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nn.py
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nn.py
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from keras.layers import Conv2D, Dense, Flatten, Input, MaxPool2D
from keras.models import Model
def defin_model(input_shape, n_classes):
assert isinstance(input_shape, list) or isinstance(input_shape, tuple)
assert len(input_shape) == 3
x0 = Input(input_shape)
x = Conv2D(32, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x0)
x = Conv2D(32, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = Conv2D(32, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = MaxPool2D()(x)
x = Conv2D(64, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = Conv2D(64, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = Conv2D(64, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = MaxPool2D()(x)
x = Conv2D(128, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = Conv2D(128, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = Conv2D(128, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = MaxPool2D()(x)
x = Conv2D(256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = Conv2D(256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = MaxPool2D()(x)
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
preds = Dense(n_classes, activation='softmax')(x)
model = Model(inputs=x0, outputs=preds)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return model