In [2]:
DATASETS_CV = {1, 2}
DATASETS_ADDITIONAL = {3, 9, 10, 11}
sample_rate = 44100
subsampling_step = 1
min_pitch = 40
max_pitch = 88
onset_group_threshold_seconds = 0.05
image_data_format = 'channels_first'
cqt_configs = [
{
'hop_length': 512,
'fmin': 55.0,
'n_bins': 180,
'bins_per_octave': 36,
'scale': False,
},
]
n_frames_before = 15
n_frames_after = 20
LOSS = 'binary_crossentropy'
OPTIMIZER = 'adam'
METRICS = None
BATCH_SIZE = 256
In [3]:
wav_file_paths_cv, truth_dataset_format_tuples_cv = get_wav_and_truth_files(DATASETS_CV)
wav_file_paths_additional, truth_dataset_format_tuples_additional = get_wav_and_truth_files(DATASETS_ADDITIONAL)
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping AR_Lick11_FN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping AR_Lick11_KN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping AR_Lick11_MN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:135: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset2\audio\desktop.ini, not a .wav file.
warn('Skipping ' + path_to_wav + ', not a .wav file.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping FS_Lick11_FN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping FS_Lick11_KN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping FS_Lick11_MN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping LP_Lick11_FN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping LP_Lick11_KN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping LP_Lick11_MN.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping G63-44104-1111-20675.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping G71-40100-1111-20749.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping G83-45105-1111-20988.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping G91-43103-1111-21064.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping G93-46106-1111-21145.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping P94-43120-1111-41410.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
C:\Users\Michel\FH\IP6\git\music_transcription\onset_detection\read_data.py:133: UserWarning: Skipping P94-44110-1111-41396.wav, no truth found.
warn('Skipping ' + wav_file + ', no truth found.')
In [4]:
folds = []
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
for k, (train_indices, test_indices) in enumerate(k_fold.split(wav_file_paths_cv)):
if k > 0:
print('Skipping split {}'.format(k))
continue
wav_file_paths_train = [wav_file_paths_cv[i] for i in train_indices] + wav_file_paths_additional
truth_dataset_format_tuples_train = [truth_dataset_format_tuples_cv[i] for i in train_indices] + truth_dataset_format_tuples_additional
wav_file_paths_test = [wav_file_paths_cv[i] for i in test_indices]
truth_dataset_format_tuples_test = [truth_dataset_format_tuples_cv[i] for i in test_indices]
data_train, y_train, wav_file_paths_train_valid, truth_dataset_format_tuples_train_valid = read_data_y(
wav_file_paths_train, truth_dataset_format_tuples_train,
sample_rate, subsampling_step,
min_pitch, max_pitch,
onset_group_threshold_seconds=onset_group_threshold_seconds
)
feature_extractor = CnnCqtFeatureExtractor(image_data_format, sample_rate, cqt_configs, n_frames_before, n_frames_after)
list_of_X_train, sample_file_indexes_train = feature_extractor.fit_transform(data_train)
data_test, y_test, wav_file_paths_test_valid, truth_dataset_format_tuples_test_valid = read_data_y(
wav_file_paths_test, truth_dataset_format_tuples_test,
sample_rate, subsampling_step,
min_pitch, max_pitch,
onset_group_threshold_seconds=onset_group_threshold_seconds
)
list_of_X_test, sample_file_indexes_test = feature_extractor.transform(data_test, verbose=True)
folds.append((list_of_X_train, y_train, list_of_X_test, y_test))
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:90: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset2\annotation\AR_NH_IV.xml, pitch 92 is out of range.
warn('Skipping {}, pitch {} is out of range.'.format(path_to_xml, pitch))
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:90: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset2\annotation\AR_NH_IX.xml, pitch 92 is out of range.
warn('Skipping {}, pitch {} is out of range.'.format(path_to_xml, pitch))
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:90: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset2\annotation\FS_NH_IV.xml, pitch 92 is out of range.
warn('Skipping {}, pitch {} is out of range.'.format(path_to_xml, pitch))
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:90: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset2\annotation\FS_NH_IX.xml, pitch 92 is out of range.
warn('Skipping {}, pitch {} is out of range.'.format(path_to_xml, pitch))
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:90: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset2\annotation\LP_NH_IV.xml, pitch 92 is out of range.
warn('Skipping {}, pitch {} is out of range.'.format(path_to_xml, pitch))
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:56: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset3\audio\pathetique_poly.wav, cannot handle stereo signal.
warn('Skipping ' + path_to_wav + ', cannot handle stereo signal.')
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:90: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset3\annotation\pathetique_poly.xml, pitch 30 is out of range.
warn('Skipping {}, pitch {} is out of range.'.format(path_to_xml, pitch))
Creating spectrograms
Fitting standard scalers for each spectrogram and bin
(515965, 180)
3.63677319949
22.0932928692
Standardizing for each spectrogram and bin
-2.02342757837e-16
1.0
(4466, 36, 180)
Reshaping data
(4466, 1, 36, 180)
C:\Users\Michel\FH\IP6\git\music_transcription\pitch_detection\read_data.py:90: UserWarning: Skipping ..\data\IDMT-SMT-GUITAR_V2\dataset2\annotation\LP_NH_IX.xml, pitch 92 is out of range.
warn('Skipping {}, pitch {} is out of range.'.format(path_to_xml, pitch))
Creating spectrograms
(73188, 180)
5.06737530463
27.5702815749
Standardizing for each spectrogram and bin
0.0824521316465
1.19121556558
(633, 36, 180)
Reshaping data
(633, 1, 36, 180)
Skipping split 1
Skipping split 2
Skipping split 3
Skipping split 4
In [8]:
def predict(model, proba_threshold, list_of_X, y, epsilon=1e-7):
proba_matrix = model.predict(list_of_X)
y = proba_matrix > proba_threshold
y = y.astype(np.int8)
# Make sure at least one pitch is returned.
for probas, labels in zip(proba_matrix, y):
if labels.sum() == 0:
max_proba = max(probas)
max_index = np.where(np.logical_and(probas > max_proba - epsilon, probas < max_proba + epsilon))[0][0]
labels[max_index] = 1
return y
def print_metrics(y, y_predicted):
accuracy = round(accuracy_score(y, y_predicted), 4)
print('Accuracy: {}'.format(accuracy))
print(classification_report(y, y_predicted, digits=3,
target_names=[str(pitch) for pitch in range(min_pitch, max_pitch + 1)]))
return accuracy
In [6]:
def create_model_11(list_of_X, n_output_units,
dropout_conv=None, dropout_dense=None,
n_filters=None, filter_size=None, pool_size=None):
inputs = []
conv_blocks = []
for X in list_of_X:
spectrogram = Input(shape=X.shape[1:])
inputs.append(spectrogram)
conv = Conv2D(10, (10, 3), padding='valid')(spectrogram)
conv = Activation('relu')(conv)
conv = MaxPooling2D(pool_size=(6, 3))(conv)
conv = Dropout(0.15)(conv)
conv = Flatten()(conv)
conv_blocks.append(conv)
conv = Conv2D(512, (10, 180), strides=(5, 1), padding='valid')(spectrogram)
conv = Activation('relu')(conv)
conv = MaxPooling2D(pool_size=(2, 1))(conv)
conv = Dropout(0.25)(conv)
conv = Flatten()(conv)
conv_blocks.append(conv)
z = Concatenate()(conv_blocks)
z = Dense(256)(z)
z = Activation('relu')(z)
z = Dropout(0.3)(z)
output = Dense(n_output_units, activation='sigmoid')(z)
model = Model(inputs, output)
model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=METRICS)
model.summary()
return model
In [9]:
list_of_X_train, y_train, list_of_X_test, y_test = folds[0]
n = 10
sample_counts = []
scores = []
for i in range(1, n + 1):
list_of_X_train_i = []
for X in list_of_X_train:
limit = int(X.shape[0] * i / n)
X_cut = X[:limit, :, :, :]
list_of_X_train_i.append(X_cut)
y_train_i = y_train[:limit, :]
model = create_model_11(list_of_X_train_i, max_pitch - min_pitch + 1)
model.fit(list_of_X_train_i, y_train_i,
epochs=1000,
batch_size=BATCH_SIZE,
sample_weight=None,
class_weight=None,
callbacks=[EarlyStopping(monitor='loss', patience=6)],
verbose=0)
y_test_predicted = predict(model, 0.5, list_of_X_test, y_test)
accuracy = print_metrics(y_test, y_test_predicted)
sample_counts.append(limit)
scores.append(accuracy)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_1 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_2 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_2 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_1 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_3 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_3 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_2 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.7725
precision recall f1-score support
40 1.000 0.769 0.870 13
41 0.600 1.000 0.750 3
42 1.000 1.000 1.000 2
43 0.833 1.000 0.909 5
44 1.000 1.000 1.000 4
45 0.900 0.931 0.915 29
46 1.000 1.000 1.000 3
47 1.000 0.667 0.800 21
48 0.962 0.864 0.911 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 0.833 0.833 0.833 6
52 0.975 0.750 0.848 52
53 0.824 0.737 0.778 19
54 0.967 0.906 0.935 32
55 0.969 0.689 0.805 45
56 1.000 0.895 0.944 38
57 0.938 0.667 0.779 45
58 0.286 0.800 0.421 5
59 0.957 0.616 0.750 73
60 0.886 0.795 0.838 39
61 0.966 0.609 0.747 46
62 0.696 0.711 0.703 45
63 0.389 0.875 0.538 8
64 0.930 0.841 0.883 63
65 0.929 0.867 0.897 15
66 0.909 0.714 0.800 14
67 0.533 0.889 0.667 18
68 1.000 0.625 0.769 8
69 0.872 0.872 0.872 39
70 1.000 0.833 0.909 6
71 0.333 0.600 0.429 5
72 0.875 0.700 0.778 10
73 0.250 0.250 0.250 4
74 0.769 0.833 0.800 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 0.333 1.000 0.500 2
79 1.000 0.333 0.500 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 0.000 0.000 0.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 0.000 0.000 0.000 2
avg / total 0.900 0.782 0.826 862
D:\ProgramFiles\Anaconda3_64\lib\site-packages\sklearn\metrics\classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
D:\ProgramFiles\Anaconda3_64\lib\site-packages\sklearn\metrics\classification.py:1115: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_2 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_4 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_5 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_4 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_5 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_3 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_4 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_3 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_6 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_6 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_4 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.8784
precision recall f1-score support
40 1.000 1.000 1.000 13
41 1.000 1.000 1.000 3
42 1.000 1.000 1.000 2
43 0.625 1.000 0.769 5
44 1.000 1.000 1.000 4
45 0.935 1.000 0.967 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 1.000 1.000 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.667 0.800 6
52 1.000 0.942 0.970 52
53 0.864 1.000 0.927 19
54 1.000 0.969 0.984 32
55 0.956 0.956 0.956 45
56 1.000 0.921 0.959 38
57 0.977 0.933 0.955 45
58 0.667 0.800 0.727 5
59 0.985 0.890 0.935 73
60 1.000 0.846 0.917 39
61 1.000 0.739 0.850 46
62 0.780 0.711 0.744 45
63 0.800 1.000 0.889 8
64 0.894 0.937 0.915 63
65 0.929 0.867 0.897 15
66 0.929 0.929 0.929 14
67 0.727 0.889 0.800 18
68 1.000 0.875 0.933 8
69 0.947 0.923 0.935 39
70 1.000 0.833 0.909 6
71 1.000 0.600 0.750 5
72 1.000 0.800 0.889 10
73 1.000 0.500 0.667 4
74 1.000 0.750 0.857 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 0.500 0.667 2
79 1.000 0.333 0.500 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 0.000 0.000 0.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 0.000 0.000 0.000 2
avg / total 0.949 0.901 0.920 862
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_3 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_7 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_8 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_7 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_8 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_5 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_6 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_5 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_9 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_9 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_6 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.9289
precision recall f1-score support
40 1.000 1.000 1.000 13
41 0.600 1.000 0.750 3
42 1.000 1.000 1.000 2
43 0.714 1.000 0.833 5
44 1.000 1.000 1.000 4
45 0.967 1.000 0.983 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 1.000 1.000 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.667 0.800 6
52 0.981 1.000 0.990 52
53 0.905 1.000 0.950 19
54 1.000 0.938 0.968 32
55 0.976 0.911 0.943 45
56 1.000 0.974 0.987 38
57 0.978 0.978 0.978 45
58 0.800 0.800 0.800 5
59 0.946 0.959 0.952 73
60 1.000 0.846 0.917 39
61 1.000 0.935 0.966 46
62 0.896 0.956 0.925 45
63 1.000 0.875 0.933 8
64 0.951 0.921 0.935 63
65 1.000 1.000 1.000 15
66 1.000 0.929 0.963 14
67 0.778 0.778 0.778 18
68 1.000 0.875 0.933 8
69 0.950 0.974 0.962 39
70 0.833 0.833 0.833 6
71 1.000 0.600 0.750 5
72 1.000 0.700 0.824 10
73 1.000 0.500 0.667 4
74 1.000 0.833 0.909 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 0.500 0.667 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 0.000 0.000 0.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 0.000 0.000 0.000 2
avg / total 0.962 0.937 0.947 862
D:\ProgramFiles\Anaconda3_64\lib\site-packages\sklearn\metrics\classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
D:\ProgramFiles\Anaconda3_64\lib\site-packages\sklearn\metrics\classification.py:1115: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_4 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_10 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_11 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_7 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_8 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_10 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_11 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_7 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_8 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_7 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_12 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_12 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_8 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.9273
precision recall f1-score support
40 1.000 1.000 1.000 13
41 0.750 1.000 0.857 3
42 1.000 1.000 1.000 2
43 0.833 1.000 0.909 5
44 1.000 1.000 1.000 4
45 0.967 1.000 0.983 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 0.983 0.991 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.833 0.909 6
52 0.981 1.000 0.990 52
53 0.950 1.000 0.974 19
54 1.000 0.969 0.984 32
55 0.978 0.978 0.978 45
56 1.000 1.000 1.000 38
57 0.978 0.978 0.978 45
58 1.000 0.800 0.889 5
59 0.986 0.945 0.965 73
60 1.000 0.897 0.946 39
61 1.000 0.891 0.943 46
62 0.875 0.933 0.903 45
63 1.000 0.875 0.933 8
64 0.952 0.937 0.944 63
65 1.000 0.800 0.889 15
66 1.000 0.929 0.963 14
67 0.842 0.889 0.865 18
68 1.000 0.750 0.857 8
69 0.950 0.974 0.962 39
70 0.833 0.833 0.833 6
71 1.000 0.600 0.750 5
72 1.000 0.700 0.824 10
73 1.000 0.500 0.667 4
74 1.000 0.750 0.857 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 0.333 0.500 0.400 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 1.000 0.333 0.500 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 0.000 0.000 0.000 2
avg / total 0.971 0.940 0.952 862
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_5 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_13 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_14 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_9 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_10 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_13 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_14 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_9 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_10 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_9 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_15 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_15 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_10 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.94
precision recall f1-score support
40 1.000 1.000 1.000 13
41 1.000 1.000 1.000 3
42 1.000 1.000 1.000 2
43 0.714 1.000 0.833 5
44 1.000 1.000 1.000 4
45 0.935 1.000 0.967 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 1.000 1.000 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.833 0.909 6
52 1.000 1.000 1.000 52
53 0.947 0.947 0.947 19
54 1.000 0.938 0.968 32
55 0.977 0.956 0.966 45
56 1.000 1.000 1.000 38
57 0.978 0.978 0.978 45
58 1.000 0.800 0.889 5
59 0.973 0.986 0.980 73
60 1.000 0.846 0.917 39
61 1.000 0.935 0.966 46
62 0.911 0.911 0.911 45
63 1.000 0.875 0.933 8
64 0.968 0.952 0.960 63
65 1.000 0.933 0.966 15
66 1.000 1.000 1.000 14
67 0.789 0.833 0.811 18
68 1.000 0.875 0.933 8
69 0.927 0.974 0.950 39
70 0.833 0.833 0.833 6
71 1.000 0.600 0.750 5
72 1.000 0.700 0.824 10
73 1.000 0.250 0.400 4
74 1.000 0.833 0.909 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 1.000 1.000 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 1.000 0.333 0.500 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 0.000 0.000 0.000 2
avg / total 0.972 0.945 0.956 862
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_6 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_16 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_17 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_11 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_12 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_16 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_17 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_11 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_12 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_6 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_11 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_18 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_18 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_12 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.9494
precision recall f1-score support
40 1.000 1.000 1.000 13
41 1.000 1.000 1.000 3
42 1.000 1.000 1.000 2
43 0.714 1.000 0.833 5
44 1.000 1.000 1.000 4
45 1.000 1.000 1.000 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 1.000 1.000 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.833 0.909 6
52 1.000 1.000 1.000 52
53 0.905 1.000 0.950 19
54 1.000 0.969 0.984 32
55 0.977 0.956 0.966 45
56 1.000 1.000 1.000 38
57 0.977 0.956 0.966 45
58 1.000 0.800 0.889 5
59 1.000 0.986 0.993 73
60 0.946 0.897 0.921 39
61 1.000 0.935 0.966 46
62 0.894 0.933 0.913 45
63 1.000 0.875 0.933 8
64 1.000 0.968 0.984 63
65 1.000 1.000 1.000 15
66 1.000 1.000 1.000 14
67 0.810 0.944 0.872 18
68 1.000 0.625 0.769 8
69 1.000 0.974 0.987 39
70 1.000 0.833 0.909 6
71 1.000 0.600 0.750 5
72 1.000 0.800 0.889 10
73 1.000 0.500 0.667 4
74 1.000 0.917 0.957 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 1.000 1.000 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 1.000 1.000 1.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 1.000 1.000 1.000 2
avg / total 0.982 0.961 0.969 862
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_7 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_19 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_20 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_13 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_19 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_20 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_13 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_14 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_7 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_13 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_21 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_21 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_14 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.9542
precision recall f1-score support
40 1.000 1.000 1.000 13
41 1.000 1.000 1.000 3
42 1.000 1.000 1.000 2
43 0.714 1.000 0.833 5
44 1.000 1.000 1.000 4
45 1.000 1.000 1.000 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 1.000 1.000 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.667 0.800 6
52 1.000 1.000 1.000 52
53 0.950 1.000 0.974 19
54 1.000 0.969 0.984 32
55 1.000 1.000 1.000 45
56 1.000 0.974 0.987 38
57 0.978 0.978 0.978 45
58 1.000 0.800 0.889 5
59 1.000 0.959 0.979 73
60 1.000 0.846 0.917 39
61 1.000 0.935 0.966 46
62 0.915 0.956 0.935 45
63 1.000 0.750 0.857 8
64 1.000 0.984 0.992 63
65 1.000 1.000 1.000 15
66 1.000 0.929 0.963 14
67 0.842 0.889 0.865 18
68 1.000 0.875 0.933 8
69 0.974 0.974 0.974 39
70 0.833 0.833 0.833 6
71 1.000 0.800 0.889 5
72 1.000 0.800 0.889 10
73 1.000 0.250 0.400 4
74 1.000 0.833 0.909 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 1.000 1.000 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 1.000 1.000 1.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 1.000 0.500 0.667 2
avg / total 0.986 0.956 0.968 862
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_8 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_22 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_23 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_22 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_23 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_15 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_16 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_8 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_15 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_24 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_24 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_16 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.9494
precision recall f1-score support
40 1.000 1.000 1.000 13
41 1.000 1.000 1.000 3
42 1.000 1.000 1.000 2
43 1.000 0.800 0.889 5
44 1.000 1.000 1.000 4
45 1.000 1.000 1.000 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 1.000 1.000 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.833 0.909 6
52 1.000 1.000 1.000 52
53 0.895 0.895 0.895 19
54 1.000 0.938 0.968 32
55 0.978 0.978 0.978 45
56 1.000 0.974 0.987 38
57 0.978 0.978 0.978 45
58 1.000 0.800 0.889 5
59 0.986 1.000 0.993 73
60 0.972 0.897 0.933 39
61 1.000 0.891 0.943 46
62 0.933 0.933 0.933 45
63 1.000 0.750 0.857 8
64 0.954 0.984 0.969 63
65 1.000 0.867 0.929 15
66 1.000 0.929 0.963 14
67 0.875 0.778 0.824 18
68 1.000 0.875 0.933 8
69 1.000 0.974 0.987 39
70 0.833 0.833 0.833 6
71 1.000 0.600 0.750 5
72 1.000 0.700 0.824 10
73 1.000 0.500 0.667 4
74 1.000 1.000 1.000 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 1.000 1.000 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 1.000 1.000 1.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 1.000 1.000 1.000 2
avg / total 0.982 0.951 0.965 862
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_9 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_25 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_26 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_18 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_25 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_26 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_17 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_18 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_9 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_17 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_27 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_27 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_18 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.9526
precision recall f1-score support
40 1.000 1.000 1.000 13
41 1.000 1.000 1.000 3
42 1.000 1.000 1.000 2
43 0.714 1.000 0.833 5
44 1.000 1.000 1.000 4
45 1.000 1.000 1.000 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 0.983 0.991 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.667 0.800 6
52 1.000 1.000 1.000 52
53 0.900 0.947 0.923 19
54 1.000 0.969 0.984 32
55 0.978 1.000 0.989 45
56 1.000 0.974 0.987 38
57 0.978 0.978 0.978 45
58 1.000 0.800 0.889 5
59 1.000 0.986 0.993 73
60 1.000 0.872 0.932 39
61 1.000 0.935 0.966 46
62 0.913 0.933 0.923 45
63 1.000 0.750 0.857 8
64 1.000 0.968 0.984 63
65 0.933 0.933 0.933 15
66 1.000 1.000 1.000 14
67 0.933 0.778 0.848 18
68 1.000 0.625 0.769 8
69 1.000 0.974 0.987 39
70 0.833 0.833 0.833 6
71 1.000 0.600 0.750 5
72 1.000 0.800 0.889 10
73 1.000 0.500 0.667 4
74 1.000 0.917 0.957 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 1.000 1.000 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 1.000 1.000 1.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 1.000 1.000 1.000 2
avg / total 0.986 0.952 0.967 862
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_10 (InputLayer) (None, 1, 36, 180) 0
____________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 10, 27, 178) 310
____________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 512, 6, 1) 922112
____________________________________________________________________________________________________
activation_28 (Activation) (None, 10, 27, 178) 0
____________________________________________________________________________________________________
activation_29 (Activation) (None, 512, 6, 1) 0
____________________________________________________________________________________________________
max_pooling2d_19 (MaxPooling2D) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
max_pooling2d_20 (MaxPooling2D) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
dropout_28 (Dropout) (None, 10, 4, 59) 0
____________________________________________________________________________________________________
dropout_29 (Dropout) (None, 512, 3, 1) 0
____________________________________________________________________________________________________
flatten_19 (Flatten) (None, 2360) 0
____________________________________________________________________________________________________
flatten_20 (Flatten) (None, 1536) 0
____________________________________________________________________________________________________
concatenate_10 (Concatenate) (None, 3896) 0
____________________________________________________________________________________________________
dense_19 (Dense) (None, 256) 997632
____________________________________________________________________________________________________
activation_30 (Activation) (None, 256) 0
____________________________________________________________________________________________________
dropout_30 (Dropout) (None, 256) 0
____________________________________________________________________________________________________
dense_20 (Dense) (None, 49) 12593
====================================================================================================
Total params: 1,932,647.0
Trainable params: 1,932,647.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Accuracy: 0.9479
precision recall f1-score support
40 1.000 1.000 1.000 13
41 1.000 1.000 1.000 3
42 1.000 1.000 1.000 2
43 0.714 1.000 0.833 5
44 1.000 1.000 1.000 4
45 1.000 1.000 1.000 29
46 1.000 1.000 1.000 3
47 1.000 1.000 1.000 21
48 1.000 1.000 1.000 59
49 1.000 1.000 1.000 15
50 1.000 1.000 1.000 43
51 1.000 0.667 0.800 6
52 1.000 1.000 1.000 52
53 0.864 1.000 0.927 19
54 1.000 0.969 0.984 32
55 0.977 0.956 0.966 45
56 1.000 0.921 0.959 38
57 0.978 0.978 0.978 45
58 1.000 0.800 0.889 5
59 1.000 0.973 0.986 73
60 1.000 0.872 0.932 39
61 1.000 0.957 0.978 46
62 0.932 0.911 0.921 45
63 1.000 0.750 0.857 8
64 0.983 0.937 0.959 63
65 0.933 0.933 0.933 15
66 1.000 1.000 1.000 14
67 0.938 0.833 0.882 18
68 1.000 0.625 0.769 8
69 1.000 0.949 0.974 39
70 0.833 0.833 0.833 6
71 1.000 0.600 0.750 5
72 1.000 0.800 0.889 10
73 1.000 0.750 0.857 4
74 1.000 0.833 0.909 12
75 1.000 1.000 1.000 3
76 1.000 1.000 1.000 4
77 1.000 1.000 1.000 1
78 1.000 1.000 1.000 2
79 1.000 1.000 1.000 3
80 0.000 0.000 0.000 0
81 0.000 0.000 0.000 0
82 0.000 0.000 0.000 0
83 1.000 1.000 1.000 3
84 0.000 0.000 0.000 0
85 0.000 0.000 0.000 0
86 0.000 0.000 0.000 0
87 0.000 0.000 0.000 0
88 1.000 1.000 1.000 2
avg / total 0.985 0.947 0.963 862
In [10]:
from matplotlib import pyplot as plt
%matplotlib inline
_ = plt.plot(sample_counts, scores)
Out[10]:
[<matplotlib.lines.Line2D at 0x1f61f6f5240>]
Content source: festivalhopper/music-transcription
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