mirror of
https://github.com/JoergFranke/ADNC.git
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116 lines
4.4 KiB
Python
Executable File
116 lines
4.4 KiB
Python
Executable File
# 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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os.environ["CUDA_VISIBLE_DEVICES"]="" #gpu not required for inference
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import yaml
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import numpy as np
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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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# Choose a pre trained model by uncomment
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# expt_dir = "experiments/pre_trained/babi_task_1/dnc" # DNC trained on bAbI tasks 1
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# expt_dir = "experiments/pre_trained/babi_task_1/adnc" # ADNC trained on bAbI tasks 1
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# expt_dir = "experiments/pre_trained/babi_task_1/biadnc" # BiADNC trained on bAbI tasks 1
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# expt_dir = "experiments/pre_trained/babi_task_all/biadnc" # BiADNC trained on all bAbI tasks
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expt_dir = "experiments/pre_trained/babi_task_all/biadnc_aug16" # BiADNC trained on all bAbI tasks with task 16 augmentation
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config_file = 'config.yml'
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with open(os.path.join(expt_dir, config_file) , 'r') as f:
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configs = yaml.load(f) # load config from file
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dataset_config = configs['babi_task']
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model_config = configs['mann']
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dataset_config['batch_size'] = 1
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model_config['batch_size'] = 1
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dataset_config['threads'] = 1 # only one thread for data loading
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dataset_config['max_len'] = 1921 # set max length to maximal
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dataset_config['augment16'] = False # disable augmentation for inference
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if dataset_config['task_selection'] == ['all']:
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task_list = [i+1 for i in range(20)]
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else:
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task_list = [int(i) for i in dataset_config['task_selection']]
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dl = DataLoader(dataset_config) # load data loader by config
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model_config['input_size'] = dl.x_size # add data size to model config
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model_config['output_size'] = dl.y_size
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model_config['memory_unit_config']['bypass_dropout'] = False # no dropout during inference
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model = MANN(model_config) # load memory augmented neural network model
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data, target, mask = model.feed # create data feed for session run
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word_dict = dl.dataset.word_dict # full dictionary of all tasks
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re_word_dict = dl.dataset.re_word_dict # reverse dictionary
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print("vocabulary size: {}".format(dl.vocabulary_size))
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print("train set length: {}".format(dl.sample_amount('train')))
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print("valid set length: {}".format(dl.sample_amount('valid')))
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print("model parameter amount: {}".format(model.parameter_amount))
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saver = tf.train.Saver()
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conf = tf.ConfigProto()
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conf.gpu_options.per_process_gpu_memory_fraction = 0.8
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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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mean_error = []
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for task in task_list:
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# load data loader for task
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dataset_config['task_selection'] = [task]
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dl = DataLoader(dataset_config, word_dict, re_word_dict)
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valid_loader = dl.get_data_loader('test')
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predictions, targets, masks = [], [], []
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all_corrects, all_overall = 0, 0
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# infer model
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for v in range(int(dl.batch_amount('test'))):
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sample = next(valid_loader)
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prediction = sess.run([model.prediction, ],feed_dict={data: sample['x'],
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target: sample['y'],
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mask: sample['m']})
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predictions.append(prediction)
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targets.append(sample['y'])
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masks.append(sample['m'])
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# calculate mean error rate for task
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for p, t, m in zip(predictions, targets, masks):
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tm = np.argmax(t, axis=-1)
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pm = np.argmax(p, axis=-1)
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corrects = np.equal(tm, pm)
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all_corrects += np.sum(corrects * m)
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all_overall += np.sum(m)
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word_error_rate = 1- (all_corrects/all_overall)
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mean_error.append(word_error_rate)
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print("word error rate task {:2}: {:0.3}".format(task, word_error_rate))
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print("mean word error rate : {:0.3}".format(np.mean(mean_error)))
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