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@@ -65,36 +65,41 @@ def generate_model():
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model = Sequential()
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- model.add(Conv2D(30, (2, 1), input_shape=input_shape))
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+ model.add(Conv2D(60, (2, 1), input_shape=input_shape))
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model.add(Activation('relu'))
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model.add(BatchNormalization())
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model.add(MaxPooling2D(pool_size=(2, 1)))
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- model.add(Conv2D(20, (2, 1)))
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+ model.add(Conv2D(40, (2, 1)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 1)))
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- model.add(Conv2D(10, (2, 1)))
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+ model.add(Conv2D(30, (2, 1)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 1)))
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model.add(Flatten())
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- model.add(Dense(70, kernel_regularizer=l2(0.01)))
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+ model.add(Dense(150, kernel_regularizer=l2(0.01)))
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model.add(BatchNormalization())
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model.add(Activation('relu'))
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- model.add(Dropout(0.3))
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+ model.add(Dropout(0.2))
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- model.add(Dense(50, kernel_regularizer=l2(0.01)))
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+ model.add(Dense(120, kernel_regularizer=l2(0.01)))
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model.add(BatchNormalization())
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model.add(Activation('relu'))
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- model.add(Dropout(0.4))
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+ model.add(Dropout(0.2))
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- model.add(Dense(30, kernel_regularizer=l2(0.01)))
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+ model.add(Dense(80, kernel_regularizer=l2(0.01)))
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model.add(BatchNormalization())
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model.add(Activation('relu'))
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- model.add(Dropout(0.3))
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+ model.add(Dropout(0.2))
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- model.add(Dense(10, kernel_regularizer=l2(0.01)))
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+ model.add(Dense(40, kernel_regularizer=l2(0.01)))
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+ model.add(BatchNormalization())
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+ model.add(Activation('relu'))
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+ model.add(Dropout(0.2))
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+
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+ model.add(Dense(20, kernel_regularizer=l2(0.01)))
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model.add(BatchNormalization())
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model.add(Activation('relu'))
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model.add(Dropout(0.1))
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