95768fe65dae1b92319a591e2358925b91cb3776,src/models.py,,simple_CNN,#,7

Before Change

    model.add(Dense(32))
    model.add(Activation("relu"))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation("softmax",name="predictions"))
    return model

def attention_CNN(input_shape, num_classes):

After Change

    model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding="same",
                            name="image_array", input_shape=input_shape))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding="same"))
    model.add(BatchNormalization())
    model.add(Activation("relu"))
    model.add(AveragePooling2D(pool_size=(2, 2), padding="same"))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding="same"))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding="same"))
    model.add(BatchNormalization())
    model.add(Activation("relu"))
    model.add(AveragePooling2D(pool_size=(2, 2), padding="same"))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding="same"))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding="same"))
    model.add(BatchNormalization())
    model.add(Activation("relu"))
    model.add(AveragePooling2D(pool_size=(2, 2), padding="same"))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding="same"))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding="same"))
    model.add(BatchNormalization())
    model.add(Activation("relu"))
    model.add(AveragePooling2D(pool_size=(2, 2), padding="same"))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=256, kernel_size=(3, 3), padding="same"))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=num_classes, kernel_size=(3, 3), padding="same"))
    model.add(GlobalAveragePooling2D())
    model.add(Activation("softmax",name="predictions"))
    return model

Instances