add results and finish up

diff --git a/experiments.py b/experiments.py
index 354f787..833e1c5 100644 (file)
--- a/experiments.py
+++ b/experiments.py
@@ -10,13 +10,14 @@ import numpy as np
 
 # keras
 from keras.models import Sequential
-from keras.layers import Dense, Dropout  # , Activation
+from keras.layers import Dense  # , Dropout  # , Activation
 from keras import backend, utils
 
 # Testset ratio
 testset = 0.10
 samplerate = 16000
-verbosity = 1
+verbosity = 0
+
 
 def get_datafiles():
     files = glob.glob(os.path.join(os.getcwd(), 'textgrid', '*.TextGrid'))
@@ -25,9 +26,11 @@ def get_datafiles():
         num = re.match('^.*/(\\d+).TextGrid$', tg).group(1)
         yield (tg, 'wav/{:02d}.wav'.format(int(num)), int(num))
 
+
 def label_from_annotation(ann):
     return 0 if ann.strip() == '' else 1
 
+
 def features_from_wav(tg, wavp, typ='mfcc', winlen=0.025, winstep=0.01):
     # Load textgrid
     tgob = pympi.TextGrid(tg)
@@ -39,7 +42,8 @@ def features_from_wav(tg, wavp, typ='mfcc', winlen=0.025, winstep=0.01):
         data = mfcc(sig, rate, winlen=winlen, winstep=winstep, numcep=13,
                     appendEnergy=True)
     elif typ == 'fbank':
-        (data, energy) = logfbank(sig, rate, winlen=winlen, winstep=winstep, nfilt=26)
+        (data, energy) = logfbank(
+            sig, rate, winlen=winlen, winstep=winstep, nfilt=26)
     else:
         raise ValueError("No such type")
 
@@ -62,6 +66,7 @@ def features_from_wav(tg, wavp, typ='mfcc', winlen=0.025, winstep=0.01):
         i += 1
     return (data, labels)
 
+
 def singerfun(num, l):
     if l == 1:
         if 0 <= num <= 11:
@@ -75,6 +80,7 @@ def singerfun(num, l):
     else:
         return 0
 
+
 def run(typ, winlen, winstep, modelfun, modelname, multiclass=False):
     datas = []
     labels = []
@@ -88,14 +94,12 @@ def run(typ, winlen, winstep, modelfun, modelname, multiclass=False):
         else:
             labels.append(l)
 
-
     datas = np.concatenate(datas)
     labels = np.concatenate(labels)
     print(np.unique(labels, return_counts=True))
     if multiclass:
         labels = utils.to_categorical(labels, num_classes=4)
 
-
     rng_state = np.random.get_state()
     np.random.shuffle(datas)
     np.random.set_state(rng_state)
@@ -108,48 +112,65 @@ def run(typ, winlen, winstep, modelfun, modelname, multiclass=False):
 
     model = modelfun(traindata)
 
-    #Train
+    # Train
     model.fit(traindata, trainlabels, epochs=10, batch_size=32, shuffle=False,
               verbose=verbosity)
 
-    #Test
+    # Test
     loss, acc = model.evaluate(testdata, testlabels, batch_size=32,
                                verbose=verbosity)
     print('{}\t{}\t{}\t{}\t{}\n'.format(
         winlen, winstep, modelname, loss, acc))
     return model
 
-def bottlemodel(d):
-    model = Sequential()
-    model.add(Dense(13, activation='relu', input_shape=(d.shape[1],)))
-    model.add(Dense(1, activation='sigmoid'))
+
+def bottlemodel(layers):
+    def fun(d):
+        model = Sequential()
+        model.add(Dense(layers, activation='relu', input_shape=(d.shape[1],)))
+        model.add(Dense(1, activation='sigmoid'))
 #    model.add(
 #        Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
 #    model.add(Dense(13, activation='relu'))
 #    model.add(Dense(1, activation='sigmoid'))
-    model.compile(optimizer='rmsprop',
-                  loss='binary_crossentropy',
-                  metrics=['accuracy'])
-    return model
-
-def multimodel(d):
-    model = Sequential()
-#    model.add(Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
-    model.add(Dense(13, activation='relu', input_shape=(d.shape[1],)))
-    model.add(Dense(4, activation='softmax'))
-    model.compile(optimizer='rmsprop',
-                  loss='categorical_crossentropy',
-                  metrics=['accuracy'])
-    return model
-
+        model.compile(optimizer='rmsprop',
+                      loss='binary_crossentropy',
+                      metrics=['accuracy'])
+        return model
+    return fun
+
+
+def multimodel(layers):
+    def fun(d):
+        model = Sequential()
+#  model.add(Dense(d.shape[1]*2, input_shape=(d.shape[1],), activation='relu'))
+        model.add(Dense(layers, activation='relu', input_shape=(d.shape[1],)))
+        model.add(Dense(4, activation='softmax'))
+        model.compile(optimizer='rmsprop',
+                      loss='categorical_crossentropy',
+                      metrics=['accuracy'])
+        return model
+    return fun
+
+
+models = [
+    ('bottle3', bottlemodel(3), False),
+    ('bottle5', bottlemodel(5), False),
+    ('bottle8', bottlemodel(8), False),
+    ('bottle13', bottlemodel(13), False),
+    ('multi3', multimodel(3), True),
+    ('multi5', multimodel(5), True),
+    ('multi8', multimodel(8), True),
+    ('multi13', multimodel(13), True)]
 
 if __name__ == '__main__':
     print('winlen\twinstep\tmodel\tloss\taccuracy\n')
     with backend.get_session():
         for winlen, winstep in ((0.025, 0.01), (0.1, 0.04), (0.2, 0.08)):
-            for name, model, multi in reversed((('bottle', bottlemodel, False), ('multi', multimodel, True))):
+            for name, model, multi in models:
                 m = run('mfcc', winlen, winstep, model, name, multi)
                 fproot = 'model_{}_{}_{}'.format(winlen, winstep, name)
+                print(fproot);
                 with open('{}.json'.format(fproot), 'w') as f:
                     f.write(m.to_json())
                 m.save_weights('{}.hdf5'.format(fproot))

diff --git a/log.txt b/log.txt
new file mode 100644 (file)
index 0000000..a67f009
--- /dev/null
+++ b/log.txt
@@ -0,0 +1,99 @@
+winlen winstep model   loss    accuracy
+
+(array([0, 1]), array([476266, 325691]))
+0.025  0.01    bottle3 0.3389156284828169      0.8597792879855353
+
+model_0.025_0.01_bottle3
+(array([0, 1]), array([476266, 325691]))
+0.025  0.01    bottle5 0.30842042285169746     0.874156742939086
+
+model_0.025_0.01_bottle5
+(array([0, 1]), array([476266, 325691]))
+0.025  0.01    bottle8 0.2965808613601041      0.8830849803611148
+
+model_0.025_0.01_bottle8
+(array([0, 1]), array([476266, 325691]))
+0.025  0.01    bottle13        0.28287798926393115     0.889955732901797
+
+model_0.025_0.01_bottle13
+(array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
+0.025  0.01    multi3  0.48447136703078025     0.8319970072947191
+
+model_0.025_0.01_multi3
+(array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
+0.025  0.01    multi5  0.43238697158434836     0.8497038468739897
+
+model_0.025_0.01_multi5
+(array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
+0.025  0.01    multi8  0.4072758802538003      0.8624353139223143
+
+model_0.025_0.01_multi8
+(array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
+0.025  0.01    multi13 0.3707011521600716      0.868520481326766
+
+model_0.025_0.01_multi13
+(array([0, 1]), array([119037,  81431]))
+0.1    0.04    bottle3 0.32152209714074387     0.8698992317728829
+
+model_0.1_0.04_bottle3
+(array([0, 1]), array([119037,  81431]))
+0.1    0.04    bottle5 0.29785478306865665     0.879028235071257
+
+model_0.1_0.04_bottle5
+(array([0, 1]), array([119037,  81431]))
+0.1    0.04    bottle8 0.31144997012195363     0.8783797266109135
+
+model_0.1_0.04_bottle8
+(array([0, 1]), array([119037,  81431]))
+0.1    0.04    bottle13        0.28727721201615886     0.8858126309547645
+
+model_0.1_0.04_bottle13
+(array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
+0.1    0.04    multi3  0.47640502210221436     0.824603412157997
+
+model_0.1_0.04_multi3
+(array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
+0.1    0.04    multi5  0.44105214603370885     0.8400678439588946
+
+model_0.1_0.04_multi5
+(array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
+0.1    0.04    multi8  0.3903473072779056      0.8636635737803053
+
+model_0.1_0.04_multi8
+(array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
+0.1    0.04    multi13 0.3752127004474934      0.8698493464852026
+
+model_0.1_0.04_multi13
+(array([0, 1]), array([59493, 40723]))
+0.2    0.08    bottle3 0.35470913447088        0.847719788444267
+
+model_0.2_0.08_bottle3
+(array([0, 1]), array([59493, 40723]))
+0.2    0.08    bottle5 0.32276059763122966     0.870272427901407
+
+model_0.2_0.08_bottle5
+(array([0, 1]), array([59493, 40723]))
+0.2    0.08    bottle8 0.2949916362783269      0.8849416225925556
+
+model_0.2_0.08_bottle8
+(array([0, 1]), array([59493, 40723]))
+0.2    0.08    bottle13        0.3005677448865742      0.8803512623490669
+
+model_0.2_0.08_bottle13
+(array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
+0.2    0.08    multi3  0.4804011737202328      0.8227721784312548
+
+model_0.2_0.08_multi3
+(array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
+0.2    0.08    multi5  0.4430828566863061      0.8434287995210059
+
+model_0.2_0.08_multi5
+(array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
+0.2    0.08    multi8  0.39796411239295504     0.8585969464244296
+
+model_0.2_0.08_multi8
+(array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
+0.2    0.08    multi13 0.391048318939632       0.8592954794930646
+
+model_0.2_0.08_multi13
+