Covid_Cough_Test/models.py at master · Bitsy-Chuck/Covid_Cough_Test · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
from tensorflow.keras import layers
from tensorflow.keras.layers import TimeDistributed
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2
from kapre.time_frequency import Melspectrogram
from kapre.utils import Normalization2D
import tensorflow as tf
import os


N_CLASSES = len(os.listdir('wavfiles'))


def Conv1D(SR=16000, DT=1.0):
    i = layers.Input(shape=(1, int(SR*DT)), name='input')
    x = Melspectrogram(n_dft=512, n_hop=160,
                       padding='same', sr=SR, n_mels=128,
                       fmin=0.0, fmax=SR/2, power_melgram=1.0,
                       return_decibel_melgram=True, trainable_fb=False,
                       trainable_kernel=False,
                       name='melbands')(i)
    x = Normalization2D(str_axis='batch', name='batch_norm')(x)
    x = layers.Permute((2,1,3), name='permute')(x)
    x = TimeDistributed(layers.Conv1D(8, kernel_size=(4), activation='tanh'), name='td_conv_1d_tanh')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_1')(x)
    x = TimeDistributed(layers.Conv1D(16, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_1')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_2')(x)
    x = TimeDistributed(layers.Conv1D(32, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_2')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_3')(x)
    x = TimeDistributed(layers.Conv1D(64, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_3')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_4')(x)
    x = TimeDistributed(layers.Conv1D(128, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_4')(x)
    x = layers.GlobalMaxPooling2D(name='global_max_pooling_2d')(x)
    x = layers.Dropout(rate=0.1, name='dropout')(x)
    x = layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='dense')(x)
    o = layers.Dense(N_CLASSES, activation='softmax', name='softmax')(x)

    model = Model(inputs=i, outputs=o, name='1d_convolution')
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])

    return model


def Conv2D(SR=16000, DT=1.0):
    i = layers.Input(shape=(1, int(SR*DT)), name='input')
    x = Melspectrogram(n_dft=512, n_hop=160,
                       padding='same', sr=SR, n_mels=128,
                       fmin=0.0, fmax=SR/2, power_melgram=1.0,
                       return_decibel_melgram=True, trainable_fb=False,
                       trainable_kernel=False,
                       name='melbands')(i)
    x = Normalization2D(str_axis='batch', name='batch_norm')(x)
    x = layers.Conv2D(8, kernel_size=(7,7), activation='tanh', padding='same', name='conv2d_tanh')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_1')(x)
    x = layers.Conv2D(16, kernel_size=(5,5), activation='relu', padding='same', name='conv2d_relu_1')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_2')(x)
    x = layers.Conv2D(16, kernel_size=(3,3), activation='relu', padding='same', name='conv2d_relu_2')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_3')(x)
    x = layers.Conv2D(32, kernel_size=(3,3), activation='relu', padding='same', name='conv2d_relu_3')(x)
    x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_4')(x)
    x = layers.Conv2D(32, kernel_size=(3,3), activation='relu', padding='same', name='conv2d_relu_4')(x)
    x = layers.Flatten(name='flatten')(x)
    x = layers.Dropout(rate=0.2, name='dropout')(x)
    x = layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='dense')(x)
    o = layers.Dense(N_CLASSES, activation='softmax', name='softmax')(x)

    model = Model(inputs=i, outputs=o, name='2d_convolution')
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])

    return model


def LSTM(SR=16000, DT=1.0):
    i = layers.Input(shape=(1, int(SR*DT)), name='input')
    x = Melspectrogram(n_dft=512, n_hop=160,
                       padding='same', sr=SR, n_mels=128,
                       fmin=0.0, fmax=SR/2, power_melgram=1.0,
                       return_decibel_melgram=True, trainable_fb=False,
                       trainable_kernel=False,
                       name='melbands')(i)
    x = Normalization2D(str_axis='batch', name='batch_norm')(x)
    x = layers.Permute((2,1,3), name='permute')(x)
    x = TimeDistributed(layers.Reshape((-1,)), name='reshape')(x)
    s = TimeDistributed(layers.Dense(64, activation='tanh'),
                        name='td_dense_tanh')(x)
    x = layers.Bidirectional(layers.LSTM(32, return_sequences=True),
                             name='bidirectional_lstm')(s)
    x = layers.concatenate([s, x], axis=2, name='skip_connection')
    x = layers.Dense(64, activation='relu', name='dense_1_relu')(x)
    x = layers.MaxPooling1D(name='max_pool_1d')(x)
    x = layers.Dense(32, activation='relu', name='dense_2_relu')(x)
    x = layers.Flatten(name='flatten')(x)
    x = layers.Dropout(rate=0.2, name='dropout')(x)
    x = layers.Dense(32, activation='relu',
                         activity_regularizer=l2(0.001),
                         name='dense_3_relu')(x)
    o = layers.Dense(N_CLASSES, activation='softmax', name='softmax')(x)

    model = Model(inputs=i, outputs=o, name='long_short_term_memory')
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])

    return model