Multi-output classification using Tensorflow












0















I have a multi-output problem (multi-label, multi-classification). In brief, the problem regards instrument recognition in polyphonic music, therefore my model needs to be able to predict the instruments (which can be multiple) in a song.



I need to use a CNN for this. The first block of the network model has been omitted since it is composed only by the feature extractor, with all the convolution, pooling and dropout. I am using the high level API of Tensorflow, Estimators.



    # first part omitted, this is last dropout from a fully connected layer
dropout = tf.layers.dropout(inputs=dense, rate=0.5, training=mode == tf.estimator.ModeKeys.TRAIN)

print('Shape Dropout', dropout.shape)

###########################################################################

# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=labels.shape[1])

print('Shape Logits:', logits.shape)

predictions = tf.round(tf.sigmoid(logits))

if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)


loss = tf.nn.weighted_cross_entropy_with_logits(targets=tf.cast(labels, tf.float32), logits=logits, pos_weight=3, name=None)

loss = tf.reduce_mean(tf.reduce_sum(loss, axis=1))

accuracy = tf.reduce_mean(tf.cast(tf.equal(predictions, tf.round(tf.cast(labels, tf.float32))), tf.float32))

# calculate f1_score, precision and recall for multilabel problem
f1s = [0, 0, 0]

labels = tf.cast(labels, tf.float64)
predictions = tf.cast(predictions, tf.float64)

for i, axis in enumerate([None, 0]):
TP = tf.count_nonzero(predictions * labels, axis=axis)
FP = tf.count_nonzero(predictions * (labels - 1), axis=axis)
FN = tf.count_nonzero((predictions - 1) * labels, axis=axis)

precision = tf.truediv(TP, (TP + FP))
recall = tf.truediv(TP, (TP + FN))
f1 = 2 * precision * recall / (precision + recall)

f1s[i] = tf.reduce_mean(f1)

weights = tf.reduce_sum(labels, axis=0)
weights /= tf.reduce_sum(weights)

f1s[2] = tf.reduce_sum(tf.cast(f1, tf.float64) * weights)

micro, macro, weighted = f1s


tf.summary.scalar("micro_mLabels", micro)
tf.summary.scalar("macro_mLabels", macro)
tf.summary.scalar("weighted_mLabels", weighted)


tf.summary.scalar("precision_sLabel", tf.metrics.precision(labels=labels, predictions=tf.cast(predictions, tf.int32)))
tf.summary.scalar("recall_sLabel",tf.metrics.recall(labels=labels, predictions=tf.cast(predictions, tf.int32)))


if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
logging_hook = tf.train.LoggingTensorHook({"loss": loss, "accuracy": accuracy}, every_n_iter=10)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks = [logging_hook])



eval_metric_ops = {
"accuracy_sLabel": tf.metrics.accuracy(
labels=labels,
predictions=predictions),
"precision_sLabel": tf.metrics.precision(
labels=labels,
predictions=tf.cast(predictions, tf.int32)),
"recall_sLabel": tf.metrics.recall(
labels=labels,
predictions=tf.cast(predictions, tf.int32)),
}

return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


I recalculate precision and recall internally to check whether was a Tensorflow bug.



Basically, the problem I am having is that the accuracy is really high from the beginning, like in 20 iterations is already at 90% (very weird, should take few epochs), precision and recall differently sometimes are 0 and sometimes not, depending from what hyperparameters I set.



I understand I need to follow a Sigmoid approach for multi-label but I think I am doing things in a wrong way.



What I believe is that the way in which I store the predictions from the logits and in how I calculate the loss is wrong. However, I am not quite sure about the rest of the code, that's why I added the full block.



Really appreciate some help,



Thank you in advance.










share|improve this question



























    0















    I have a multi-output problem (multi-label, multi-classification). In brief, the problem regards instrument recognition in polyphonic music, therefore my model needs to be able to predict the instruments (which can be multiple) in a song.



    I need to use a CNN for this. The first block of the network model has been omitted since it is composed only by the feature extractor, with all the convolution, pooling and dropout. I am using the high level API of Tensorflow, Estimators.



        # first part omitted, this is last dropout from a fully connected layer
    dropout = tf.layers.dropout(inputs=dense, rate=0.5, training=mode == tf.estimator.ModeKeys.TRAIN)

    print('Shape Dropout', dropout.shape)

    ###########################################################################

    # Logits Layer
    logits = tf.layers.dense(inputs=dropout, units=labels.shape[1])

    print('Shape Logits:', logits.shape)

    predictions = tf.round(tf.sigmoid(logits))

    if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)


    loss = tf.nn.weighted_cross_entropy_with_logits(targets=tf.cast(labels, tf.float32), logits=logits, pos_weight=3, name=None)

    loss = tf.reduce_mean(tf.reduce_sum(loss, axis=1))

    accuracy = tf.reduce_mean(tf.cast(tf.equal(predictions, tf.round(tf.cast(labels, tf.float32))), tf.float32))

    # calculate f1_score, precision and recall for multilabel problem
    f1s = [0, 0, 0]

    labels = tf.cast(labels, tf.float64)
    predictions = tf.cast(predictions, tf.float64)

    for i, axis in enumerate([None, 0]):
    TP = tf.count_nonzero(predictions * labels, axis=axis)
    FP = tf.count_nonzero(predictions * (labels - 1), axis=axis)
    FN = tf.count_nonzero((predictions - 1) * labels, axis=axis)

    precision = tf.truediv(TP, (TP + FP))
    recall = tf.truediv(TP, (TP + FN))
    f1 = 2 * precision * recall / (precision + recall)

    f1s[i] = tf.reduce_mean(f1)

    weights = tf.reduce_sum(labels, axis=0)
    weights /= tf.reduce_sum(weights)

    f1s[2] = tf.reduce_sum(tf.cast(f1, tf.float64) * weights)

    micro, macro, weighted = f1s


    tf.summary.scalar("micro_mLabels", micro)
    tf.summary.scalar("macro_mLabels", macro)
    tf.summary.scalar("weighted_mLabels", weighted)


    tf.summary.scalar("precision_sLabel", tf.metrics.precision(labels=labels, predictions=tf.cast(predictions, tf.int32)))
    tf.summary.scalar("recall_sLabel",tf.metrics.recall(labels=labels, predictions=tf.cast(predictions, tf.int32)))


    if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(
    loss=loss,
    global_step=tf.train.get_global_step())
    logging_hook = tf.train.LoggingTensorHook({"loss": loss, "accuracy": accuracy}, every_n_iter=10)
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks = [logging_hook])



    eval_metric_ops = {
    "accuracy_sLabel": tf.metrics.accuracy(
    labels=labels,
    predictions=predictions),
    "precision_sLabel": tf.metrics.precision(
    labels=labels,
    predictions=tf.cast(predictions, tf.int32)),
    "recall_sLabel": tf.metrics.recall(
    labels=labels,
    predictions=tf.cast(predictions, tf.int32)),
    }

    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


    I recalculate precision and recall internally to check whether was a Tensorflow bug.



    Basically, the problem I am having is that the accuracy is really high from the beginning, like in 20 iterations is already at 90% (very weird, should take few epochs), precision and recall differently sometimes are 0 and sometimes not, depending from what hyperparameters I set.



    I understand I need to follow a Sigmoid approach for multi-label but I think I am doing things in a wrong way.



    What I believe is that the way in which I store the predictions from the logits and in how I calculate the loss is wrong. However, I am not quite sure about the rest of the code, that's why I added the full block.



    Really appreciate some help,



    Thank you in advance.










    share|improve this question

























      0












      0








      0








      I have a multi-output problem (multi-label, multi-classification). In brief, the problem regards instrument recognition in polyphonic music, therefore my model needs to be able to predict the instruments (which can be multiple) in a song.



      I need to use a CNN for this. The first block of the network model has been omitted since it is composed only by the feature extractor, with all the convolution, pooling and dropout. I am using the high level API of Tensorflow, Estimators.



          # first part omitted, this is last dropout from a fully connected layer
      dropout = tf.layers.dropout(inputs=dense, rate=0.5, training=mode == tf.estimator.ModeKeys.TRAIN)

      print('Shape Dropout', dropout.shape)

      ###########################################################################

      # Logits Layer
      logits = tf.layers.dense(inputs=dropout, units=labels.shape[1])

      print('Shape Logits:', logits.shape)

      predictions = tf.round(tf.sigmoid(logits))

      if mode == tf.estimator.ModeKeys.PREDICT:
      return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)


      loss = tf.nn.weighted_cross_entropy_with_logits(targets=tf.cast(labels, tf.float32), logits=logits, pos_weight=3, name=None)

      loss = tf.reduce_mean(tf.reduce_sum(loss, axis=1))

      accuracy = tf.reduce_mean(tf.cast(tf.equal(predictions, tf.round(tf.cast(labels, tf.float32))), tf.float32))

      # calculate f1_score, precision and recall for multilabel problem
      f1s = [0, 0, 0]

      labels = tf.cast(labels, tf.float64)
      predictions = tf.cast(predictions, tf.float64)

      for i, axis in enumerate([None, 0]):
      TP = tf.count_nonzero(predictions * labels, axis=axis)
      FP = tf.count_nonzero(predictions * (labels - 1), axis=axis)
      FN = tf.count_nonzero((predictions - 1) * labels, axis=axis)

      precision = tf.truediv(TP, (TP + FP))
      recall = tf.truediv(TP, (TP + FN))
      f1 = 2 * precision * recall / (precision + recall)

      f1s[i] = tf.reduce_mean(f1)

      weights = tf.reduce_sum(labels, axis=0)
      weights /= tf.reduce_sum(weights)

      f1s[2] = tf.reduce_sum(tf.cast(f1, tf.float64) * weights)

      micro, macro, weighted = f1s


      tf.summary.scalar("micro_mLabels", micro)
      tf.summary.scalar("macro_mLabels", macro)
      tf.summary.scalar("weighted_mLabels", weighted)


      tf.summary.scalar("precision_sLabel", tf.metrics.precision(labels=labels, predictions=tf.cast(predictions, tf.int32)))
      tf.summary.scalar("recall_sLabel",tf.metrics.recall(labels=labels, predictions=tf.cast(predictions, tf.int32)))


      if mode == tf.estimator.ModeKeys.TRAIN:
      optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
      train_op = optimizer.minimize(
      loss=loss,
      global_step=tf.train.get_global_step())
      logging_hook = tf.train.LoggingTensorHook({"loss": loss, "accuracy": accuracy}, every_n_iter=10)
      return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks = [logging_hook])



      eval_metric_ops = {
      "accuracy_sLabel": tf.metrics.accuracy(
      labels=labels,
      predictions=predictions),
      "precision_sLabel": tf.metrics.precision(
      labels=labels,
      predictions=tf.cast(predictions, tf.int32)),
      "recall_sLabel": tf.metrics.recall(
      labels=labels,
      predictions=tf.cast(predictions, tf.int32)),
      }

      return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


      I recalculate precision and recall internally to check whether was a Tensorflow bug.



      Basically, the problem I am having is that the accuracy is really high from the beginning, like in 20 iterations is already at 90% (very weird, should take few epochs), precision and recall differently sometimes are 0 and sometimes not, depending from what hyperparameters I set.



      I understand I need to follow a Sigmoid approach for multi-label but I think I am doing things in a wrong way.



      What I believe is that the way in which I store the predictions from the logits and in how I calculate the loss is wrong. However, I am not quite sure about the rest of the code, that's why I added the full block.



      Really appreciate some help,



      Thank you in advance.










      share|improve this question














      I have a multi-output problem (multi-label, multi-classification). In brief, the problem regards instrument recognition in polyphonic music, therefore my model needs to be able to predict the instruments (which can be multiple) in a song.



      I need to use a CNN for this. The first block of the network model has been omitted since it is composed only by the feature extractor, with all the convolution, pooling and dropout. I am using the high level API of Tensorflow, Estimators.



          # first part omitted, this is last dropout from a fully connected layer
      dropout = tf.layers.dropout(inputs=dense, rate=0.5, training=mode == tf.estimator.ModeKeys.TRAIN)

      print('Shape Dropout', dropout.shape)

      ###########################################################################

      # Logits Layer
      logits = tf.layers.dense(inputs=dropout, units=labels.shape[1])

      print('Shape Logits:', logits.shape)

      predictions = tf.round(tf.sigmoid(logits))

      if mode == tf.estimator.ModeKeys.PREDICT:
      return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)


      loss = tf.nn.weighted_cross_entropy_with_logits(targets=tf.cast(labels, tf.float32), logits=logits, pos_weight=3, name=None)

      loss = tf.reduce_mean(tf.reduce_sum(loss, axis=1))

      accuracy = tf.reduce_mean(tf.cast(tf.equal(predictions, tf.round(tf.cast(labels, tf.float32))), tf.float32))

      # calculate f1_score, precision and recall for multilabel problem
      f1s = [0, 0, 0]

      labels = tf.cast(labels, tf.float64)
      predictions = tf.cast(predictions, tf.float64)

      for i, axis in enumerate([None, 0]):
      TP = tf.count_nonzero(predictions * labels, axis=axis)
      FP = tf.count_nonzero(predictions * (labels - 1), axis=axis)
      FN = tf.count_nonzero((predictions - 1) * labels, axis=axis)

      precision = tf.truediv(TP, (TP + FP))
      recall = tf.truediv(TP, (TP + FN))
      f1 = 2 * precision * recall / (precision + recall)

      f1s[i] = tf.reduce_mean(f1)

      weights = tf.reduce_sum(labels, axis=0)
      weights /= tf.reduce_sum(weights)

      f1s[2] = tf.reduce_sum(tf.cast(f1, tf.float64) * weights)

      micro, macro, weighted = f1s


      tf.summary.scalar("micro_mLabels", micro)
      tf.summary.scalar("macro_mLabels", macro)
      tf.summary.scalar("weighted_mLabels", weighted)


      tf.summary.scalar("precision_sLabel", tf.metrics.precision(labels=labels, predictions=tf.cast(predictions, tf.int32)))
      tf.summary.scalar("recall_sLabel",tf.metrics.recall(labels=labels, predictions=tf.cast(predictions, tf.int32)))


      if mode == tf.estimator.ModeKeys.TRAIN:
      optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
      train_op = optimizer.minimize(
      loss=loss,
      global_step=tf.train.get_global_step())
      logging_hook = tf.train.LoggingTensorHook({"loss": loss, "accuracy": accuracy}, every_n_iter=10)
      return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks = [logging_hook])



      eval_metric_ops = {
      "accuracy_sLabel": tf.metrics.accuracy(
      labels=labels,
      predictions=predictions),
      "precision_sLabel": tf.metrics.precision(
      labels=labels,
      predictions=tf.cast(predictions, tf.int32)),
      "recall_sLabel": tf.metrics.recall(
      labels=labels,
      predictions=tf.cast(predictions, tf.int32)),
      }

      return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


      I recalculate precision and recall internally to check whether was a Tensorflow bug.



      Basically, the problem I am having is that the accuracy is really high from the beginning, like in 20 iterations is already at 90% (very weird, should take few epochs), precision and recall differently sometimes are 0 and sometimes not, depending from what hyperparameters I set.



      I understand I need to follow a Sigmoid approach for multi-label but I think I am doing things in a wrong way.



      What I believe is that the way in which I store the predictions from the logits and in how I calculate the loss is wrong. However, I am not quite sure about the rest of the code, that's why I added the full block.



      Really appreciate some help,



      Thank you in advance.







      python tensorflow






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 13 '18 at 19:17









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