BMM_attention_model.py 文件源码

def minst_attention(inc_noise=False, attention=True):
    #make layers
    inputs = Input(shape=(1,image_size,image_size),name='input')

    conv_1a = Convolution2D(32, 3, 3,activation='relu',name='conv_1')
    maxp_1a = MaxPooling2D((3, 3), strides=(2,2),name='convmax_1')
    norm_1a = crosschannelnormalization(name="convpool_1")
    zero_1a = ZeroPadding2D((2,2),name='convzero_1')

    conv_2a = Convolution2D(32,3,3,activation='relu',name='conv_2')
    maxp_2a = MaxPooling2D((3, 3), strides=(2,2),name='convmax_2')
    norm_2a = crosschannelnormalization(name="convpool_2")
    zero_2a = ZeroPadding2D((2,2),name='convzero_2')

    dense_1a = Lambda(global_average_pooling,output_shape=global_average_pooling_shape,name='dense_1')
    dense_2a = Dense(10, activation = 'softmax', init='uniform',name='dense_2')

    #make actual model
    if inc_noise:
        inputs_noise = noise.GaussianNoise(2.5)(inputs)
        input_pad = ZeroPadding2D((1,1),input_shape=(1,image_size,image_size),name='input_pad')(inputs_noise)
    else:
        input_pad = ZeroPadding2D((1,1),input_shape=(1,image_size,image_size),name='input_pad')(inputs)

    conv_1 = conv_1a(input_pad)
    conv_1 = maxp_1a(conv_1)
    conv_1 = norm_1a(conv_1)
    conv_1 = zero_1a(conv_1)

    conv_2_x = conv_2a(conv_1)
    conv_2 = maxp_2a(conv_2_x)
    conv_2 = norm_2a(conv_2)
    conv_2 = zero_2a(conv_2)
    conv_2 = Dropout(0.5)(conv_2)

    dense_1 = dense_1a(conv_2)
    dense_2 = dense_2a(dense_1)

    conv_shape1 = Lambda(change_shape1,output_shape=(32,),name='chg_shape')(conv_2_x)
    find_att = dense_2a(conv_shape1)

    if attention:
        find_att = Lambda(attention_control,output_shape=att_shape,name='att_con')([find_att,dense_2])
    else:
        find_att = Lambda(no_attention_control,output_shape=att_shape,name='att_con')([find_att,dense_2])

    zero_3a = ZeroPadding2D((1,1),name='convzero_3')(find_att)
    apply_attention  = Merge(mode='mul',name='attend')([zero_3a,conv_1])

    conv_3 = conv_2a(apply_attention)
    conv_3 = maxp_2a(conv_3)
    conv_3 = norm_2a(conv_3)
    conv_3 = zero_2a(conv_3)

    dense_3 = dense_1a(conv_3)
    dense_4 = dense_2a(dense_3)

    model = Model(input=inputs,output=dense_4)

    return model