31 lines
1.2 KiB
Markdown
31 lines
1.2 KiB
Markdown
An example that use [[categorical_crossentropy]] and softmax
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```python
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dropRatio = 0.1
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model = Sequential()
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model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(self.DATA_LEN, 1)))
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model.add(Dropout(dropRatio))
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model.add(MaxPooling1D(pool_size=2))
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model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
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model.add(Dropout(dropRatio))
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model.add(MaxPooling1D(pool_size=2))
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model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
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model.add(Dropout(dropRatio))
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model.add(MaxPooling1D(pool_size=2))
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model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
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model.add(MaxPooling1D(pool_size=2))
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model.add(Dropout(dropRatio))
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model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
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model.add(MaxPooling1D(pool_size=2))
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model.add(Dropout(dropRatio))
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model.add(Flatten())
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model.add(Dropout(dropRatio))
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model.add(Dense(units=128, activation='relu'))
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model.add(Dropout(dropRatio))
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model.add(Dense(units=64, activation='relu'))
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model.add(Dropout(dropRatio))
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model.add(Dense(units=32, activation='relu'))
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model.add(Dense(units=len(self.DEVICE_LABEL), activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.01), metrics=['accuracy'])
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``` |