title: Tensorflow estimator ValueError: logits and labels must have the same shape ((?, 1) vs (?,))
body: <p>I'm classifying movie reviews as positive or negative using binary crossentropy. So, when I'm trying to wrap my keras model with tensorflow estimator, I get the error:</p>
  <pre><code>Tensorflow estimator ValueError: logits and labels must have the same shape ((?, 1) vs (?,))
  </code></pre>
  <p>I'm using sigmoid activation as my last layer, guess I'm missing something trivial here. Any help?</p>
  <pre><code>from tensorflow import keras
  import tensorflow as tf
  print(&quot;Tensorflow {} loaded&quot;.format(tf.__version__))
  import numpy as np
  
  keras.__version__
  from keras.datasets import imdb
  
  (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
  def vectorize_sequences(sequences, dimension=10000):
      # Create an all-zero matrix of shape (len(sequences), dimension)
      results = np.zeros((len(sequences), dimension))
      for i, sequence in enumerate(sequences):
          results[i, sequence] = 1.  # set specific indices of results[i] to 1s
      return results.astype('float32')
  
  # Our vectorized training data
  x_train = vectorize_sequences(train_data)
  
  # Our vectorized test data
  x_test = vectorize_sequences(test_data)
  
  # Our vectorized labels
  y_train = np.asarray(train_labels).astype('float32')
  y_test = np.asarray(test_labels).astype('float32')
  
  x_val = x_train[:10000]
  partial_x_train = x_train[10000:]
  y_val = y_train[:10000]
  partial_y_train = y_train[10000:]
  
  model = keras.models.Sequential()
  model.add(keras.layers.Dense(16, activation='relu', input_shape=(10000,), name='reviews'))
  model.add(keras.layers.Dense(16, activation='relu'))
  model.add(keras.layers.Dense(1, activation='sigmoid'))
  model.compile(optimizer='rmsprop',
                loss='binary_crossentropy',
                metrics=['accuracy'])
  estimator_model = keras.estimator.model_to_estimator(keras_model=model)
  
  def input_function(features,labels=None,shuffle=False,epochs=None,batch_size=None):
      input_fn = tf.estimator.inputs.numpy_input_fn(
          x={&quot;reviews_input&quot;: features},
          y=labels,
          shuffle=shuffle,
          num_epochs=epochs,
          batch_size=batch_size
      )
      return input_fn
  
  estimator_model.train(input_fn=input_function(partial_x_train, partial_y_train, True,20,512))
  score = estimator_model.evaluate(input_function(x_val, labels=y_val))
  print(score)
  </code></pre>