Overfitting after first epoch

I am using convolutional neural networks (via Keras) as my model for facial expression recognition (55 subjects). My data set is quite hard and around 450k with 7 classes. I have balanced my training set per subject and per class label.

I implemented a very simple CNN architecture (with real-time data augmentation):

model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode=borderMode, init=initialization,  input_shape=(48, 48, 3)))
model.add(BatchNormalization())
model.add(PReLU())
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(256))
model.add(BatchNormalization())
model.add(PReLU())
model.add(Dropout(0.5))

model.add(Dense(nb_output))
model.add(Activation('softmax'))

After first epoch, my training loss decreases constantly while validation loss increases. Could overfitting happen that soon? Or is there a problem with my data being confusing? Should I also balance my testing set?

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It could be that the task is easy to solve and after one epoch the model has learned enough to solve it, and training for more epochs just increases overfitting.

But if you have balanced the train set and not the test set, what may be happening is that you are training for one task (expression recognition on evenly distributed data) and then you are testing on a slightly different task, because the test set is not balanced.

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