title: Keras InvalidArgumentError: Incompatible shapes: [1,8,32] vs. [1,10,32]
body: <p>It stops training at the beginning of the first epoch. </p>
  
  <p>The error is: </p>
  
  <pre><code>InvalidArgumentError: Incompatible shapes: [1,8,32] vs. [1,10,32]
       [[Node: training_8/RMSprop/gradients/loss_8/time_distributed_9_loss/mul_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@train.../Reshape_1"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](training_8/RMSprop/gradients/loss_8/time_distributed_9_loss/mul_grad/Shape, training_8/RMSprop/gradients/loss_8/time_distributed_9_loss/mul_grad/Shape_1)]]
  </code></pre>
  
  <p>I'm not sure if the error might be the format of the training data.
  This is a sample of train_x:</p>
  
  <pre><code>[array([ 0,  1,  2,  3,  4,  5,  6,  3,  7,  8,  9, 10, 11, 12, 13, 14], dtype=int32), array([15, 16, 17, 18, 19], dtype=int32), array([20, 16, 17, 18, 21, 22, 23, 24, 25, 26], dtype=int32), array([27,  1, 17, 28, 18, 29, 30, 31, 24, 32], dtype=int32), array([33,  1, 17,  3, 34, 35, 36, 37, 18, 38], dtype=int32), array([39, 16, 40, 28, 41, 42], dtype=int32), array([39,  1, 40, 28, 41, 43], dtype=int32), array([44,  6,  3, 45], dtype=int32), array([15, 16, 40, 46, 47, 48,  3, 49, 50, 51, 52, 53], dtype=int32), array([ 0, 54, 28, 55, 56, 57, 58, 59], dtype=int32)]
  </code></pre>
  
  <p>This is a sample of train_label</p>
  
  <pre><code>[array([0, 1, 2, 1, 2, 3, 0, 1, 4, 2, 2, 4, 5, 6, 0, 7], dtype=int32), array([0, 5, 8, 9, 9], dtype=int32), array([10,  5,  8,  9,  7,  9,  7,  7], dtype=int32), array([10,  1,  8,  1,  9,  9,  3,  0,  7,  2,  7], dtype=int32), array([10,  1,  8,  1,  9,  9,  9,  7,  9,  7], dtype=int32), array([0, 5, 8, 1, 2, 2], dtype=int32), array([0, 1, 8, 1, 2, 2], dtype=int32), array([11,  0,  1,  9], dtype=int32), array([ 0,  5,  8,  7, 12, 13,  1,  2,  9,  9,  5,  6], dtype=int32), array([ 0, 14,  1,  2,  2, 12,  4, 15], dtype=int32)]
  </code></pre>
  
  <p>This is the code:</p>
  
  <pre><code>n_vocab = len(unique_words) #1517
  model = Sequential()
  model.add(Embedding(n_vocab,100))
  model.add(Convolution1D(128, 5, border_mode='same', activation='relu'))
  model.add(Dropout(0.25))
  model.add(GRU(100,return_sequences=True))
  model.add(TimeDistributed(Dense(n_classes, activation='softmax')))
  model.compile('rmsprop', 'categorical_crossentropy')
  
  n_epochs = 30
  train_x = [np.asarray(x, dtype=np.int32) for x in encoded_sentences]
  train_label = [np.asarray(x, dtype=np.int32) for x in encoded_POS]
  
  for i in range(n_epochs):
      print("Training epoch {}".format(i))
  
      bar = progressbar.ProgressBar(maxval=len(train_x))
      for n_batch, sent in bar(enumerate(train_x)):
          label = train_label[n_batch]
          # Make labels one hot
          label = np.eye(n_classes)[label][np.newaxis,:]
          # View each sentence as a batch
          sent = sent[np.newaxis,:]
  
          if sent.shape[1] &gt; 1: #ignore 1 word sentences
              model.train_on_batch(sent, label)
  </code></pre>