如何在Tensorflow 2.2中训练多输入的Keras模型?

Tensorflow documentation about models with multiple inputs

import tensorflow as tf
from tensorflow.keras import Input, Model, models, layers


def build_model():
    input1 = Input(shape=(50,), dtype=tf.int32, name='x1')
    input2 = Input(shape=(1,), dtype=tf.float32, name='x2')
    y1 = layers.Embedding(1000, 10, input_length=50)(input1)
    y1 = layers.Flatten()(y1)
    y = layers.Concatenate(axis=1)([y1, input2])
    y = layers.Dense(1)(y)
    return Model(inputs=[input1, input2], outputs=y)

建立这种模式也很好:

model = build_model()
model.compile(loss='mse')
model.summary()

summary() this gist .

def make_dummy_data():
    X1 = tf.data.Dataset.from_tensor_slices(tf.random.uniform([100, 50], maxval=1000, dtype=tf.int32))
    X2 = tf.data.Dataset.from_tensor_slices(tf.random.uniform([100, 1], dtype=tf.float32))
    X = tf.data.Dataset.zip((X1, X2)).map(lambda x1, x2: {'x1': x1, 'x2': x2})
    y_true = tf.data.Dataset.from_tensor_slices(tf.random.uniform([100, 1], dtype=tf.float32))
    return X, y_true


X, y_true = make_dummy_data()
Xy = tf.data.Dataset.zip((X, y_true))
model.fit(Xy, batch_size=32)

……但现在 fit() 失败,并显示不可理解的错误消息(请参阅 full message here

WARNING:tensorflow:Model was constructed with shape (None, 50) for input Tensor("x1:0", shape=(None, 50), dtype=int32), but it was called on an input with incompatible shape (50, 1).

嗯,1号的额外尺寸是从哪里来的?还有,我该怎么摆脱它呢?

还有一件事:通过删除 Embedding -图层确实会突然使模型运行。

如果你想玩弄上面的样品,我准备了 a notebook on Google Colab for it . 感谢任何帮助。