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from keras.layers import Activation, Convolution2D, Dropout, Conv2D |
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from keras.layers import AveragePooling2D, BatchNormalization |
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from keras.layers import GlobalAveragePooling2D |
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from keras.models import Sequential |
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from keras.layers import Flatten |
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from keras.models import Model |
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from keras.layers import Input |
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from keras.layers import MaxPooling2D |
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from keras.layers import SeparableConv2D |
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from keras import layers |
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from keras.regularizers import l2 |
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def simple_CNN(input_shape, num_classes): |
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model = Sequential() |
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model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same', |
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name='image_array', input_shape=input_shape)) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(AveragePooling2D(pool_size=(2, 2), padding='same')) |
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model.add(Dropout(.5)) |
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model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(AveragePooling2D(pool_size=(2, 2), padding='same')) |
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model.add(Dropout(.5)) |
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model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(AveragePooling2D(pool_size=(2, 2), padding='same')) |
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model.add(Dropout(.5)) |
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model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(AveragePooling2D(pool_size=(2, 2), padding='same')) |
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model.add(Dropout(.5)) |
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model.add(Convolution2D(filters=256, kernel_size=(3, 3), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=num_classes, kernel_size=(3, 3), padding='same')) |
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model.add(GlobalAveragePooling2D()) |
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model.add(Activation('softmax',name='predictions')) |
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return model |
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def simpler_CNN(input_shape, num_classes): |
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model = Sequential() |
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model.add(Convolution2D(filters=16, kernel_size=(5, 5), padding='same', |
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name='image_array', input_shape=input_shape)) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=16, kernel_size=(5, 5), |
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strides=(2, 2), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(Dropout(.25)) |
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model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=32, kernel_size=(5, 5), |
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strides=(2, 2), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(Dropout(.25)) |
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model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=64, kernel_size=(3, 3), |
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strides=(2, 2), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(Dropout(.25)) |
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model.add(Convolution2D(filters=64, kernel_size=(1, 1), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=128, kernel_size=(3, 3), |
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strides=(2, 2), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(Dropout(.25)) |
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model.add(Convolution2D(filters=256, kernel_size=(1, 1), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=128, kernel_size=(3, 3), |
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strides=(2, 2), padding='same')) |
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model.add(Convolution2D(filters=256, kernel_size=(1, 1), padding='same')) |
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model.add(BatchNormalization()) |
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model.add(Convolution2D(filters=num_classes, kernel_size=(3, 3), |
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strides=(2, 2), padding='same')) |
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model.add(Flatten()) |
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model.add(Activation('softmax',name='predictions')) |
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return model |
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def tiny_XCEPTION(input_shape, num_classes, l2_regularization=0.01): |
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regularization = l2(l2_regularization) |
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img_input = Input(input_shape) |
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x = Conv2D(5, (3, 3), strides=(1, 1), kernel_regularizer=regularization, |
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use_bias=False)(img_input) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = Conv2D(5, (3, 3), strides=(1, 1), kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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residual = Conv2D(8, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(8, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(8, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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residual = Conv2D(16, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(16, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(16, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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residual = Conv2D(32, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(32, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(32, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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residual = Conv2D(64, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(64, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(64, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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x = Conv2D(num_classes, (3, 3), |
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padding='same')(x) |
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x = GlobalAveragePooling2D()(x) |
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output = Activation('softmax',name='predictions')(x) |
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model = Model(img_input, output) |
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return model |
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def mini_XCEPTION(input_shape, num_classes, l2_regularization=0.01): |
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regularization = l2(l2_regularization) |
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img_input = Input(input_shape) |
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x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, |
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use_bias=False)(img_input) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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residual = Conv2D(16, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(16, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(16, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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residual = Conv2D(32, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(32, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(32, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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residual = Conv2D(64, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(64, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(64, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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residual = Conv2D(128, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(128, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = Activation('relu')(x) |
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x = SeparableConv2D(128, (3, 3), padding='same', |
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kernel_regularizer=regularization, |
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use_bias=False)(x) |
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x = BatchNormalization()(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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x = Conv2D(num_classes, (3, 3), |
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padding='same')(x) |
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x = GlobalAveragePooling2D()(x) |
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output = Activation('softmax',name='predictions')(x) |
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model = Model(img_input, output) |
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return model |
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def big_XCEPTION(input_shape, num_classes): |
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img_input = Input(input_shape) |
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x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False)(img_input) |
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x = BatchNormalization(name='block1_conv1_bn')(x) |
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x = Activation('relu', name='block1_conv1_act')(x) |
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x = Conv2D(64, (3, 3), use_bias=False)(x) |
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x = BatchNormalization(name='block1_conv2_bn')(x) |
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x = Activation('relu', name='block1_conv2_act')(x) |
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residual = Conv2D(128, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x) |
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x = BatchNormalization(name='block2_sepconv1_bn')(x) |
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x = Activation('relu', name='block2_sepconv2_act')(x) |
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x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x) |
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x = BatchNormalization(name='block2_sepconv2_bn')(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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residual = Conv2D(256, (1, 1), strides=(2, 2), |
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padding='same', use_bias=False)(x) |
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residual = BatchNormalization()(residual) |
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x = Activation('relu', name='block3_sepconv1_act')(x) |
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x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x) |
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x = BatchNormalization(name='block3_sepconv1_bn')(x) |
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x = Activation('relu', name='block3_sepconv2_act')(x) |
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x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x) |
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x = BatchNormalization(name='block3_sepconv2_bn')(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) |
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x = layers.add([x, residual]) |
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x = Conv2D(num_classes, (3, 3), |
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padding='same')(x) |
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x = GlobalAveragePooling2D()(x) |
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output = Activation('softmax',name='predictions')(x) |
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model = Model(img_input, output) |
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return model |
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if __name__ == "__main__": |
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input_shape = (64, 64, 1) |
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num_classes = 7 |
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model = simple_CNN((48, 48, 1), num_classes) |
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model.summary() |
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