title: AttributeError: 'History' object has no attribute 'predict_classes'
body: <p>I'm trying to to use keras to create a classifier but I can't generate some class predictions from my test set for some reason.
  To do so I'm using the following model.</p>
  
  <pre><code>def get_model():          #takes ch1, ch2, y_train
  
  nclass = 6
  #Channel 1
  ch1_input = Input(shape=X_train_ch1[0].shape)       #(3000,1)
  signal_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(ch1_input)
  signal_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(signal_1)
  signal_1 = MaxPool1D(pool_size=2)(signal_1)
  signal_1 = SpatialDropout1D(rate=0.1)(signal_1)
  signal_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(signal_1)
  signal_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(signal_1)
  signal_1 = MaxPool1D(pool_size=2)(signal_1)
  signal_1 = SpatialDropout1D(rate=0.)(signal_1)
  signal_1 = Convolution1D(64, kernel_size=3, activation=activations.relu, padding="valid")(signal_1)
  signal_1 = Convolution1D(64, kernel_size=3, activation=activations.relu, padding="valid")(signal_1)
  flatten_1 = Flatten()(signal_1)
  
  #Channel 2
  ch2_input = Input(shape=X_train_ch2[0].shape)
  signal_2 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(ch2_input)
  signal_2 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(signal_2)
  signal_2 = MaxPool1D(pool_size=2)(signal_2)
  signal_2 = SpatialDropout1D(rate=0.1)(signal_2)
  signal_2 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(signal_2)
  signal_2 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(signal_2)
  signal_2 = MaxPool1D(pool_size=2)(signal_2)
  signal_2 = SpatialDropout1D(rate=0.2)(signal_2)
  signal_2 = Convolution1D(64, kernel_size=3, activation=activations.relu, padding="valid")(signal_2)
  signal_2 = Convolution1D(64, kernel_size=3, activation=activations.relu, padding="valid")(signal_2)
  flatten_2 = Flatten()(signal_2)
  
  # merge CNN's being trained on each channel 
  merged = concatenate([flatten_1, flatten_2])
  
  # Output
  dense_1 = Dropout(rate=0.15)(Dense(64, activation=activations.relu, name="dense_1")(merged))
  #dense_1 = Dense(, activation=activations.relu)(dense_1)
  dense_1 = Dropout(rate=0.25)(Dense(32, activation=activations.relu, name="dense_2")(dense_1))
  dense_1 = Dense(nclass, activation=activations.softmax, name="dense_3")(dense_1)
  
  # Compile model 
  model = Model(inputs=[ch1_input, ch2_input], outputs=dense_1)
  model.compile(loss='categorical_crossentropy',
            optimizer='adam',
            metrics=['accuracy'])
  model.summary()
  print(model.summary)
  return model
  
  #  --------------------------- Create train data and model
  sequence = Standardise_and_Augment(sequence)  
  X_train_ch1, X_train_ch2, X_test_ch1, X_test_ch2, X_test, y_train, y_test = Process_data()  
  y_flat = np.argmax(y_train, axis=1) 
  model = get_model()
  
  #  ---------------------------  Run model 
  ch_model = model.fit([X_train_ch1,X_train_ch2], y_train, epochs=20, batch_size=32, 
                   validation_split=0.2 ,class_weight='auto', shuffle = True)
  
  #  ---------------------------  Get get class breakdown
  from sklearn.metrics import classification_report
  
  Y_test = np.argmax(y_test, axis=1) # Convert one-hot to index
  y_pred = ch_model.predict_classes(X_test)
  print(classification_report(Y_test, y_pred))
  </code></pre>
  
  <p>Running this gives AttributeError: 'History' object has no attribute 'predict_classes'
  I know my model history is being stored as I can generate graphs of my models performance by running:</p>
  
  <pre><code># Plot model accuracy 
  plt.subplot(2, 1, 1)
  plt.plot(ch_model.history['accuracy'])
  plt.plot(ch_model.history['val_accuracy'])
  plt.title('Model accuracy')
  plt.ylabel('Accuracy')
  plt.xlabel('Epoch')
  plt.show()
  </code></pre>
  
  <p>All solutions to this error that I've seen so far have been about using sequential model which I'm sure I'm using. 
  If anyone could let me know where I've gone wrong or any alternative ways of generating y_pred, I would appreciate it.</p>