cnn.py 文件源码

def prep_model(inputs, N, s0pad, s1pad, c):
    Nc, outputs = B.cnnsum_input(inputs, N, s0pad, siamese=c['cnnsiamese'],
                        dropout=c['dropout'], l2reg=c['l2reg'],
                        cnninit=c['cnninit'], cnnact=c['cnnact'], cdim=c['cdim'])

    # Projection
    if c['project']:
        outputs = Dense(int(N*c['pdim']), kernal_regularizer=l2(c['l2reg']), activation=c['pact'])(outputs)
        # model.add_shared_node(name='proj', inputs=['e0s_', 'e1s_'], outputs=['e0p', 'e1p'],
        #                       layer=Dense(input_dim=Nc, output_dim=int(N*c['pdim']),
        #                                   W_regularizer=l2(c['l2reg']), activation=c['pact']))
        # This dropout is controversial; it might be harmful to apply,
        # or at least isn't a clear win.
        # model.add_shared_node(name='projdrop', inputs=['e0p', 'e1p'], outputs=['e0p_', 'e1p_'],
        #                       layer=Dropout(c['dropout'], input_shape=(N,)))
        # return ('e0p_', 'e1p_')
    return outputs, N