out = f(X@W + b)

out = relut(X@W + b)

import tensorflow as tf

x = tf.random.normal([4, 784])

net = tf.keras.layers.Dense(512)

out = net(x)

out.shape

net.kernel.shape, net.bias.shape

net = tf.keras.layers.Dense(10)

try:

    net.bias

except Exception as e:

    print(e)

net.build(input_shape=(None, 4))

net.kernel.shape, net.bias.shape

net.build(input_shape=(None, 20))

net.kernel.shape, net.bias.shape

net.build(input_shape=(2, 4))

net.kernel

from tensorflow import keras

x = tf.random.normal([2, 3])

model = keras.Sequential([

    keras.layers.Dense(2, activation='relu'),

    keras.layers.Dense(2, activation='relu'),

    keras.layers.Dense(2)

])

model.build(input_shape=[None, 3])

model.summary()

# [w1,b1,w2,b2,w3,b3]

for p in model.trainable_variables:

    print(p.name, p.shape)