# Why is accuracy very low and losses high and fluctuating for cnn-lstm

Below given is my cnn-lstm architecture.

model = Sequential()
input_shape=(10,128, 128 ,1)))

model.compile(optimizer='RMSProp', loss='mse', metrics=['mean_absolute_error', 'mean_absolute_percentage_error','mean_squared_error','accuracy'])

print (data.shape)
(x_train,x_test) = train_test_split(data)

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

print (x_train.shape)
print (x_test.shape)
history = model.fit(x_train, x_train,
epochs=100,
batch_size=1,
shuffle=False,
validation_data=(x_test, x_test))

encoded_imgs = model.predict(x_test)
decoded_imgs = model.predict(encoded_imgs)


I am trying to extract the compressed representation of videos using cnn-lstm inorder to use it for classification purposes using kmeans. Here is the output after a single set of training. Why are the metrics fluctuating as shown in the output. Also i trained a similar model but further training increases the validation and training loss. Ill post a link if someone would like to take a look at it.

What is actually wrong here? is it the number of neurons or hidden layers or has it got something to do with the data im feeding?