title: VGG16 different epoch and batch size generating the same result
body: <p>I am trying to learn how vgg16 works. Below is my code, using vgg16 for another classification.</p>
  
  <pre><code># Generate a model with all layers (with top)
  model_vgg16_conv = VGG16(weights='imagenet', include_top=False)
  model_vgg16_conv.summary()
  
  # create your own input format
  input = Input(shape=(128,128,3),name = 'image_input')
  
  # Use the generated model
  output_vgg16_conv = model_vgg16_conv(input)
  
  # Add the fully-connected layers
  x = Flatten(name='flatten')(output_vgg16_conv)
  x = Dense(4096, activation='relu', name='fc1')(x)
  x = Dense(4096, activation='relu', name='fc2')(x)
  x = Dense(5, activation='softmax', name='predictions')(x)
  
  #Create your own model 
  model = Model(input=input, output=x)
  
  #In the summary, weights and layers from VGG part will be hidden, but they will be fit during the training
  model.summary()
  
  # Specify an optimizer to use
  adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
  
  # Choose loss function, optimization method, and metrics (which results to display)
  model.compile(
      optimizer = adam,
      loss='categorical_crossentropy',
      metrics=['accuracy']
  )
  
  model.fit(X_train,y_train,epochs=10,batch_size=10,verbose=2)
  # model.fit(X_train,y_train,epochs=30,batch_size=100,verbose=2)
  result = model.predict(y_test) # same result
  </code></pre>
  
  <p>For some reason, using different epoch size and batch size generate exactly the same result. Am I doing something wrong?</p>