title: Model.evaluate in keras with multi inputs and multi outputs
body: <p>I m working on a deep multimodal autoencoder in the case of unsupervised learning which takes two inputs with shape of (1000, 50) and (1000,60) respectively to reconstruct the intial two inputs. The model has 3 hidden layers and aim to concatenate the two latent layer of input1 and input2.  Both outputs are then used to compute two losses(MSE).</p>
  <p>Please note that X and X1 were generated as the following in order to compute for each element the average value of its neighborhood :</p>
  <pre><code>matrix(1000,50) , matrix1(1000,60) and A is the adjacency matrix with the shape of (1000,1000)
  summed_groups_matrix = A@matrix
  summed_groups_matrix1 = A@matrix1
  neighborhood_sizes = A.sum(axis=1)
  X=summed_groups_matrix / neighborhood_sizes
  X1=summed_groups_matrix1 / neighborhood_sizes
  </code></pre>
  <p>The complete code of the multi-modal autoencoder is the following :</p>
  <pre><code>input_X = Input(shape=(X[0].shape))
  
  dense_X = Dense(40,activation='relu')(input_X)
  
  dense1_X = Dense(20,activation='relu')(dense_X)
  
  latent_X= Dense(2,activation='relu')(dense1_X)
  
  input_X1 = Input(shape=(X1[0].shape))
  
  dense_X1 = Dense(40,activation='relu')(input_X1)
  
  dense1_X1 = Dense(20,activation='relu')(dense_X1)
  
  latent_X1= Dense(2,activation='relu')(dense1_X1)
  
  Concat_X_X1 = concatenate([latent_X, latent_X1])
  
  decoding_X = Dense(20,activation='relu')(Concat_X_X1)
  
  decoding1_X = Dense(40,activation='relu')(decoding_X)
  
  output_X = Dense(X[0].shape[0],activation='sigmoid')(decoding1_X)
  
  decoding_X1 = Dense(20,activation='relu')(Concat_X_X1)
  
  decoding1_X1 = Dense(40,activation='relu')(decoding_X1)
  
  output_X1 = Dense(X1[0].shape[0],activation='sigmoid')(decoding1_X1)
  
  multi_modal_autoencoder = Model([input_X, input_X1], [output_X, output_X1], name='multi_modal_autoencoder')
  
  multi_modal_autoencoder.compile(optimizer=keras.optimizers.Adam(lr=0.001),loss='mse')
  
  model = multi_modal_autoencoder.fit([X,X1], [X, X1], epochs=70, batch_size=150)
  </code></pre>
  <p>While using multi_modal_autoencoder.evaluate(X,X1) it returns this error :</p>
  <pre><code>TypeError: 'method' object is not subscriptable
  </code></pre>
  <p><strong>What should i pass in the model.evaluate ?</strong></p>