CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdeep-learning
Trush P
Trush P

Reputation: 53

Good training/validation accuracy but poor test accuracy

Ive trained a model to classify 4 types of eye diseases using the VGG16 pretrained model. I am fairly new to machine learning so didn't know what to make out of the results. After training it for about 6 hours on 90,000 images:

After looking at the confusion matrix, it seems my test isn't performing well

Image_height = 196
Image_width = 300
val_split = 0.2
batches_size = 10
lr = 0.0001
spe = 512
vs = 32
epoch = 10

#Creating batches
#Creating batches
train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input,validation_split=val_split) \
    .flow_from_directory(directory=train_folder, target_size=(Image_height,Image_width), classes=['CNV','DME','DRUSEN','NORMAL'], batch_size=batches_size,class_mode="categorical",
                              subset="training")
validation_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input,validation_split=val_split) \
    .flow_from_directory(directory=train_folder, target_size=(Image_height,Image_width), classes=['CNV','DME','DRUSEN','NORMAL'], batch_size=batches_size,class_mode="categorical",
                              subset="validation")
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
                       .flow_from_directory(test_folder, target_size=(Image_height,Image_width), 
                         classes=['CNV','DME','DRUSEN','NORMAL'], batch_size=batches_size,class_mode="categorical")

#Function to create model. We will be using a pretrained model
def create():
  vgg16_model = keras.applications.vgg16.VGG16(input_tensor=Input(shape=(Image_height, Image_width, 3)),input_shape=(Image_height,Image_width,3), include_top = False)
  model = Sequential()
  model.add(vgg16_model)
  for layer in model.layers:
    layer.trainable = False
  model.add(Flatten())
  model.add(Dense(4, activation='softmax'))
  return model

model = create()
model.compile(Adam(lr=lr),loss="categorical_crossentropy",metrics=['accuracy'])

model.fit(train_batches, steps_per_epoch=spe,
                    validation_data=validation_batches,validation_steps=vs, epochs=epoch)

enter image description here

Any suggestions on what I can improve on so the confusion matrix isn't doing so poorly? I also have the model saved if its possible to just retrain it with more layers.

Upvotes: 0

Views: 3450

Answers (3)

Mustafain Rehmani
Mustafain Rehmani

Reputation: 19

while adding layers to model you have to remove last dense layer of the model, as your model has four classes but vgg16 has 1000 classes so you have to remove last dense layer then add your own dense layers:

def create():
    vgg16_model = keras.applications.vgg16.VGG16(input_tensor=Input(shape=(Image_height, Image_width, 3)),input_shape=(Image_height,Image_width,3), include_top = False)

    model = Sequential()
    for layer in vgg16_model.layers[:-1]:
        model.add(layer)
    model.summary()

    for layer in model.layers:
        layer.trainable = False
    model.add(Flatten())
    model.add(Dense(4, activation='softmax'))

    return model

Upvotes: 0

Gerry P
Gerry P

Reputation: 8092

A number of issues and recommendations. You are using VGG16 model. That model has over 40 million trainable parameters. On a data set of 90,000 images your training time will be very long. So I recommend you consider using the MobileNet model. It only has 4 million trainable parameters and is essentially just as accurate as VGG16. Documentation is [here.][1] Next irrespective of which model you use you should set the initial weights to the imagenet weights. Your model will start off trained on images.I find I get better results by making all layers in the model trainable. Now you say your model reached an accuracy of 88%. I do not think that is very good. I believe you need to achieve at least 95%. You can do that by using an adjustable learning rate. The keras callback ReduceLROnPlateau makes doing that easy. Documentation is [here.][2] Set it up to monitor validation loss and reduce the learning rate if it fails to decrease on consecutive epochs. Next you want to save the model that has the lowest validation loss and use that to make predictions. The Keras callback ModelCheckpoint can be set up to monitor validation loss and save the model with the lowest loss. Documentation is [here.][3] . Code below shows how to implement the MobileNet model for your problem and define the callbacks. You will also have to make changes to the generator to use Mobilenet preprocessing and set target size to (224,224). Also I believe you are missing () around the pre-processing function Hope this helps..

mobile = tf.keras.applications.mobilenet.MobileNet( include_top=False,
                                                           input_shape=(224, 224,3),
                                                           pooling='max', weights='imagenet',
                                                           alpha=1, depth_multiplier=1,dropout=.5)                                                          
x=mobile.layers[-1].output
x=keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001 )(x)
predictions=Dense (4, activation='softmax')(x)
model = Model(inputs=mobile.input, outputs=predictions)    
for layer in model.layers:
    layer.trainable=True
model.compile(Adamax(lr=lr), loss='categorical_crossentropy', metrics=['accuracy'])
checkpoint=tf.keras.callbacks.ModelCheckpoint(filepath=save_loc, monitor='val_loss', verbose=0, save_best_only=True,
    save_weights_only=False, mode='auto', save_freq='epoch', options=None)
lr_adjust=tf.keras.callbacks.ReduceLROnPlateau( monitor="val_loss", factor=0.5, patience=1, verbose=0, mode="auto",
    min_delta=0.00001,  cooldown=0,  min_lr=0) 
callbacks=[checkpoint, lr_adjust]


  [1]: http://httphttps://keras.io/api/applications/mobilenet/s://
  [2]: https://keras.io/api/callbacks/reduce_lr_on_plateau/
  [3]: https://keras.io/api/callbacks/model_checkpoint/

Upvotes: 3

Dominiksr
Dominiksr

Reputation: 78

You don't train any layer except the last one. You need to set the training capability to the last few or add more layers.

Add

tf.keras.applications.VGG16(... weights='imagenet'... )

In your code, the weights are not pretrained on any set.

The available options are explained here:

https://www.tensorflow.org/api_docs/python/tf/keras/applications/VGG16

Upvotes: 1

Related Questions