_uninitialized_variables())) # run a loop for no_of_epochs iterations: for epoch in range(no_of_epochs): print( "------------------------------------------------------------------------------------------------------------" ) print("current_epoch: ", (epoch + 1)) # Iterate over the batches of the given train data: for batch_no in range( int(np.ceil(float(no_of_total_examples) / batch_size))): # obtain the current batch of data: start = (batch_no * batch_size) end = start + batch_size batch_inp_field = train_X_field[start:end] batch_inp_conte = train_X_content[start:end] batch_inp_label = train_Y[start:end] # pad the current batch of data: inp_field = pad_sequences(batch_inp_field) inp_conte = pad_sequences(batch_inp_conte) inp_label = pad_sequences(batch_inp_label) # extract the sequence lengths of examples in this batch inp_lengths = get_lengths(batch_inp_field) lab_lengths = get_lengths(batch_inp_label) # execute the cost and the train_step _, cost = sess.run( [self.train_step, self.loss], feed_dict={ self.inp_field_encodings: inp_field, self.inp_content_encodings: inp_conte, self.inp_label_encodings: inp_label, self.inp_sequence_lengths: inp_lengths, self.lab_sequence_lengths: lab_lengths }) print("Range: ", "[", start, "-", (start + len(inp_field)), "]", " Cost: ", cost) if ((global_step + 1) % checkpoint_factor == 0 or global_step == 0): # generate the summary for this batch: sums, predicts = sess.run( [self.all_summaries, self.outputs], feed_dict={ self.inp_field_encodings: inp_field, self.inp_content_encodings: inp_conte, self.inp_label_encodings: inp_label, self.inp_sequence_lengths: inp_lengths, self.lab_sequence_lengths: lab_lengths }) # save this generated summary to the summary file tensorboard_writer.add_summary(sums, global_step=global_step) # also save the model saver.save(sess, os.path.join(model_save_path, model_name), global_step=global_step) # print a random sample from this batch: random_index = np.random.randint(len(inp_field)) random_label_sample = inp_label[random_index] random_predicts_sample = np.argmax( predicts, axis=-1)[random_index] hypotheses = reduce(lambda x, y: x + " " + y, [ self.content_label_vocabulary[label] for label in random_predicts_sample ]) references = reduce(lambda x, y: x + " " + y, [ self.content_label_vocabulary[label] for label in random_label_sample ]) # print the extracted sample in meaningful format print("\nOriginal Summary: ") print(references) print("\nPredicted Summary: ") print(hypotheses) # calculate the Bleu score print("\nCalculating the Blue score ...") print( "Bleu_Score =", moses_multi_bleu(np.array([hypotheses]), np.array([references]))) global_step += 1 print( "------------------------------------------------------------------------------------------------------------" ) print("Training complete ...\n\n")