如何使用单个数据集在 tensorflow keras 中训练多个输入 model

Question

我正在尝试使用具有 3 列的单个大型数据集训练多输入 model - featuers_mlp 、 featuers_lstm 、 labels

（使用 pentastorm 因为数据集非常大）

model：

LEARNING_RATE=0.001
BATCH_SIZE = 256
TRAIN_MAX_STEPS=None
STEPS = None
NUM_EPOCHS = 2
LEAKY_RELU_ALPHA = 0.1


input_layer_mlp = Input(shape=(len(mlp_feature),), name='input_mlp')
input_layer_lstm = Input(shape=(5,6,), name='input_lstm')

l1 = LSTM(64, return_sequences=True, kernel_initializer='glorot_uniform')(input_layer_lstm)
l2 = LSTM(32, return_sequences=False)(l1)
m1 = Dense(32, activation=LeakyReLU(alpha=LEAKY_RELU_ALPHA), kernel_initializer='glorot_uniform')(input_layer_mlp)
m2 = Concatenate()([l2, m1])
m3 = Dense(16, activation=LeakyReLU(alpha=LEAKY_RELU_ALPHA))(m2)
out = Dense(1, activation=LeakyReLU(alpha=LEAKY_RELU_ALPHA))(m3)
optimizer = tf.keras.optimizers.Adam(learning_rate = LEARNING_RATE)

model = tf.keras.Model(inputs=[input_layer_mlp, input_layer_lstm],
                  outputs=[out])

losses = {'out': Huber(delta=1.0)}
metrics = {'out': MeanAbsoluteError()}

model.compile(optimizer=optimizer, loss=losses, metrics = metrics)

培训代码

model = build_model_mlp(in_shape=11)
with penta_train_mlp.make_tf_dataset(transform_spec=transform_spec_fn, 
                                   batch_size=BATCH_SIZE) as train_dataset, \
   penta_test_mlp.make_tf_dataset(transform_spec=transform_spec_fn, 
                                 batch_size=BATCH_SIZE) as val_dataset:
    # tf.keras only accept tuples, not namedtuples
#     print(train_dataset)
    train_dataset_val = train_dataset.map(lambda x: (x.features, x.labels))
    steps_per_epoch = len(penta_train_mlp) // BATCH_SIZE

    val_dataset_val = val_dataset.map(lambda x: (x.features, x.labels))
    validation_steps = max(1, len(penta_test_mlp) // BATCH_SIZE)

    print(f"steps_per_epoch: {steps_per_epoch}, validation_steps: {validation_steps}")

    hist = model.fit(train_dataset_val, 
                     steps_per_epoch=steps_per_epoch,
                     epochs=NUM_EPOCHS,
                     validation_data=val_dataset_val,
                     validation_steps=validation_steps,
                     verbose=2)



# print("Validation Accuracy: {}".format(accuracy))

错误：

 Failed to find data adapter that can handle input: (<class 'tuple'> containing values of types {"<class 'tensorflow.python.data.ops.dataset_ops.DatasetV1Adapter'>"}), <class 'tensorflow.python.data.ops.dataset_ops.DatasetV1Adapter'>

如何创建多输入数据集？

我尝试使用张量切片，但由于数据已经是数据集格式，所以它不起作用

dataset_train = tf.data.Dataset.from_tensor_slices(({'input_mlp': train_mlp_x, 'input_lstm': train_lstm_x}, train_mlp_y)).batch(1)#tf.data.Dataset.from_tensor_slices(((val_mlp_x, data_b), labels)).batch(1).repeat()

error:
 Unbatching a dataset is only supported for rank >= 1

Answer 1

得到了答案

def map_func(x):
    return {
        'input_1': x.input_1,
        'input_2': tf.reshape(x.input_2,(-1,5,6))
    }, x.labels