How to use a single dataset to train multiple input model in tensorflow keras

General Discussion
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#1

Hi all, I am trying to train a multi-input model, but have a single large dataset. that has 3 column, featuers_mlp, featuers_lstm, labels

(using pentastorm as the dataset is very large)

code :

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)

Training code

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))

error:

 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'>

How do I create a multi-input dataset?

I tried using tensor slices, but as the data is already in dataset format it does not work

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
#3

solved with
df = pd.DataFrame({
‘input_x’: data_a,
‘input_y’: data_b,
‘label’ : labels
})

1 Like
#5

I have a similar question on how to use single dataset from a tensor to feed two input RNN. The dataset tensor format is as follow:
<PrefetchDataset element_spec=((TensorSpec(shape=(None, None, 300), dtype=tf.float32, name=None), TensorSpec(shape=(None, None, 300), dtype=tf.float32, name=None)), (TensorSpec(shape=(None, None), dtype=tf.int32, name=None), TensorSpec(shape=(None, None), dtype=tf.int32, name=None)))>

forward_inputs, backward_inputs, forward_targets, backward_targets

Is there an argument within the Input layer for multiple input or I have to create two RNN inputs and if I do have to create 2 inputs, how do I assign each dataset within tensor to the inputs of the Input layer?