title: Keras' model evaluate expecting training set dimensions instead of test set dimensions
body: <p>I currently have a simple LSTM model implemented in Keras, with a training set x_train of dimensions (1,18227, 98) and a test set x_test of dimensions (1,3217, 98) timesteps/features, respectively. Currently the model is training without hitch, but when I attempt to evaluate using my test set, I receive this error:</p>
  
  <pre><code>    File "keras_LSTM.py", line 170, in &lt;module&gt;
          loss, f1, precision = model.evaluate(x_test, y_test, 
      batch_size=batch_size)
        File 
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site- 
     packages/keras/engine/training.py", line 1102, in evaluate
          batch_size=batch_size)
        File 
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 751, in _standardize_user_data
          exception_prefix='input')
        File 
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site- 
     packages/keras/engine/training_utils.py", line 138, in 
      standardize_input_data
          str(data_shape))
      ValueError: Error when checking input: expected lstm_1_input to have 
      shape (18227, 98) but got array with shape (3217, 98)
  </code></pre>
  
  <p>Any help would be greatly appreciated - will provide code if needed. It should also be noted that my input shapes are 3 dimensional - however the error report omits the batch_size dimension and output a tuple of (sequence_length, feature_number).</p>