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@pranav6670
Last active August 18, 2019 07:40
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Audio Classification Model
def conv_model():
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', strides=(1, 1),
padding='same', input_shape=input_shape))
model.add(Conv2D(32, (3, 3), activation='relu', strides=(1,1),
padding='same'))
model.add(Conv2D(64, (3, 3), activation='relu', strides=(1,1),
padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu', strides=(1,1),
padding='same'))
model.add(MaxPool2D(2, 2))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(8, activation='softmax')) # Change
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['acc'])
return model
def recurrent_model():
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape=input_shape))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(0.5))
model.add(TimeDistributed(Dense(64, activation='relu')))
model.add(TimeDistributed(Dense(32, activation='relu')))
model.add(TimeDistributed(Dense(16, activation='relu')))
model.add(TimeDistributed(Dense(8, activation='relu')))
model.add(Flatten())
model.add(Dense(8, activation='softmax')) # Change
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['acc'])
return model
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