title: Validation and training accuracy high in the first epoch [Keras]
body: <p>I am training an image classifier with 2 classes and 53k images, and validating it with 1.3k images using keras. Here is the structure of the neural network : </p>
  
  <pre><code>model = Sequential()
  model.add(Flatten(input_shape=train_data.shape[1:]))
  model.add(Dense(256, activation='relu'))
  model.add(Dropout(0.5))
  model.add(Dense(256, activation='relu'))
  model.add(Dropout(0.5))
  model.add(Dense(1, activation='sigmoid'))
  model.compile(optimizer='adam',
                loss='binary_crossentropy', metrics=['accuracy'])
  </code></pre>
  
  <p>Training accuracy increases from ~50% to ~85% in the first epoch, with 85% validation accuracy. Subsequent epochs increase the training accuracy consistently, however, validation accuracy stays in the 80-90% region. </p>
  
  <p>I'm curious, is it possible to get high validation and training accuracy in the first epoch? If my understanding is correct, it starts small and increases steadily with each passing epoch. </p>
  
  <p>Thanks</p>
  
  <p>EDIT : The image size is 150x150 after rescaling and the mini-batch size is 16.</p>