[FIXED] Input multiple datasets to tensorflow model

Issue

Hi I'm trying to input multiple datasets in a model. This is an example of my problem, however in my case one of my models has 2 input parameters while the other one has one. The error I get in my case is :

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

Code:

import tensorflow as tf

# Create first model
model1 = tf.keras.Sequential()
model1.add(tf.keras.layers.Dense(1))
model1.compile()
model1.build([None,3])

# Create second model
model2 = tf.keras.Sequential()
model2.add(tf.keras.layers.Dense(1))
model2.compile()
model2.build([None,3])


# Concatenate
fusion_model = tf.keras.layers.Concatenate()([model1.output, model2.output])
t = tf.keras.layers.Dense(1, activation='tanh')(fusion_model)
model = tf.keras.models.Model(inputs=[model1.input, model2.input], outputs=t)
model.compile()

#Datasets
ds1 = tf.data.Dataset.from_tensors(([1,2,3],1))
ds2 = tf.data.Dataset.from_tensors(([1,2,3], 2))

print(ds1)
print(ds2)
# Fit
model.fit([ds1,ds2])

Running this example code produces that:

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

I need to use the dataset modules because they provide in built lazy loading of the data.

Solution

As noted in the comment, the TensorFlow .fit function in TensorFlow models does not support a list of Datasets.

If you really want to use Datasets, you could use a dictionary as the input, and have named input layers to match to the dict.

Here's how you do it:

model1 = tf.keras.Sequential(name="layer_1")
model2 = tf.keras.Sequential(name="layer_2")

model.summary()

ds1 = tf.data.Dataset.from_tensors(({"layer_1": [[1,2,3]],
                                     "layer_2": [[1,2,3]]}, [[2]]))

model.fit(ds1)

An easier option is to simply use tensors instead of datasets. .fit supports a list of tensors as input so just use that.

model = tf.keras.models.Model(inputs=[model1.input, model2.input], outputs=t)
model.compile(loss='mse')

model.summary()

a = tf.constant([[1, 2, 3]])
b = tf.constant([[1, 2, 3]])

c = tf.constant([[1]])

model.fit([a, b], c)

Answered By - hydra1