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 2 Answers
Answers 1
I think you have too low learning rate and too few epochs. try with lr = 0.001 and epochs = 100.
Answers 2
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.
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