mirror of
https://github.com/JoergFranke/ADNC.git
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110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
# Copyright 2018 Jörg Franke
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import os
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import argparse
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import yaml
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from tqdm import tqdm
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import tensorflow as tf
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from adnc.data.loader import DataLoader
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from adnc.model.mann import MANN
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expt_dir = "experiments/pre_trained/cnn_rc_task/adnc"
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# load config file
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with open(os.path.join(expt_dir, 'config.yml'), 'r') as f:
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configs = yaml.load(f)
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data_set_config = configs['cnn']
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model_config = configs['mann']
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# set batch size to 1 for inference and max_len to maximal value
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BATCH_SIZE = 1
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data_set_config['batch_size'] = BATCH_SIZE
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data_set_config['max_len'] = 20000
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model_config['batch_size'] = BATCH_SIZE
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# load data loader and restore word to index dictionary
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dl = DataLoader(data_set_config)
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valid_loader = dl.get_data_loader('valid', max_len=20000)
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test_loader = dl.get_data_loader('test')
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dl.dataset.load_dictionary(expt_dir)
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# set model config for inference
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model_config['input_size'] = dl.x_size
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model_config['output_size'] = dl.y_size
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model_config['memory_unit_config']['bypass_dropout'] = False
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model_config['input_embedding'] = {'word_idx_dict': dl.dataset.word_idx_dict, 'embedding_size': 100}
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# init MANN model
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model = MANN(model_config)
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# print model/data values
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print("vocabulary size: {}".format(dl.vocabulary_size))
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print("test set length: {}".format(dl.sample_amount('test')))
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print("test batch amount: {}".format(dl.batch_amount('test')))
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print("valid set length: {}".format(dl.sample_amount('valid')))
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print("valid batch amount: {}".format(dl.batch_amount('valid')))
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print("model parameter amount: {}".format(model.parameter_amount))
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print("x size: {}".format(dl.y_size))
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# calculate error
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data = model._data
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answer_idx = tf.placeholder(tf.int64, [data_set_config['batch_size']], name='labels')
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candidates_mask = tf.placeholder(tf.float32, [data_set_config['batch_size'], dl.y_size], name='candidates')
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outputs = model.outputs
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last_output = outputs[-1, :, :]
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masked_output = candidates_mask * last_output
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predictions = tf.nn.softmax(masked_output, dim=-1)
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arg_predictions = tf.arg_max(predictions, dimension=-1)
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equal = tf.cast(tf.equal(arg_predictions, answer_idx), tf.float32)
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error_rate = tf.reduce_mean(equal)
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# init saver
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saver = tf.train.Saver()
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# init session
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conf = tf.ConfigProto()
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conf.gpu_options.allocator_type = 'BFC'
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conf.gpu_options.allow_growth = True
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with tf.Session(config=conf) as sess:
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saver.restore(sess, os.path.join(expt_dir, "model_dump.ckpt"))
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# calculate validation error
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valid_cost = 0
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valid_error = 0
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valid_count = 0
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for _ in tqdm(range(int(dl.batch_amount('valid')))):
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sample = next(valid_loader)
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verror = sess.run([error_rate], feed_dict={data: sample['x'], answer_idx: sample['answer_idx'],
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candidates_mask: sample['candidates']})
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valid_error += verror
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valid_count += 1
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valid_error = valid_error / valid_count
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print("valid: tf mean acc: {}".format(valid_error))
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# calculate test error
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test_cost = 0
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test_error = 0
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test_count = 0
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for _ in tqdm(range(int(dl.batch_amount('test')))):
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sample = next(test_loader)
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terror = sess.run([error_rate], feed_dict={data: sample['x'], answer_idx: sample['answer_idx'],
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candidates_mask: sample['candidates']})
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test_error += terror
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test_count += 1
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test_error = test_error / test_count
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print("test: tf mean acc: {}".format(test_error))
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