model2predict.py 文件源码

def generate_model(args, nb_features, input_length, nb_repeats=1):
    """
    Generate the model.
    """
    emb_weights = np.eye(nb_features)

    model = Sequential()
    model.add(Embedding(input_dim=nb_features, output_dim=nb_features, input_length=input_length,
                        weights=[emb_weights], trainable=False))
    for layer_id in range(args.input_layers):
        model.add(args.cell_type(args.hidden_layers,
                                 return_sequences=layer_id + 1 < args.input_layers))
        model.add(Dropout(args.dropout))

    model.add(RepeatVector(nb_repeats))
    for _ in range(args.output_layers):
        model.add(args.cell_type(args.hidden_layers, return_sequences=True))
        model.add(Dropout(args.dropout))

    model.add(TimeDistributed(Dense(nb_features)))
    model.add(Activation("softmax"))
    model.compile(loss="sparse_categorical_crossentropy",
                  optimizer=args.optimizer,
                  metrics=["accuracy"])
    return model