Map function to partial tensor in TensorFlow












0















In the TensorFlow architecture, how do you apply a function to only some elements in a tensor? For example, on the final output of a layer, some variables represent pixel densities which I would like to run through a sigmoid, and a few variables at the beginning of the tensor represent unbounded continuous values, which I would not like to apply an activation function at all.



Here is what I am trying:



    output_activation=tf.nn.sigmoid

    x = tf.layers.dense(z, hidden_size, activation=hidden_activation)

    x = tf.layers.dense(x, hidden_size, activation=hidden_activation)

    _logits = tf.layers.dense(x, output_size, activation=None)

    # Apply sigmoid only to image variables

    logits = tf.concat(_logits[:functional_inputs], tf.map_fn(output_activation, _logits[functional_inputs:]), 0)


Which is giving the following value error:



ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("build_decoder/map/TensorArrayStack/TensorArrayGatherV3:0", shape=(?, 288755), dtype=float32)'


Is this the best way to do this?






python tensorflow







share|improve this question























  • I think the problem is in output_activation, which seems to return a int32 tensor for the float32 input. From the documentation of tf.map_fn: "Users must provide dtype if it is different from the data type of elems". So try adding dtype=tf.int32 to tf.map_fn.

    – jdehesa
    Nov 29 '18 at 10:32













  • I will give that a shot, thanks

    – taylormade201
    Nov 29 '18 at 23:02
















0















In the TensorFlow architecture, how do you apply a function to only some elements in a tensor? For example, on the final output of a layer, some variables represent pixel densities which I would like to run through a sigmoid, and a few variables at the beginning of the tensor represent unbounded continuous values, which I would not like to apply an activation function at all.



Here is what I am trying:



    output_activation=tf.nn.sigmoid

    x = tf.layers.dense(z, hidden_size, activation=hidden_activation)

    x = tf.layers.dense(x, hidden_size, activation=hidden_activation)

    _logits = tf.layers.dense(x, output_size, activation=None)

    # Apply sigmoid only to image variables

    logits = tf.concat(_logits[:functional_inputs], tf.map_fn(output_activation, _logits[functional_inputs:]), 0)


Which is giving the following value error:



ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("build_decoder/map/TensorArrayStack/TensorArrayGatherV3:0", shape=(?, 288755), dtype=float32)'


Is this the best way to do this?






python tensorflow







share|improve this question























  • I think the problem is in output_activation, which seems to return a int32 tensor for the float32 input. From the documentation of tf.map_fn: "Users must provide dtype if it is different from the data type of elems". So try adding dtype=tf.int32 to tf.map_fn.

    – jdehesa
    Nov 29 '18 at 10:32













  • I will give that a shot, thanks

    – taylormade201
    Nov 29 '18 at 23:02














0












0








0








In the TensorFlow architecture, how do you apply a function to only some elements in a tensor? For example, on the final output of a layer, some variables represent pixel densities which I would like to run through a sigmoid, and a few variables at the beginning of the tensor represent unbounded continuous values, which I would not like to apply an activation function at all.



Here is what I am trying:



    output_activation=tf.nn.sigmoid

    x = tf.layers.dense(z, hidden_size, activation=hidden_activation)

    x = tf.layers.dense(x, hidden_size, activation=hidden_activation)

    _logits = tf.layers.dense(x, output_size, activation=None)

    # Apply sigmoid only to image variables

    logits = tf.concat(_logits[:functional_inputs], tf.map_fn(output_activation, _logits[functional_inputs:]), 0)


Which is giving the following value error:



ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("build_decoder/map/TensorArrayStack/TensorArrayGatherV3:0", shape=(?, 288755), dtype=float32)'


Is this the best way to do this?






python tensorflow







share|improve this question














In the TensorFlow architecture, how do you apply a function to only some elements in a tensor? For example, on the final output of a layer, some variables represent pixel densities which I would like to run through a sigmoid, and a few variables at the beginning of the tensor represent unbounded continuous values, which I would not like to apply an activation function at all.



Here is what I am trying:



    output_activation=tf.nn.sigmoid

    x = tf.layers.dense(z, hidden_size, activation=hidden_activation)

    x = tf.layers.dense(x, hidden_size, activation=hidden_activation)

    _logits = tf.layers.dense(x, output_size, activation=None)

    # Apply sigmoid only to image variables

    logits = tf.concat(_logits[:functional_inputs], tf.map_fn(output_activation, _logits[functional_inputs:]), 0)


Which is giving the following value error:



ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("build_decoder/map/TensorArrayStack/TensorArrayGatherV3:0", shape=(?, 288755), dtype=float32)'


Is this the best way to do this?






python tensorflow




python tensorflow






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 28 '18 at 22:18









taylormade201taylormade201

2711620




2711620













  • I think the problem is in output_activation, which seems to return a int32 tensor for the float32 input. From the documentation of tf.map_fn: "Users must provide dtype if it is different from the data type of elems". So try adding dtype=tf.int32 to tf.map_fn.

    – jdehesa
    Nov 29 '18 at 10:32













  • I will give that a shot, thanks

    – taylormade201
    Nov 29 '18 at 23:02



















  • I think the problem is in output_activation, which seems to return a int32 tensor for the float32 input. From the documentation of tf.map_fn: "Users must provide dtype if it is different from the data type of elems". So try adding dtype=tf.int32 to tf.map_fn.

    – jdehesa
    Nov 29 '18 at 10:32













  • I will give that a shot, thanks

    – taylormade201
    Nov 29 '18 at 23:02

















I think the problem is in output_activation, which seems to return a int32 tensor for the float32 input. From the documentation of tf.map_fn: "Users must provide dtype if it is different from the data type of elems". So try adding dtype=tf.int32 to tf.map_fn.

– jdehesa
Nov 29 '18 at 10:32







I think the problem is in output_activation, which seems to return a int32 tensor for the float32 input. From the documentation of tf.map_fn: "Users must provide dtype if it is different from the data type of elems". So try adding dtype=tf.int32 to tf.map_fn.

– jdehesa
Nov 29 '18 at 10:32















I will give that a shot, thanks

– taylormade201
Nov 29 '18 at 23:02





I will give that a shot, thanks

– taylormade201
Nov 29 '18 at 23:02












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