Keras: Why my val_acc suddenly drops at Epoch 42/50? - tensorflow

Question

Using around 27.000 image samples for a CNN, having a very good performance, but all of a sudden, at epoch 42 the validation accuracy drops dramatically (from val_acc: 0.9982 to val_acc: 0.0678)!. Any idea? should I just stop training at the maximum val_acc? It's also weird that the validation accuracy is always higher than the training accuracy.
Using TensorFlow backend.
...
27091/27067 [==============================] - 2645s - loss: 0.0120 - acc: 0.9967 - val_loss: 0.0063 - val_acc: 0.9982
Epoch 33/50
27091/27067 [==============================] - 2674s - loss: 0.0114 - acc: 0.9971 - val_loss: 0.0145 - val_acc: 0.9975
Epoch 34/50
27091/27067 [==============================] - 2654s - loss: 0.0200 - acc: 0.9962 - val_loss: 0.0063 - val_acc: 0.9979
Epoch 35/50
27091/27067 [==============================] - 2649s - loss: 0.0137 - acc: 0.9964 - val_loss: 0.0069 - val_acc: 0.9985
Epoch 36/50
27091/27067 [==============================] - 2663s - loss: 0.0161 - acc: 0.9962 - val_loss: 0.0117 - val_acc: 0.9978
Epoch 37/50
27091/27067 [==============================] - 2680s - loss: 0.0155 - acc: 0.9959 - val_loss: 0.0039 - val_acc: 0.9993
Epoch 38/50
27091/27067 [==============================] - 2660s - loss: 0.0145 - acc: 0.9965 - val_loss: 0.0117 - val_acc: 0.9973
Epoch 39/50
27091/27067 [==============================] - 2647s - loss: 0.0111 - acc: 0.9970 - val_loss: 0.0127 - val_acc: 0.9982
Epoch 40/50
27091/27067 [==============================] - 2644s - loss: 0.0112 - acc: 0.9970 - val_loss: 0.0092 - val_acc: 0.9984
Epoch 41/50
27091/27067 [==============================] - 2658s - loss: 0.0131 - acc: 0.9967 - val_loss: 0.0057 - val_acc: 0.9982
Epoch 42/50
27091/27067 [==============================] - 2662s - loss: 0.0114 - acc: 0.7715 - val_loss: 1.1921e-07 - val_acc: 0.0678
Epoch 43/50
27091/27067 [==============================] - 2661s - loss: 1.1921e-07 - acc: 0.0714 - val_loss: 1.1921e-07 - val_acc: 0.0653
Epoch 44/50
27091/27067 [==============================] - 2668s - loss: 1.1921e-07 - acc: 0.0723 - val_loss: 1.1921e-07 - val_acc: 0.0664
Epoch 45/50
27091/27067 [==============================] - 2669s - loss: 1.1921e-07 - acc: 0.0731 - val_loss: 1.1921e-07 - val_acc: 0.0683

Answer 1

Thanks Marcin Możejkofor pointing me to the right direction.
This can happen at very high learning rates
loss can start increasing after some epochs as described here
It worked reducing the learning rate as described in the keras callbacks documentation.
Example:
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=5, min_lr=0.001)
model.fit(X_train, Y_train, callbacks=[reduce_lr])