title: How apply Gridsearch on autoencoder model?
body: <p>I want to apply GridSearchCV on the autoencoder model. The code of the atuoencoder and GridSearchCV  is added below please tell me how I change this code to run  GridSearchCV successfully.</p>
  
  <pre><code>autoencoder = Sequential()
  # Encoder Layers
  autoencoder.add(Conv2D(16, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
  autoencoder.add(MaxPooling2D((2, 2), padding='same'))
  autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
  autoencoder.add(MaxPooling2D((2, 2), padding='same'))
  autoencoder.add(Conv2D(8, (3, 3), strides=(2,2), activation='relu', padding='same'))
  # Flatten encoding for visualization
  autoencoder.add(Flatten())
  autoencoder.add(Reshape((4, 4, 8)))
  # Decoder Layers
  autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
  autoencoder.add(UpSampling2D((2, 2)))
  autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
  autoencoder.add(UpSampling2D((2, 2)))
  autoencoder.add(Conv2D(16, (3, 3), activation='relu'))
  autoencoder.add(UpSampling2D((2, 2)))
  autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))
  
  autoencoder.summary()
  </code></pre>
  
  <p><strong>I want to apply GridSearch on the above autoencoder code</strong></p>
  
  <pre><code>from sklearn.model_selection import GridSearchCV
  from keras.wrappers.scikit_learn import KerasClassifier
  
  model_classifier = KerasClassifier(autoencoder, verbose=1, batch_size=10, epochs=10)
  # define the grid search parameters
  batch_size = [10]
  loss = ['mean_squared_error', 'binary_crossentropy']
  optimizer = [Adam, SGD, RMSprop]
  learning_rate = [0.001]
  epochs = [3, 5]
  param_grid = dict(optimizer=optimizer, learning_rate=learning_rate)
  grid = GridSearchCV(cv=[(slice(None), slice(None))], estimator=model_classifier, param_grid=param_grid, n_jobs=1)
  grid_result = grid.fit(x_train, x_train)
  print("training Successfully completed")
  </code></pre>