Create keras_mean_pooling.py

Author: imohitmayank

deep_learning/keras_mean_pooling.py

@@ -0,0 +1,31 @@
+"""Use Avg pooling on output of LSTM, RNN, GRU, or any recurrent layer.
+Author: Mohit Mayank
+Taken from my answer to this question: https://stackoverflow.com/questions/36428323/lstm-followed-by-mean-pooling/64630846#64630846
+"""
+
+# create sample data
+A=np.array([[1,2,3],[4,5,6],[0,0,0],[0,0,0],[0,0,0]])
+B=np.array([[1,3,0],[4,0,0],[0,0,1],[0,0,0],[0,0,0]])
+C=np.array([A,B]).astype("float32")
+# expected answer (for temporal mean)
+np.mean(C, axis=1)
+"""
+The output is
+
+array([[1. , 1.4, 1.8],
+ [1. , 0.6, 0.2]], dtype=float32)
+Now using AveragePooling1D,
+"""
+model = keras.models.Sequential(
+ tf.keras.layers.AveragePooling1D(pool_size=5)
+)
+model.predict(C)
+"""
+The output is,
+
+array([[[1. , 1.4, 1.8]],
+ [[1. , 0.6, 0.2]]], dtype=float32)
+Some points to consider,
+- The pool_size should be equal to the step/timesteps size of the recurrent layer.
+- The shape of the output is (batch_size, downsampled_steps, features), which contains one additional downsampled_steps dimension. This will be always 1 if you set the pool_size equal to timestep size in recurrent layer.
+"""