How Keras add two layers?

Sun, 13 Dec 2020 13:44:00 +0800

How Keras add two layers?

tf.keras.layers.add() method can add two layer?

What it do is sum the values of corresponding positions in two layers.

For example:


input_shape = (1,2,3)
import tensorflow as tf
tf.enable_eager_execution()

print("----------x1 tensor-----------")
x1 = tf.random.uniform(input_shape, maxval=10, dtype=tf.dtypes.int32)
tf.print(x1);
print("----------x2 tensor-----------")
x2 = tf.random.uniform(input_shape, maxval=10, dtype=tf.dtypes.int32)
tf.print(x2);
print("----------add 2 tensors-----------")
y = tf.keras.layers.add([x1,x2])
tf.print(y);

Output: ———-x1 tensor———– [[[7 6 1] [5 7 2]]] ———-x2 tensor———– [[[0 7 8] [2 9 6]]] ———-add 2 tensors———– [[[7 13 9] [7 16 8]]]

如何计算一个BatchNormalization的参数？

Tue, 17 Nov 2020 19:48:00 +0800

如何计算一个BatchNormalization的参数？

# Environment：
# OS			macOS Catalina 10.15.6
# python 		3.7
# pip 			20.1.1
# tensorflow	1.14.0
# Keras 		2.1.5

from keras.models import Sequential
from keras.layers import Conv2D,BatchNormalization
model = Sequential();
# conv2d + max pooling
model.add(
	Conv2D(96, 
		kernel_size = (11,11), 
		strides=(4, 4), 
		padding="valid", 
		input_shape=(224,224,3),
		activation="relu")
	); # output 55 * 55 * 96 

# batchNormalization ! 
model.add(BatchNormalization()) # output
model.summary();

output:

Keras on Yaohong

How Keras add two layers?

How Keras add two layers?

如何计算一个BatchNormalization的参数？

如何计算一个BatchNormalization的参数？