CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdeep-learning
student17
student17

Reputation: 791

Constant Validation Accuracy with a high loss in machine learning

I'm currently trying to do create an image classification model using Inception V3 with 2 classes. I have 1428 images which are balanced about 70/30. When I run my model I get a pretty high loss of as well as a constant validation accuracy. What might be causing this constant value?

data = np.array(data, dtype="float")/255.0
labels = np.array(labels,dtype ="uint8")

(trainX, testX, trainY, testY) = train_test_split(
                            data,labels, 
                            test_size=0.2, 
                            random_state=42) 

img_width, img_height = 320, 320 #InceptionV3 size

train_samples =  1145 
validation_samples = 287
epochs = 20

batch_size = 32

base_model = keras.applications.InceptionV3(
        weights ='imagenet',
        include_top=False, 
        input_shape = (img_width,img_height,3))

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(0.2))
model_top.add(keras.layers.Dense(1,activation = 'sigmoid'))
model = keras.models.Model(inputs = base_model.input, outputs = model_top(base_model.output))


for layer in model.layers[:30]:
  layer.trainable = False

model.compile(optimizer = keras.optimizers.Adam(
                    lr=0.00001,
                    beta_1=0.9,
                    beta_2=0.999,
                    epsilon=1e-08),
                    loss='binary_crossentropy',
                    metrics=['accuracy'])

#Image Processing and Augmentation 
train_datagen = keras.preprocessing.image.ImageDataGenerator(
          zoom_range = 0.05,
          #width_shift_range = 0.05, 
          height_shift_range = 0.05,
          horizontal_flip = True,
          vertical_flip = True,
          fill_mode ='nearest') 

val_datagen = keras.preprocessing.image.ImageDataGenerator()


train_generator = train_datagen.flow(
        trainX, 
        trainY,
        batch_size=batch_size,
        shuffle=True)

validation_generator = val_datagen.flow(
                testX,
                testY,
                batch_size=batch_size)

history = model.fit_generator(
    train_generator, 
    steps_per_epoch = train_samples//batch_size,
    epochs = epochs, 
    validation_data = validation_generator, 
    validation_steps = validation_samples//batch_size,
    callbacks = [ModelCheckpoint])

This is my log when I run my model:

Epoch 1/20
35/35 [==============================]35/35[==============================] - 52s 1s/step - loss: 0.6347 - acc: 0.6830 - val_loss: 0.6237 - val_acc: 0.6875

Epoch 2/20
35/35 [==============================]35/35 [==============================] - 14s 411ms/step - loss: 0.6364 - acc: 0.6756 - val_loss: 0.6265 - val_acc: 0.6875

Epoch 3/20
35/35 [==============================]35/35 [==============================] - 14s 411ms/step - loss: 0.6420 - acc: 0.6743 - val_loss: 0.6254 - val_acc: 0.6875

Epoch 4/20
35/35 [==============================]35/35 [==============================] - 14s 414ms/step - loss: 0.6365 - acc: 0.6851 - val_loss: 0.6289 - val_acc: 0.6875

Epoch 5/20
35/35 [==============================]35/35 [==============================] - 14s 411ms/step - loss: 0.6359 - acc: 0.6727 - val_loss: 0.6244 - val_acc: 0.6875

Epoch 6/20
35/35 [==============================]35/35 [==============================] - 15s 415ms/step - loss: 0.6342 - acc: 0.6862 - val_loss: 0.6243 - val_acc: 0.6875

Upvotes: 4

Views: 1895

Answers (2)

tomsgd
tomsgd

Reputation: 1135

Your accuracy is 68.25%. Given that your classes are split roughly 70/30 it is likely that your model is just predicting the same thing every time, ignoring the input. That would give the accuracy you are seeing. Your model has not yet learned from your data.

As Novak said, your learning rate seems very low, so maybe try increasing that first to see if that helps.

Upvotes: 4

Novak
Novak

Reputation: 2171

I think you have too low learning rate and too few epochs. try with lr = 0.001 and epochs = 100.

Upvotes: 5

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