trying to overfit the model to identify false-negative types
Malar Kannan
parent
1190312def
commit
bb72c4045e
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@ -0,0 +1,77 @@
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bleach==1.5.0
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click==6.7
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cloudpickle==0.4.1
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cycler==0.10.0
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dask==0.15.4
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decorator==4.1.2
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distributed==1.19.3
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entrypoints==0.2.3
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enum34==1.1.6
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futures==3.1.1
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h5py==2.7.1
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HeapDict==1.0.0
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html5lib==0.9999999
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ipykernel==4.6.1
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ipython==6.2.1
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ipython-genutils==0.2.0
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ipywidgets==7.0.3
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jedi==0.11.0
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Jinja2==2.9.6
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jsonschema==2.6.0
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jupyter==1.0.0
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jupyter-client==5.1.0
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jupyter-console==5.2.0
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jupyter-core==4.3.0
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Keras==2.0.8
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locket==0.2.0
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Markdown==2.6.9
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MarkupSafe==1.0
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matplotlib==2.1.0
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mistune==0.7.4
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msgpack-python==0.4.8
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nbconvert==5.3.1
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nbformat==4.4.0
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notebook==5.2.0
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numexpr==2.6.4
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numpy==1.13.3
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pandas==0.20.3
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pandocfilters==1.4.2
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parso==0.1.0
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partd==0.3.8
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pexpect==4.2.1
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pickleshare==0.7.4
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pkg-resources==0.0.0
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progressbar2==3.34.3
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prompt-toolkit==1.0.15
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protobuf==3.4.0
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psutil==5.4.0
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ptyprocess==0.5.2
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PyAudio==0.2.11
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Pygments==2.2.0
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pyparsing==2.2.0
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pysndfile==1.0.0
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python-dateutil==2.6.1
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python-utils==2.2.0
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pytz==2017.2
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PyYAML==3.12
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pyzmq==16.0.2
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qtconsole==4.3.1
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scikit-learn==0.19.0
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scipy==0.19.1
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simplegeneric==0.8.1
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six==1.11.0
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sortedcontainers==1.5.7
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tables==3.4.2
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tblib==1.3.2
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tensorflow==1.3.0
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tensorflow-tensorboard==0.4.0rc1
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terminado==0.6
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testpath==0.3.1
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toolz==0.8.2
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tornado==4.5.2
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tqdm==4.19.4
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traitlets==4.3.2
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wcwidth==0.1.7
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Werkzeug==0.12.2
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widgetsnbextension==3.0.6
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zict==0.1.3
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@ -20,9 +20,10 @@ def siamese_pairs(rightGroup, wrongGroup):
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group1 = [r for (i, r) in rightGroup.iterrows()]
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group2 = [r for (i, r) in wrongGroup.iterrows()]
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rightWrongPairs = [(g1, g2) for g2 in group2 for g1 in group1]
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rightRightPairs = [i for i in itertools.combinations(group1, 2)]
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random.shuffle(rightWrongPairs)
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random.shuffle(rightRightPairs)
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rightRightPairs = [i for i in itertools.combinations(group1, 2)]#+[i for i in itertools.combinations(group2, 2)]
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# random.shuffle(rightWrongPairs)
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# random.shuffle(rightRightPairs)
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# return rightRightPairs[:10],rightWrongPairs[:10]
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return rightRightPairs[:32],rightWrongPairs[:32]
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@ -45,8 +46,7 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0):
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n_records,n_spec,n_features = 0,0,0
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def write_samples(wg,sample_name):
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wg_sampled = reservoir_sample(wg,sample_count) if sample_count > 0 else wg
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word_group_prog = tqdm(wg_sampled,desc='Computing spectrogram')
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word_group_prog = tqdm(wg,desc='Computing spectrogram')
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record_file = './outputs/{}.{}.tfrecords'.format(audio_group,sample_name)
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writer = tf.python_io.TFRecordWriter(record_file)
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for (w, word_group) in word_group_prog:
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@ -100,7 +100,8 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0):
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writer.close()
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word_groups = [i for i in audio_samples.groupby('word')]
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tr_audio_samples,te_audio_samples = train_test_split(word_groups,test_size=0.1)
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wg_sampled = reservoir_sample(word_groups,sample_count) if sample_count > 0 else word_groups
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tr_audio_samples,te_audio_samples = train_test_split(wg_sampled,test_size=0.1)
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write_samples(tr_audio_samples,'train')
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write_samples(te_audio_samples,'test')
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const_file = os.path.join('./outputs',audio_group+'.constants')
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@ -124,7 +125,7 @@ def reservoir_sample(iterable, k):
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sample[j] = item # replace item with gradually decreasing probability
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return sample
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def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,sample_size=100):
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def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,test_size=100):
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records_file = os.path.join('./outputs',audio_group+'.train.tfrecords')
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input_pairs = []
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output_class = []
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@ -160,13 +161,14 @@ def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,sample_si
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# Read test in one-shot
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te_records_file = os.path.join('./outputs',audio_group+'.test.tfrecords')
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te_re_iterator = tf.python_io.tf_record_iterator(path=records_file)
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te_n_records = len([i for i in te_re_iterator])
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te_re_iterator = tf.python_io.tf_record_iterator(path=records_file)
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print('reading tfrecords({}-test)...'.format(audio_group))
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samples = min([sample_size,n_records])
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# samples = n_records
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input_data = np.zeros((samples,2,n_spec,n_features))
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output_data = np.zeros((samples,2))
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random_samples = enumerate(reservoir_sample(te_re_iterator,samples))
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for (i,string_record) in tqdm(random_samples,total=samples):
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test_size = min([test_size,te_n_records]) if test_size > 0 else te_n_records
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input_data = np.zeros((test_size,2,n_spec,n_features))
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output_data = np.zeros((test_size,2))
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random_samples = enumerate(reservoir_sample(te_re_iterator,test_size))
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for (i,string_record) in tqdm(random_samples,total=test_size):
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example = tf.train.Example()
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example.ParseFromString(string_record)
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spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
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@ -187,7 +189,7 @@ def audio_samples_word_count(audio_group='audio'):
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return len(audio_samples.groupby(audio_samples['word']))
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def fix_csv(audio_group='audio'):
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audio_csv_lines = open('./outputs/' + audio_group + '.csv','r').readlines()
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audio_csv_lines = open('./outputs/' + audio_group + '.csv.orig','r').readlines()
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audio_csv_data = [i.strip().split(',') for i in audio_csv_lines]
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proper_rows = [i for i in audio_csv_data if len(i) == 7]
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with open('./outputs/' + audio_group + '.csv','w') as fixed_csv:
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@ -220,10 +222,13 @@ if __name__ == '__main__':
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# read_siamese_tfrecords('story_all')
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# read_siamese_tfrecords('story_words_test')
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# padd_zeros_siamese_tfrecords('story_words')
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# fix_csv()
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# fix_csv('story_words')
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# pickle_constants('story_words')
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# create_spectrogram_tfrecords('audio',sample_count=100)
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read_siamese_tfrecords_generator('audio')
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# create_spectrogram_tfrecords('story_all',sample_count=25)
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create_spectrogram_tfrecords('story_words',sample_count=10)
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# create_spectrogram_tfrecords('audio',sample_count=50)
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# read_siamese_tfrecords_generator('audio')
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# padd_zeros_siamese_tfrecords('audio')
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# create_padded_spectrogram()
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# create_speech_pairs_data()
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@ -2,7 +2,7 @@ from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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# from speech_data import speech_model_data
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from speech_data import read_siamese_tfrecords_oneshot,read_siamese_tfrecords_generator
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from speech_data import read_siamese_tfrecords_generator
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from keras.models import Model,load_model
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from keras.layers import Input, Dense, Dropout, LSTM, Lambda, Concatenate
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from keras.losses import categorical_crossentropy
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@ -14,42 +14,46 @@ from keras.callbacks import TensorBoard, ModelCheckpoint
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from keras import backend as K
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def create_dir(direc):
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import os
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if not os.path.exists(direc):
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os.makedirs(direc)
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def euclidean_distance(vects):
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x, y = vects
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return K.sqrt(
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K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
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def eucl_dist_output_shape(shapes):
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shape1, shape2 = shapes
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return (shape1[0], 1)
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def contrastive_loss(y_true, y_pred):
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'''Contrastive loss from Hadsell-et-al.'06
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http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
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'''
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return K.mean(y_true * K.square(y_pred) +
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(1 - y_true) * K.square(K.maximum(1 - y_pred, 0)))
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# def euclidean_distance(vects):
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# x, y = vects
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# return K.sqrt(
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# K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
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#
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#
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# def eucl_dist_output_shape(shapes):
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# shape1, shape2 = shapes
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# return (shape1[0], 1)
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#
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#
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# def contrastive_loss(y_true, y_pred):
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# '''Contrastive loss from Hadsell-et-al.'06
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# http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
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# '''
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# return K.mean(y_true * K.square(y_pred) +
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# (1 - y_true) * K.square(K.maximum(1 - y_pred, 0)))
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def create_base_rnn_network(input_dim):
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'''Base network to be shared (eq. to feature extraction).
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'''
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inp = Input(shape=input_dim)
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ls1 = LSTM(256, return_sequences=True)(inp)
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ls0 = LSTM(512, return_sequences=True)(inp)
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ls1 = LSTM(256, return_sequences=True)(ls0)
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ls2 = LSTM(128, return_sequences=True)(ls1)
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# ls3 = LSTM(32, return_sequences=True)(ls2)
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ls4 = LSTM(64)(ls2)
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d1 = Dense(128, activation='relu')(ls4)
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d2 = Dense(64, activation='relu')(d1)
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return Model(inp, ls4)
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def compute_accuracy(y_true, y_pred):
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'''Compute classification accuracy with a fixed threshold on distances.
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'''
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pred = y_pred.ravel() < 0.5
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pred = y_pred.ravel() > 0.5
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return np.mean(pred == y_true)
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@ -60,11 +64,12 @@ def accuracy(y_true, y_pred):
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def dense_classifier(processed):
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conc_proc = Concatenate()(processed)
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d1 = Dense(16, activation='relu')(conc_proc)
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d1 = Dense(64, activation='relu')(conc_proc)
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# dr1 = Dropout(0.1)(d1)
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d2 = Dense(8, activation='relu')(d1)
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d2 = Dense(128, activation='relu')(d1)
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d3 = Dense(8, activation='relu')(d2)
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# dr2 = Dropout(0.1)(d2)
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return Dense(2, activation='softmax')(d2)
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return Dense(2, activation='softmax')(d3)
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def siamese_model(input_dim):
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# input_dim = (15, 1654)
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@ -85,10 +90,10 @@ def siamese_model(input_dim):
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def train_siamese(audio_group = 'audio'):
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# the data, shuffled and split between train and test sets
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# tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
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batch_size = 512
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batch_size = 128
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model_dir = './models/'+audio_group
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create_dir(model_dir)
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tr_gen_fn,te_pairs,te_y,n_step,n_features,n_records = read_siamese_tfrecords_generator(audio_group,batch_size,300)
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tr_gen_fn,te_pairs,te_y,n_step,n_features,n_records = read_siamese_tfrecords_generator(audio_group,batch_size,256)
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tr_gen = tr_gen_fn()
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# tr_y = to_categorical(tr_y_e, num_classes=2)
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# te_y = to_categorical(te_y_e, num_classes=2)
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@ -113,12 +118,12 @@ def train_siamese(audio_group = 'audio'):
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cp_file_fmt,
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monitor='val_loss',
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verbose=0,
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save_best_only=False,
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save_weights_only=False,
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save_best_only=True,
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save_weights_only=True,
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mode='auto',
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period=1)
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# train
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rms = RMSprop(lr=0.001)
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rms = RMSprop()#lr=0.001
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model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
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# model.fit(
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# [tr_pairs[:, 0], tr_pairs[:, 1]],
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@ -128,11 +133,11 @@ def train_siamese(audio_group = 'audio'):
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# validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
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# callbacks=[tb_cb, cp_cb])
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model.fit_generator(tr_gen
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,epochs=100
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,epochs=1000
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,steps_per_epoch=n_records//batch_size
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,validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
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,use_multiprocessing=True)
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# ,callbacks=[tb_cb, cp_cb])
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model.save(model_dir+'/siamese_speech_model-final.h5')
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# compute final accuracy on training and test sets
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# y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
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@ -146,4 +151,5 @@ def train_siamese(audio_group = 'audio'):
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if __name__ == '__main__':
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train_siamese()
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train_siamese('story_words')
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# train_siamese('audio')
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