How do I set custom weights for my sequential model?

Solution 1:

Using tf.keras.initializers.random_normal() like that will not work when trying to use it for a Keras layer. Check the docs here for example. Also, you should not hard-code the shape of your weights beforehand. It will be inferred based on the input to your model. You could try something like this:

import tensorflow as tf

def random_normal_init(shape, dtype=None):
    return tf.random.normal(shape) * 100    

model = tf.keras.Sequential([
                             tf.keras.layers.Dense(5, activation="tanh", input_shape=(5,), kernel_initializer=random_normal_init),
                             tf.keras.layers.Dense(2, activation="tanh"),
                             tf.keras.layers.Dense(2, activation="tanh"),
                             tf.keras.layers.Dense(1, activation="sigmoid")
])
samples = 20
print(model(tf.random.normal((samples, 5))))

tf.Tensor(
[[0.2567306 ]
 [0.79331714]
 [0.74326944]
 [0.35187328]
 [0.18808913]
 [0.81191087]
 [0.6069946 ]
 [0.74326944]
 [0.65107304]
 [0.39300534]
 [0.6069946 ]
 [0.81191087]
 [0.61664075]
 [0.35496145]
 [0.81191087]
 [0.2567306 ]
 [0.38335925]
 [0.2567306 ]
 [0.50955486]
 [0.74326944]], shape=(20, 1), dtype=float32)