Train_39_Node_Net.py 文件源码

def constructNet(input_dim=784,n_hidden=1000,n_out=1000,nb_filter=50,prob=0.5,lr=0.0001):
    nb_filters=50
    input_img= Input(shape=list(input_dim))
    a = input_img

    a1 = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(1,1),border_mode='same')(a)    
    b = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(1,1),border_mode='same')(a)  #We only use the diagonal output from this, TODO: only filter diagonal
    a2=Lambda(GetDiag, output_shape=out_diag_shape)(b)
    comb=merge([a1,a2],mode='sum')
    comb = BatchNormalization()(comb)  
    a = Activation('relu')(comb)

    l=5
    for i in range(1,l):
        a1 = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(l,l),border_mode='same')(a)    
        b = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(l,l),border_mode='same')(a)  #We only use the diagonal output from this, TODO: only filter diagonal
        a2=Lambda(GetDiag, output_shape=out_diag_shape)(b)
        comb=merge([a1,a2],mode='sum')
        comb = BatchNormalization()(comb)  
        a = Activation('relu')(comb)

    decoded = Convolution2D(1, 1, 1, activation='sigmoid', border_mode='same')(a)
    final=Flatten()(decoded)
    model = Model(input_img, final)
    model.summary()
    model.compile(optimizer='adam', loss='binary_crossentropy')
    return model