How to squeeze or reduce dimensions of a mapdataset in tensorflow-CodePudding

I have a mapdataset element with the following dimensions:

MapDataset element_spec=(TensorSpec(shape=(None, 1, 24), dtype=tf.float32, name=None), TensorSpec(shape=(None, 1, 24), dtype=tf.float32, name=None))

I want to create a functional api as below:

input_ = keras.layers.Input(shape=24)
hidden1 = keras.layers.Dense(30, activation="relu")(input_)
hidden2 = keras.layers.Dense(30, activation="relu")(hidden1)
concat = keras.layers.concatenate([input_, hidden2])
output = keras.layers.Dense(1)(concat)
model = keras.models.Model(inputs=[input_], outputs=[output])

I get the following error

ValueError: Input 0 of layer "model_3" is incompatible with the layer: expected shape=(None, 24), found shape=(None, 1, 24)

How can I reduce the dimensions in the map dataset? I tried [:, -1:, :] or the tf.squeeze() method but it did not work.

CodePudding user response：

You can just apply another map function to your dataset to reduce the dimensions, before feeding your dataset to your model:

def prepare_data(x):
  return tf.random.normal((samples, 1, 24)), tf.random.normal((samples, 1, 24))

def reduce_dimension(x, y):
  return tf.squeeze(x, axis=1), tf.squeeze(y, axis=1)

samples = 50
dataset = tf.data.Dataset.range(samples)

dataset = dataset.map(prepare_data)
print('Before reducing dimension: ', dataset.element_spec)

dataset = dataset.map(reduce_dimension)
print('After reducing dimension: ', dataset.element_spec)

Before reducing dimension:  (TensorSpec(shape=(50, 1, 24), dtype=tf.float32, name=None), TensorSpec(shape=(50, 1, 24), dtype=tf.float32, name=None))
After reducing dimension:  (TensorSpec(shape=(50, 24), dtype=tf.float32, name=None), TensorSpec(shape=(50, 24), dtype=tf.float32, name=None))

Depending on your use case, you could also simply reduce the dimensions in the first map function. Here, I am assuming that your MapDataset already exists.