图层（类型）输出形状参数＃连接到

Question

我正在尝试使用Keras（Tensorflow后端）构建模型：

def build_model(args):
    # Define the input nodes
    text1 = build_input_node('text1', args.batch_size, args.time_steps)
    text2 = build_input_node('text2', args.batch_size, args.time_steps)

    # Create the shared LSTM node
    shared_lstm = LSTM(INPUT_SIZE, stateful=args.stateful)

    # Run inputs through shared layer
    encoded1 = shared_lstm(text1)
    encoded2 = shared_lstm(text2)

    # Concatenate outputs to form a tensor of shape (2*batch_size, INPUT_SIZE)
    concatenated = concatenate([encoded1, encoded2], axis=0)

    # Input shape: (2*batch_size, INPUT_SIZE)
    # Output shape: (2*batch_size, batch_size)
    dense1 = Dense(args.batch_size,
                   input_shape=(2 * args.batch_size, INPUT_SIZE),
                   activation='sigmoid')(concatenated)

    # Input shape: (2*batch_size, batch_size)
    # Output shape: (2*batch_size, 1)
    output_shape = (2 * args.batch_size, 1)
    output = Dense(1,
                   input_shape=(2 * args.batch_size, args.batch_size),
                   activation='sigmoid')(dense1)

    model = Model(inputs=[text1, text2], outputs=output)
    optimizer = build_optimizer(name=args.optimizer, lr=args.learning_rate)
    model.compile(loss=args.loss,
                  optimizer=optimizer,
                  metrics=['accuracy'])
    return model, output_shape

应该将输入到模型中的数据重新整形以适合output_shape变量：

def build_datasets(input, time_steps, output_shape):
    T1 = []
    T2 = []
    Y = []
    for sentence1, sentence2, score in input:
        T1.append([t.vector for t in nlp(sentence1)])
        T2.append([t.vector for t in nlp(sentence2)])
        Y.append(np.full(output_shape, score))

    T1 = pad_and_reshape(T1, time_steps)
    T2 = pad_and_reshape(T2, time_steps)

    X = [T1, T2]
    Y = np.asarray(Y)
    # fit the scores between 0 and 1
    Y = expit(Y)
    return X, Y

但是当我调用model.fit(X, Y, epochs=100, batch_size=8)时，它会抛出以下错误：

ValueError：检查目标时出错：期望dense_34有2个维度，但得到的数组有形状（1468,16,1）

其中1468是样本数，16是2 * batch_size。

我做错了什么？如何为输出节点获得正确的形状？

修改模型摘要如下：

图层（类型）输出形状参数＃连接到

text1（InputLayer）（8,15,384）0

text2（InputLayer）（8,15,384）0

lstm_1（LSTM）（8,384）1181184 text1 [0] [0] 文本2 [0] [0]

concatenate_1（Concatenate）（16,384）0 lstm_1 [0] [0] lstm_1 [1] [0]

dense_1（密集）（16,8）3080 concatenate_1 [0] [0]

dense_2（密集）（16,1）9 dense_1 [0] [0]

总参数：1,184,273

可训练的参数：1,184,273

不可训练的参数：0

Answer 1

经过keras代码调试后，我发现keras使用此行调整Y的维度：

y = _standardize_input_data(y, self._feed_output_names,
                            output_shapes,
                            check_batch_axis=False,
                            exception_prefix='target')

反过来调用

data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]

由于我的y形状为(1468, 16, 1)，因此稍后在验证时会出错。

修复方法是将Y.append(np.full(output_shape, score))替换为Y.append(score)。

在Keras中拟合模型时的尺寸误差

图层（类型）输出形状参数＃连接到

1 个答案: