pythontensorflowkerasdropout

What is the proper way to implement a dropout layer in a CNN?

Is this proceeding correct?

My intention was to add a dropout layer after concatenation, but to do so i needed to adjust the concat layer's output to the appropriate shape (samples, timesteps, channels), hence expanding the dimension from (None, 4096) to (None, 1, 4096) and consequently undo the operation after the output.

enter image description here

Solution

  • If you plan to use the SpatialDropout1D layer, it has to receive a 3D tensor (batch_size, time_steps, features), so adding an additional dimension to your tensor before feeding it to the dropout layer is one option that is perfectly legitimate. Note, though, that in your case you could use both SpatialDropout1D or Dropout:

    import tensorflow as tf

samples = 2
timesteps = 1
features = 5
x = tf.random.normal((samples, timesteps, features))
s = tf.keras.layers.SpatialDropout1D(0.5)
d = tf.keras.layers.Dropout(0.5)

print(s(x, training=True))
print(d(x, training=True))

    tf.Tensor(
[[[-0.5976591  1.481788   0.         0.         0.       ]]

 [[ 0.        -4.6607018 -0.         0.7036132  0.       ]]], shape=(2, 1, 5), dtype=float32)
tf.Tensor(
[[[-0.5976591  1.481788   0.5662646  2.8400114  0.9111476]]

 [[ 0.        -0.        -0.         0.7036132  0.       ]]], shape=(2, 1, 5), dtype=float32)

    I think that SpatialDropout1D layers are most suitable after CNN layers.