SEQUENCE MODEL EX-13

 import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

# Define Sequential model with 3 layers

model = keras.Sequential([

layers.Dense(2, activation="relu",

name="layer1"),

layers.Dense(3, activation="relu",

name="layer2"),

layers.Dense(4, name="layer3"),

])

# Call model on a test input

x = tf.ones((3, 3))

y = model(x)

# Create 3 layers

layer1 = layers.Dense(2, activation="relu",

name="layer1")

layer2 = layers.Dense(3, activation="relu",

name="layer2")

layer3 = layers.Dense(4, name="layer3")

# Call layers on a test input

x = tf.ones((3, 3))

y = layer3(layer2(layer1(x)))

model = keras.Sequential(

[

layers.Dense(2, activation="relu"),

layers.Dense(3, activation="relu"),

layers.Dense(4),

]

)

model.layers

model = keras.Sequential()

model.add(layers.Dense(2, activation="relu"))

model.add(layers.Dense(3, activation="relu"))

model.add(layers.Dense(4))

model.pop()

print(len(model.layers)) # 2

model = keras.Sequential(name="my_sequential")

model.add(layers.Dense(2, activation="relu",

name="layer1"))

model.add(layers.Dense(3, activation="relu",

name="layer2"))

model.add(layers.Dense(4, name="layer3"))

layer = layers.Dense(3)

layer.weights # Empty

# Call layer on a test input

x = tf.ones((1, 4))

y = layer(x)

layer.weights # Now it has weights, of shape (4, 3) and (3,)

model = keras.Sequential(

[

layers.Dense(2, activation="relu"),

layers.Dense(3, activation="relu"),

layers.Dense(4),

]

) # No weights at this stage!

# At this point, you can't do this:

# model.weights

# You also can't do this:

# model.summary()

# Call the model on a test input

x = tf.ones((1, 4))

y = model(x)

print("Number of weights after calling the model:",

len(model.weights)) # 6

model.summary()

model = keras.Sequential()

model.add(keras.Input(shape=(4,)))

model.add(layers.Dense(2, activation="relu"))

model.summary()