title: Keras class_weight in multi-label binary classification
body: <p>Having trouble using class_weight for my multi-label problem. That is, each label is either 0 or 1, but there are many labels for each input sample.</p>
  
  <p>The code (with random data for MWE purposes):</p>
  
  <pre><code>import tensorflow as tf
  from keras.models import Sequential, Model
  from keras.layers import Input, Concatenate, LSTM, Dense
  from keras import optimizers
  from keras.utils import to_categorical
  from keras import backend as K
  import numpy as np
  
  # from http://www.deepideas.net/unbalanced-classes-machine-learning/
  def sensitivity(y_true, y_pred):
          true_positives = tf.reduce_sum(tf.round(K.clip(y_true * y_pred, 0, 1)))
          possible_positives = tf.reduce_sum(tf.round(K.clip(y_true, 0, 1)))
          return true_positives / (possible_positives + K.epsilon())
  
  # from http://www.deepideas.net/unbalanced-classes-machine-learning/    
  def specificity(y_true, y_pred):
          true_negatives = tf.reduce_sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
          possible_negatives = tf.reduce_sum(K.round(K.clip(1-y_true, 0, 1)))
          return true_negatives / (possible_negatives + K.epsilon())
  
  def to_train(a_train, y_train):
          hours_np = [np.arange(a_train.shape[1])]*a_train.shape[0]
          train_hours = to_categorical(hours_np)
          n_samples = a_train.shape[0]
          n_classes = 4
          features_in = np.zeros((n_samples, n_classes))
          supp_feat = np.random.choice(n_classes, n_samples)
          features_in[np.arange(n_samples), supp_feat] = 1
  
          #This model has 3 separate inputs
          seq_model_in = Input(shape=(1,),batch_shape=(1, 1, a_train.shape[2]), name='seq_model_in')
          feat_in = Input(shape=(1,), batch_shape=(1, features_in.shape[1]), name='feat_in')
          feat_dense = Dense(1)(feat_in)
          hours_in = Input(shape=(1,), batch_shape=(1, 1, train_hours.shape[2]), name='hours_in')
  
          #Model intermediate layers
          t_concat = Concatenate(axis=-1)([seq_model_in, hours_in])
          lstm_layer = LSTM(1, batch_input_shape=(1, 1, (a_train.shape[2]+train_hours.shape[2])), return_sequences=False, stateful=True)(t_concat)
          merged_after_lstm = Concatenate(axis=-1)([lstm_layer, feat_dense]) #may need another Dense() after
          dense_merged = Dense(a_train.shape[2], activation="sigmoid")(merged_after_lstm)
  
          #Define input and output to create model, and compile
          model = Model(inputs=[seq_model_in, feat_in, hours_in], outputs=dense_merged)
          model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[sensitivity, specificity])
  
          class_weights = {0.:1., 1.:118.}
          seq_length = 23
  
          #TRAINING (based on http://philipperemy.github.io/keras-stateful-lstm/)
          for epoch in range(2):
              for i in range(a_train.shape[0]):
                      y_true_1 = np.expand_dims(y_train[i,:], axis=1)
                      y_true = np.swapaxes(y_true_1, 0, 1)
                      #print 'y_true', y_true.shape
                      for j in range(seq_length-1):
                              input_1 = np.expand_dims(np.expand_dims(a_train[i][j], axis=1), axis=1)
                              input_1 = np.reshape(input_1, (1, 1, a_train.shape[2]))
                              input_2 = np.expand_dims(np.array(features_in[i]), axis=1)
                              input_2 = np.swapaxes(input_2, 0, 1)
                              input_3 = np.expand_dims(np.array([train_hours[i][j]]), axis=1)
                              tr_loss, tr_sens, tr_spec = model.train_on_batch([input_1, input_2, input_3], y_true, class_weight=class_weights)
                      model.reset_states()
         return 0
  
  a_train = np.random.normal(size=(50,24,5625))
  y_train = a_train[:, -1, :]
  a_train = a_train[:, :-1, :]
  y_train[y_train &gt; 0.] = 1.
  y_train[y_train &lt; 0.] = 0.
  to_train(a_train, y_train)
  </code></pre>
  
  <p>The error that I'm getting is:</p>
  
  <pre><code>ValueError: `class_weight` must contain all classes in the data. The classes set([330]) exist in the data but not in `class_weight`.
  </code></pre>
  
  <p>The value inside 'set([...])' changes at each run. But as I said, the only two classes in the data are 0 and 1; there are just multiple labels per sample. So for example, one response (y_train) looks like this:</p>
  
  <pre><code>print y_train[0,:]
  #[ 0.  0.  1. ...,  0.  1.  0.]
  </code></pre>
  
  <p>How can I use <code>class_weights</code> for a multi-label problem in Keras?</p>