Wrap tensorflow function in keras layer

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i'm trying to use the tensorflow unique function (https://www.tensorflow.org/api_docs/python/tf/unique) in a keras lambda layer. Code below:

    def unique_idx(x):
        output = tf.unique(x)
        return output[1]

then 

    inp1 = Input(batch_shape(None, 1))
    idx = Lambda(unique_idx)(inp1)

    model = Model(inputs=inp1, outputs=idx)

when I now use **model.compile(optimizer='Adam', loss='mean_squared_error')** I get the error:

ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("lambda_9_sample_weights_1:0", shape=(?,), dtype=float32)'

Does anybody know whats the error here or a different way of using the tensorflow function?

keras python-3.x python
1 Answers

A keras model expects a float32 as output, but the indices returned from tf.unique is a int32. A casting fixes your problem.
Another issue is that unique expects a flatten array. reshape fixes this one.

import tensorflow as tf
from keras import Input
from keras.layers import Lambda
from keras.engine import Model


def unique_idx(x):
    x = tf.reshape(x, [-1])
    u, indices = tf.unique(x)
    return tf.cast(indices, tf.float32)


x = Input(shape=(1,))
y = Lambda(unique_idx)(x)

model = Model(inputs=x, outputs=y)
model.compile(optimizer='adam', loss='mse')
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