Accuracy of model got stuck at 50% while training an Age and Gender detection model

Question

I was working through the Keras implementation of Age and Gender Detection model described in the research paper Age and Gender Classification using Convolutional Neural Networks'. It was originally a Caffe model but I thought to convert it to Keras. But while I was training the model, the accuracy of the model got stuck around 49 - 52%. It means that the model is not learning at all. Also, the loss can be seen exponentially increasing and at times becomes nan. I was training on google collab with GPU hardware accelerator.

My input was a folder of images whose labels are in its file name.I loaded all the images as a numpy array and labels were a collection of 10 elements (2 for gender and 8 classes for 8 different age groups as described in the paper).

model = Sequential()
model.add(Conv2D(96,(7,7),
                 activation= 'relu',
                 strides= 4,
                 use_bias= 1,
                 bias_initializer= 'Zeros',
                 data_format= 'channels_last',
                 kernel_initializer = RandomNormal(stddev= 0.01),
                 input_shape= (200,200,3)))
model.add(MaxPooling2D(pool_size= 3,
                       strides= 2))
model.add(BatchNormalization())

model.add(Conv2D(256,(5,5),
                 activation= 'relu',
                 strides= 1,
                 use_bias= 1,
                 data_format= 'channels_last',
                 bias_initializer= 'Ones',
                 kernel_initializer = RandomNormal(stddev= 0.01)
                 ))
model.add(MaxPooling2D(pool_size= 3,
                       strides= 2))
model.add(BatchNormalization())

model.add(Conv2D(384,
                 (3,3),
                 strides= 1,
                 data_format= 'channels_last',
                 use_bias= 1,
                 bias_initializer= 'Zeros',
                 padding= 'same',
                 kernel_initializer = RandomNormal(stddev= 0.01),
                 activation= 'relu'))
model.add(MaxPooling2D(pool_size= 3,
                       strides= 2))

model.add(Flatten())
model.add(Dense(512,
                use_bias= 1,
                bias_initializer= 'Ones',
                kernel_initializer= RandomNormal(stddev= 0.05),
                activation= 'relu'))
model.add(Dropout(0.5))

model.add(Dense(512,
                use_bias= 1,
                bias_initializer= 'Ones',
                kernel_initializer= RandomNormal(stddev= 0.05),
                activation= 'relu'))
model.add(Dropout(0.5))

model.add(Dense(10,
                use_bias= 1,
                kernel_initializer= RandomNormal(stddev= 0.01),
                bias_initializer= 'Zeros',
                activation= 'softmax'))

model.compile(loss= 'categorical_crossentropy', metrics= ['accuracy'], optimizer= SGD(lr= 0.0001, decay= 1e-7, nesterov= False))
model.summary()

Inputs to the model were shuffled:

X_train, X_test, y_train, y_test = train_test_split(images,labels,test_size= 0.2,shuffle= True, random_state= 42)

You can see my training results here I have used correct optimizers and correct initializers along with biases to prevent vanishing gradients.

Answer 1

Would suggest to follow the below approach to improve the accuracy of the model -

• Build two different models, one for the Gender prediction and another for the Age prediction.
• Use Label Encoder or One hot encoder on the target variables.
• For Gender Predcition model use Binary crossentrpy as loss function.
• For Age prediction model use Categorical crossentropy(if you have used Label Encoder for target variable) or sparse categorical crossentropy(if you used one hot encoder for target variable).
• Before building the model normalize all the numerical data.
• Use softmax in the final layer as activation function and relu in remaining layers.
• Also instead of 2 hidden dense layers, keep just 1(more dense layer means more weight to learn, you can experiment with the number of layers and filters).

Hope I have answered your question. Happy Learning!