Created
December 31, 2020 08:36
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| input = Input(shape=(48, 48, 1)) | |
| cnn1 = Conv2D(36, kernel_size=3, activation='relu')(input) | |
| cnn1 = MaxPool2D(pool_size=3, strides=2)(cnn1) | |
| cnn2 = Conv2D(64, kernel_size=3, activation='relu')(cnn1) | |
| cnn2 = MaxPool2D(pool_size=3, strides=2)(cnn2) | |
| cnn3 = Conv2D(128, kernel_size=3, activation='relu')(cnn2) | |
| cnn3 = MaxPool2D(pool_size=3, strides=2)(cnn3) | |
| dense = Flatten()(cnn3) | |
| dense = Dropout(0.3)(dense) | |
| dense = Dense(256, activation='relu')(dense) | |
| output = Dense(7, activation='softmax', name='race', kernel_regularizer=l1(1))(dense) | |
| emotion_model = Model(input, output) | |
| emotion_model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['categorical_accuracy']) |
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