Stacking convolutional network and recurrent layer

Question

I'm working on a classifier for video sequences. It should take several video frames on input and output a label, either 0 or 1. So, it is a many-to-one network.

I already have a classifier for single frames. This classifier makes several convolutions with Conv2D , then applies GlobalAveragePooling2D . This results in 1D vector of length 64. Then original per-frame classifier has a Dence layer with softmax activation.

Now I would like to extend this classifier to work with sequences. Ideally, sequences should be of varying length, but for now I fix the length to 4.

To extend my classifier, I'm going to replace Dense with an LSTM layer with 1 unit. So, my goal is to have the LSTM layer to take several 1D vectors of length 64, one by one, and output a label.

Schematically, what I have now:

input(99, 99, 3) - [convolutions] - features(1, 64) - [Dense] - [softmax] - label(1, 2)

Desired architecture:

4x { input(99, 99, 3) - [convolutions] - features(1, 64) } - [LSTM] - label(1, 2)

I cannot figure out, how to do it with Keras.

Here is my code for convolutions

from keras.layers import Conv2D, BatchNormalization, GlobalAveragePooling2D, \
LSTM, TimeDistributed

IMAGE_WIDTH=99
IMAGE_HEIGHT=99
IMAGE_CHANNELS=3

convolutional_layers = Sequential([
    Conv2D(input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS),
           filters=6, kernel_size=(3, 3), strides=(2, 2), activation='relu',
           name='conv1'),
    BatchNormalization(),
    Conv2D(filters=64, kernel_size=(1, 1), strides=(1, 1), activation='relu',
           name='conv5_pixel'),
    BatchNormalization(),
    GlobalAveragePooling2D(name='avg_pool6'),
])

Here is the summary:

In [24]: convolutional_layers.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv1 (Conv2D)               (None, 49, 49, 6)         168
_________________________________________________________________
batch_normalization_3 (Batch (None, 49, 49, 6)         24
_________________________________________________________________
conv5_pixel (Conv2D)         (None, 49, 49, 64)        448
_________________________________________________________________
batch_normalization_4 (Batch (None, 49, 49, 64)        256
_________________________________________________________________
avg_pool6 (GlobalAveragePool (None, 64)                0
=================================================================
Total params: 896
Trainable params: 756
Non-trainable params: 140

Now I want a recurrent layer to process sequences of these 64-dimensional vectors and output a label for each sequence.

I've read in manuals that TimeDistributed layer applies its input layer to every time slice of the input data.

I continue my code:

FRAME_NUMBER = 4

td = TimeDistributed(convolutional_layers, input_shape=(FRAME_NUMBER, 64))
model = Sequential([
    td,
    LSTM(units=1)
])

Result is the exception IndexError: list index out of range

Same exception for

td = TimeDistributed(convolutional_layers, input_shape=(None, FRAME_NUMBER, 64))

What am I doing wrong?

Answer 1

Expanding on the comments to an answer; the TimeDistributed layer applies the given layer to every time step of the input. Hence, your TimeDistributed would apply to every frame giving an input shape=(F_NUM, W, H, C) . After applying the convolution to every image, you get back (F_NUM, 64) which are features for every frame.