decreasing first layer node count to avoid gpu memory overflow

2. clean-up

requirements-linux.txt

@@ -40,6 +40,7 @@ parso==0.1.0
 partd==0.3.8
 pexpect==4.2.1
 pickleshare==0.7.4
+pkg-resources==0.0.0
 progressbar2==3.34.3
 prompt-toolkit==1.0.15
 protobuf==3.4.0
@@ -57,6 +58,7 @@ pyzmq==16.0.2
 qtconsole==4.3.1
 scikit-learn==0.19.0
 scipy==0.19.1
+seaborn==0.8.1
 simplegeneric==0.8.1
 six==1.11.0
 sortedcontainers==1.5.7

speech_data.py

@@ -21,10 +21,21 @@ def siamese_pairs(rightGroup, wrongGroup):
     group2 = [r for (i, r) in wrongGroup.iterrows()]
     rightWrongPairs = [(g1, g2) for g2 in group2 for g1 in group1]+[(g2, g1) for g2 in group2 for g1 in group1]
     rightRightPairs = [i for i in itertools.permutations(group1, 2)]#+[i for i in itertools.combinations(group2, 2)]
-    # random.shuffle(rightWrongPairs)
-    # random.shuffle(rightRightPairs)
+    def filter_criteria(s1,s2):
+        same = s1['variant'] == s2['variant']
+        phon_same = s1['phonemes'] == s2['phonemes']
+        voice_diff = s1['voice'] != s2['voice']
+        if not same and phon_same:
+            return False
+        if same and not voice_diff:
+            return False
+        return True
+    validRWPairs = [i for i in rightWrongPairs if filter_criteria(*i)]
+    validRRPairs = [i for i in rightRightPairs if filter_criteria(*i)]
+    random.shuffle(validRWPairs)
+    random.shuffle(validRRPairs)
     # return rightRightPairs[:10],rightWrongPairs[:10]
-    return rightRightPairs[:32],rightWrongPairs[:32]
+    return validRWPairs[:32],validRRPairs[:32]
 
 
 def _float_feature(value):
@@ -41,7 +52,7 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0,train_test_r
     http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
     http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfrecord.html
     '''
-    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv',index_col=0)
+    audio_samples = pd.read_csv( './outputs/' + audio_group + '.fixed.csv',index_col=0)
     audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
     n_records,n_spec,n_features = 0,0,0
 
@@ -60,13 +71,6 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0,train_test_r
             for (output,group) in groups:
                 group_prog = tqdm(group,desc='Writing Spectrogram')
                 for sample1,sample2 in group_prog:
-                    same = sample1['variant'] == sample2['variant']
-                    phon_same = sample1['phonemes'] == sample2['phonemes']
-                    voice_diff = sample1['voice'] != sample2['voice']
-                    if not same and phon_same:
-                        continue
-                    if same and not voice_diff:
-                        continue
                     group_prog.set_postfix(output=output
                         ,var1=sample1['variant']
                         ,var2=sample2['variant'])
@@ -205,19 +209,19 @@ def record_generator_count(records_file):
     return record_iterator,count
 
 def fix_csv(audio_group='audio'):
-    audio_csv_lines = open('./outputs/' + audio_group + '.csv.orig','r').readlines()
+    audio_csv_lines = open('./outputs/' + audio_group + '.csv','r').readlines()
     audio_csv_data = [i.strip().split(',') for i in audio_csv_lines]
     proper_rows = [i for i in audio_csv_data if len(i) == 7]
-    with open('./outputs/' + audio_group + '.csv','w') as fixed_csv:
+    with open('./outputs/' + audio_group + '.fixed.csv','w') as fixed_csv:
         fixed_csv_w = csv.writer(fixed_csv, quoting=csv.QUOTE_MINIMAL)
         fixed_csv_w.writerows(proper_rows)
-    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
+    audio_samples = pd.read_csv( './outputs/' + audio_group + '.fixed.csv'
         , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file'])
     audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
     audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists)
     audio_samples = audio_samples[audio_samples['file_exists'] == True]
     audio_samples = audio_samples.drop(['file_path','file_exists'],axis=1).reset_index(drop=True)
-    audio_samples.to_csv('./outputs/' + audio_group + '.csv')
+    audio_samples.to_csv('./outputs/' + audio_group + '.fixed.csv')
 
 def convert_old_audio():
     audio_samples = pd.read_csv( './outputs/audio.csv.old'
@@ -243,7 +247,8 @@ if __name__ == '__main__':
     # create_spectrogram_tfrecords('audio',sample_count=100)
     # create_spectrogram_tfrecords('story_all',sample_count=25)
     # fix_csv('story_words_test')
-    create_spectrogram_tfrecords('story_words_test',sample_count=100,train_test_ratio=0.0)
+    #fix_csv('story_phrases')
+    create_spectrogram_tfrecords('story_phrases',sample_count=10,train_test_ratio=0.1)
     # create_spectrogram_tfrecords('audio',sample_count=50)
     # read_siamese_tfrecords_generator('audio')
     # padd_zeros_siamese_tfrecords('audio')

speech_model.py

@@ -1,48 +1,28 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-# from speech_data import speech_model_data
 from speech_data import read_siamese_tfrecords_generator
 from keras.models import Model,load_model,model_from_yaml
-from keras.layers import Input, Dense, Dropout, LSTM, Lambda, Concatenate
+from keras.layers import Input, Dense, Dropout, LSTM, Lambda, Concatenate, Bidirectional
 from keras.losses import categorical_crossentropy
-# from keras.losses import binary_crossentropy
 from keras.utils import to_categorical
-# from keras.utils.np_utils import to_categorical
 from keras.optimizers import RMSprop
 from keras.callbacks import TensorBoard, ModelCheckpoint
 from keras import backend as K
 from speech_tools import create_dir,step_count
 
-# def euclidean_distance(vects):
-#     x, y = vects
-#     return K.sqrt(
-#         K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
-#
-#
-# def eucl_dist_output_shape(shapes):
-#     shape1, shape2 = shapes
-#     return (shape1[0], 1)
-#
-#
-# def contrastive_loss(y_true, y_pred):
-#     '''Contrastive loss from Hadsell-et-al.'06
-#     http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
-#     '''
-#     return K.mean(y_true * K.square(y_pred) +
-#                   (1 - y_true) * K.square(K.maximum(1 - y_pred, 0)))
 
 def create_base_rnn_network(input_dim):
     '''Base network to be shared (eq. to feature extraction).
     '''
     inp = Input(shape=input_dim)
     # ls0 = LSTM(512, return_sequences=True)(inp)
-    ls1 = LSTM(256, return_sequences=True)(inp)
+    ls1 = Bidirectional(LSTM(128, return_sequences=True))(inp)
     ls2 = LSTM(128, return_sequences=True)(ls1)
     # ls3 = LSTM(32, return_sequences=True)(ls2)
     ls4 = LSTM(64)(ls2)
     # d1 = Dense(128, activation='relu')(ls4)
-    d2 = Dense(64, activation='relu')(ls4)
+    #d2 = Dense(64, activation='relu')(ls2)
     return Model(inp, ls4)
 
 
@@ -68,7 +48,6 @@ def dense_classifier(processed):
     return Dense(2, activation='softmax')(d3)
 
 def siamese_model(input_dim):
-    # input_dim = (15, 1654)
     base_network = create_base_rnn_network(input_dim)
     input_a = Input(shape=input_dim)
     input_b = Input(shape=input_dim)
@@ -76,10 +55,6 @@ def siamese_model(input_dim):
     processed_b = base_network(input_b)
     final_output = dense_classifier([processed_a,processed_b])
     model = Model([input_a, input_b], final_output)
-    # distance = Lambda(
-    #     euclidean_distance,
-    #     output_shape=eucl_dist_output_shape)([processed_a, processed_b])
-    # model = Model([input_a, input_b], distance)
     return model
 
 def write_model_arch(mod,mod_file):
@@ -94,17 +69,13 @@ def load_model_arch(mod_file):
     return mod
 
 def train_siamese(audio_group = 'audio'):
-    # the data, shuffled and split between train and test sets
-    # tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
-    batch_size = 256
+    batch_size = 128
     model_dir = './models/'+audio_group
     create_dir(model_dir)
     log_dir = './logs/'+audio_group
     create_dir(log_dir)
     tr_gen_fn,te_pairs,te_y,n_step,n_features,n_records = read_siamese_tfrecords_generator(audio_group,batch_size=batch_size,test_size=batch_size)
     tr_gen = tr_gen_fn()
-    # tr_y = to_categorical(tr_y_e, num_classes=2)
-    # te_y = to_categorical(te_y_e, num_classes=2)
     input_dim = (n_step, n_features)
 
     model = siamese_model(input_dim)
@@ -131,29 +102,17 @@ def train_siamese(audio_group = 'audio'):
         mode='auto',
         period=1)
     # train
-    rms = RMSprop()#lr=0.001
+    rms = RMSprop()
     model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
     write_model_arch(model,model_dir+'/siamese_speech_model_arch.yaml')
-    # model.fit(
-    #     [tr_pairs[:, 0], tr_pairs[:, 1]],
-    #     tr_y,
-    #     batch_size=128,
-    #     epochs=100,
-    #     validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
-    #     callbacks=[tb_cb, cp_cb])
     epoch_n_steps = step_count(n_records,batch_size)
     model.fit_generator(tr_gen
                         , epochs=1000
                         , steps_per_epoch=epoch_n_steps
                         , validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
-                        # ,use_multiprocessing=True, workers=1
                         , max_queue_size=32
                         , callbacks=[tb_cb, cp_cb])
     model.save(model_dir+'/siamese_speech_model-final.h5')
-    # compute final accuracy on training and test sets
-    # y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
-    # tr_acc = compute_accuracy(tr_y, y_pred)
-    # print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
 
     y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
     te_acc = compute_accuracy(te_y, y_pred)
@@ -162,5 +121,4 @@ def train_siamese(audio_group = 'audio'):
 
 
 if __name__ == '__main__':
-    train_siamese('story_words_test')
-    # train_siamese('audio')
+    train_siamese('story_phrases')

speech_test.py

@@ -12,6 +12,7 @@ import tensorflow as tf
 import csv
 from tqdm import tqdm
 from speech_data import padd_zeros
+import seaborn as sns
 
 def predict_recording_with(m,sample_size=15):
     spec1 = record_spectrogram(n_sec=1.4)
@@ -35,6 +36,7 @@ def evaluate_siamese(records_file,audio_group='audio',weights = 'siamese_speech_
     print('evaluating {}...'.format(records_file))
     model = load_model_arch(arch_file)
     # model = siamese_model((n_spec, n_features))
+    n_spec = 422
     model.load_weights(weight_file)
     record_iterator,records_count = record_generator_count(records_file)
     total,same_success,diff_success,skipped,same_failed,diff_failed = 0,0,0,0,0,0
@@ -130,22 +132,22 @@ def play_results(audio_group='audio'):
 
 def visualize_results(audio_group='audio'):
     # %matplotlib inline
-    audio_group = 'story_words'
+    audio_group = 'story_phrases'
     result = pd.read_csv('./outputs/' + audio_group + '.results.csv',index_col=0)
     result.groupby('success').size().plot(kind='bar')
     result.describe(include=['object'])
     failed = result[result['success'] == False]
     same_failed = failed[failed['variant1'] == failed['variant2']]
     diff_failed = failed[failed['variant1'] != failed['variant2']]
-    same_failed[same_failed['voice1'] != same_failed['voice2']]
-    diff_failed[diff_failed['voice1'] != diff_failed['voice2']]
+    result.groupby(['voice1','voice2']).size()
 
 
 if __name__ == '__main__':
-    # evaluate_siamese('./outputs/story_words_test.train.tfrecords',audio_group='story_words',weights ='siamese_speech_model-712-epoch-0.00-acc.h5')
+    # evaluate_siamese('./outputs/story_words_test.train.tfrecords',audio_group='story_words.gpu',weights ='siamese_speech_model-58-epoch-0.00-acc.h5')
     # evaluate_siamese('./outputs/story_words.test.tfrecords',audio_group='story_words',weights ='siamese_speech_model-675-epoch-0.00-acc.h5')
+    evaluate_siamese('./outputs/story_phrases.test.tfrecords',audio_group='story_phrases',weights ='siamese_speech_model-329-epoch-0.00-acc.h5')
     # play_results('story_words')
-    visualize_results('story_words')
+    visualize_results('story_words.gpu')
 # test_with('rand_edu')
 # sunflower_data,sunflower_result = get_word_pairs_data('sweater',15)
 # print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1))