title: Increasing Accuracy of Image Classification Model
body: <p>I'm trying to use do image classification on two different classes using the pre-trained Inception V3 model. I have a data set of around 1400 images which are roughly balanced. When I run my program I get results that are off at the first couple epochs. Is this normal when training the model?</p>
  
  <pre><code>epochs =  175
  
  batch_size = 64
  
  #include_top = false to accomodate new classes 
  base_model = keras.applications.InceptionV3(
          weights ='imagenet',
          include_top=False, 
          input_shape = (img_width,img_height,3))
  
  #Classifier Model ontop of Convolutional Model
  model_top = keras.models.Sequential()
  model_top.add(keras.layers.GlobalAveragePooling2D(input_shape=base_model.output_shape[1:], data_format=None)),
  model_top.add(keras.layers.Dense(350,activation='relu'))
  model_top.add(keras.layers.Dropout(0.4))
  model_top.add(keras.layers.Dense(1,activation = 'sigmoid'))
  model = keras.models.Model(inputs = base_model.input, outputs = model_top(base_model.output))
  
  #freeze the convolutional layers of InceptionV3
  for layer in model.layers[:30]:
  layer.trainable = False
  
  #Compiling model using Adam Optimizer 
  model.compile(optimizer = keras.optimizers.Adam(
                      lr=0.000001,
                      beta_1=0.9,
                      beta_2=0.999,
                      epsilon=1e-08),
                      loss='binary_crossentropy',
                      metrics=['accuracy'])
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/d23Fn.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/d23Fn.png" alt="enter image description here"></a></p>
  
  <p>With my current parameters I only get an accuracy of 89% with a test loss of 0.3 when testing on a separated set of images. Do I need to add more layers to my model to increase this accuracy?</p>