Poor accuracy of CNN model with Keras

Question

I need advice. I got a very poor result(10% accuracy) when building a CNN model with Keras when only using a subset of CIFAR10 dataset (only use 10000 data, 1000 per class). How can I increase the accuracy? I try to change/increase the epoch, but the result is still the same. Here is my CNN architecture:

cnn = models.Sequential()
cnn.add(layers.Conv2D(25, (3, 3), input_shape=(32, 32, 3)))
cnn.add(layers.MaxPooling2D((2, 2)))
cnn.add(layers.Activation('relu'))
cnn.add(layers.Conv2D(50, (3, 3)))
cnn.add(layers.MaxPooling2D((2, 2)))
cnn.add(layers.Activation('relu'))
cnn.add(layers.Conv2D(100, (3, 3)))
cnn.add(layers.MaxPooling2D((2, 2)))
cnn.add(layers.Activation('relu'))
cnn.add(layers.Flatten())
cnn.add(layers.Dense(100))
cnn.add(layers.Activation('relu'))
cnn.add(layers.Dense(10))
cnn.add(layers.Activation('softmax'))

compile and fit:

EPOCHS = 200
BATCH_SIZE = 10
LEARNING_RATE = 0.1

cnn.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE),
            loss='binary_crossentropy',
            metrics=['accuracy'])

es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)
mc = ModelCheckpoint(filepath=checkpoint_path, monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)

history_cnn = cnn.fit(train_images, train_labels, epochs=EPOCHS, batch_size=BATCH_SIZE,
                validation_data=(test_images, test_labels),callbacks=[es, mc],verbose=0)

The data i use is CIFAR10, but i only take 1000 images per class so total data is only 10000. I use normalization for preprocessing the data.

Answer 1

First of all, the problem is the loss. Your dataset is a multi-class problem , not a binary and not multi-label one

As stated here :

The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.

In this situation is suggested the use of the categorical crossentropy . Keep in mind that if your label are sparse (encoded with the number between 0 and 999) and not as one hot encoded vector ([0, 0, 0... 1, 0, 0]) you should use the sparse categorical crossentropy .

• not sparse (labels encoded as vectors [0, 0, 1,....0])

   cnn.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE), loss='categorical_crossentropy', metrics=['accuracy'])

• sparse (labels encoded as numbers in (0, ... 999))

   cnn.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Also, the learning rate is quite high (0.1). I'll suggest you to start with something lower (0.001) for example.

this post is also relevant for your problem

Edit: my bad, for the number of filters it is a commong approach having an increasing number of filters