what is the difference between Flatten() and GlobalAveragePooling2D() in keras

Question

I want to pass the output of ConvLSTM and Conv2D to a Dense Layer in Keras, what is the difference between using global average pooling and flatten Both is working in my cas

Answer 1

You can test the difference between Flatten and GlobalPooling on your own making comparison with numpy, if you are more confident

We make a demonstration using, as input, a batch of images with this shape (batch_dim, height, width, n_channel)

import numpy as np
from tensorflow.keras.layers import *

batch_dim, H, W, n_channels = 32, 5, 5, 3
X = np.random.uniform(0,1, (batch_dim,H,W,n_channels)).astype('float32')

• Flatten accepts as input tensor of at least 3D. It operates a reshape of the input in 2D with this format (batch_dim, all the rest). In our case of 4D, it operates a reshape in this format (batch_dim, H*W*n_channels).

  np_flatten = X.reshape(batch_dim, -1) # (batch_dim, H*W*n_channels)
  tf_flatten = Flatten()(X).numpy() # (batch_dim, H*W*n_channels)
  
  (tf_flatten == np_flatten).all() # True
  

• GlobalAveragePooling2D accepts as input 4D tensor. It operates the mean on the height and width dimensionalities for all the channels. The resulting dimensionality is 2D (batch_dim, n_channels). GlobalMaxPooling2D makes the same but with max operation.

  np_GlobalAvgPool2D = X.mean(axis=(1,2)) # (batch_dim, n_channels)
  tf_GlobalAvgPool2D = GlobalAveragePooling2D()(X).numpy() # (batch_dim, n_channels)
  
  (tf_GlobalAvgPool2D == np_GlobalAvgPool2D).all() # True