demand_simulation_mnist.py 文件源码

def conv_embedding(images, output, other_features = [], dropout_rate=0.1,
                   embedding_dropout=0.1, embedding_l2=0.05, constrain_norm=True):
    print("Building conv net")
    x_embedding = architectures.convnet(images, Dense(64, activation='linear'),
                        dropout_rate=embedding_dropout,
                        activations='relu',
                        l2_rate=embedding_l2, constrain_norm=constrain_norm)

    if len(other_features) > 0:
        embedd = Concatenate(axis=1)([x_embedding] + other_features)
    else:
        embedd = x_embedding
    out = architectures.feed_forward_net(embedd, output,
                        hidden_layers=[32],
                        dropout_rate=dropout_rate,
                        activations='relu', constrain_norm=constrain_norm)
    return out