Question

我制作了一个模型并出现了这个错误（它是一个 kaggle notebook），这是错误：

    ValueError: Input 0 of layer bidirectional_1 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (13, 64)

        <ipython-input-25-f223adf5c5a7> in <module>
             12   for i in range(len(scores_transformed_train)):
             13     # Optimize the model
        ---> 14     loss_value, grads = grad(temp_model, title_train[i], 
    scores_transformed_train[i])
             15     optimizer.apply_gradients(zip(grads, temp_model.trainable_variables))
             16

模型：

    encoder = tf.keras.layers.experimental.preprocessing.TextVectorization()

    encoder.adapt(title_train)

    model = tf.keras.Sequential([
        encoder,
        tf.keras.layers.Embedding(
            input_dim=len(encoder.get_vocabulary()),
            output_dim=64,
            mask_zero=True),
        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(1, 'sigmoid')
    ])

我的功能

    loss_object = tf.keras.losses.MeanSquaredError()
    
    def loss(model, x, y, training):
      y_ = model(x, training=training)

      return loss_object(y_true=y, y_pred=y_)


    def grad(model, inputs, targets):
      with tf.GradientTape() as tape:
        loss_value = loss(model, inputs, targets, training=True)
      return loss_value, tape.gradient(loss_value, model.trainable_variables)

    optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)

请帮我修复它。如果需要，我很乐意回答任何其他问题...

Answer 1

通常这种模型中的预期维度是 3。（时间 x 批次 x 功能）。您提供了 2d。

ValueError: 层 bidirectional_1 的输入 0 与层不兼容：预期 ndim=3，发现 ndim=2。收到完整形状：(13, 64)

1 个答案: