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joelouismarino/googlenet_keras_inception.py

Created July 30, 2017 04:04
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googlenet_keras_inception.py
inception_3a_1x1 = Convolution2D(64,1,1, border_mode='same', activation='relu', name='inception_3a/1x1')(pool2_3x3_s2)
inception_3a_3x3_reduce = Convolution2D(96,1,1, border_mode='same', activation='relu', name='inception_3a/3x3_reduce')(pool2_3x3_s2)
inception_3a_3x3 = Convolution2D(128,3,3, border_mode='same', activation='relu', name='inception_3a/3x3')(inception_3a_3x3_reduce)
inception_3a_5x5_reduce = Convolution2D(16,1,1, border_mode='same', activation='relu', name='inception_3a/5x5_reduce')(pool2_3x3_s2)
inception_3a_5x5 = Convolution2D(32,5,5, border_mode='same', activation='relu', name='inception_3a/5x5')(inception_3a_5x5_reduce)
inception_3a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), border_mode='same', name='inception_3a/pool')(pool2_3x3_s2)
inception_3a_pool_proj = Convolution2D(32,1,1, border_mode='same', activation='relu', name='inception_3a/pool_proj')(inception_3a_pool)
inception_3a_output = merge([inception_3a_1x1, inception_3a_3x3, inception_3a_5x5, inception_3a_pool_proj], mode='concat', concat_axis=1, name='inception_3a/output')
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