Initial weights in Network with Keras / Tensorflow

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networkinitialkerasweights
90%

Update Oct/2021: Deprecated predict_class syntax,Deep Learning (keras),Deep Learning with Python (my book),,Gentle Introduction to the Adam Optimization Algorithm for Deep Learning

Example_snippet/controller/utility/_network.js/ TensorShapeProto. . .
TensorShapeProto
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88%

Initializers define the way to set the initial random weights of Keras layers,,Initializer capable of adapting its scale to the shape of weights tensors

Example_snippet/controller/utility/_network.js/ from tensorflow.keras import l. . .
from tensorflow.keras
import layers
from tensorflow.keras
import initializers

layer = layers.Dense(
   units = 64,
   kernel_initializer = initializers.RandomNormal(stddev = 0.01),
   bias_initializer = initializers.Zeros()
)
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72%

We can set the kernel_initializer arugment of all the Dense layers in our model to zeros to initialize our weight vectors to all zeros, Since the bias is a scalar quantity, even if we set it to zeros it won’t matter that much as it would for the weights

Example_snippet/controller/utility/_network.js/ tf.keras.layers.Dense(256, act. . .
tf.keras.layers.Dense(256, activation = 'relu', kernel_initializer = init_scheme, bias_initializer = 'zeros')
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65%

I am trying to get the initial weights for a given network,, Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers ,Save the initial weights right after compiling the model but before training it

Example_snippet/controller/utility/_network.js/ test_model = Test(n_inputs=1, . . .
test_model = Test(n_inputs = 1, neurons = 100)
test_model(np.random.random((32, 1)))
print(test_model.get_weights())
#[array([
   [0.00057544]
]), array([0.3752869])]
Step 2 continued with test_model.build(input_shape=(. . .
test_model.build(input_shape = (32, 1))
print(test_model.get_weights())
#[array([
   [8.942684e-05]
], dtype = float32), array([-1.6799461], dtype = float32)]
Step 3 continued with class Test(tf.keras.Model): . . .
class Test(tf.keras.Model):
   def __init__(self, n_inputs: int, neurons = 10):
   super(Test, self).__init__(name = "Test")
self.neurons = neurons

# Initilializers
mean, std = 0., 0.0005
bias_normalization = None
kernel_initializer = tf.keras.initializers.RandomNormal(mean = mean,
   stddev = std)
model_input = tf.keras.layers.Input(shape = (n_inputs, ))
x = tf.keras.layers.Dense(n_inputs, activation = "linear", name = "h1",
   kernel_initializer = kernel_initializer,
   bias_initializer = bias_normalization)(model_input)
self.model = tf.keras.Model(model_input, x)
Step 4 continued with test_model = Test(n_inputs=1, . . .
test_model = Test(n_inputs = 1, neurons = 100)
print(test_model.get_weights())
#[array([
   [0.00045629]
], dtype = float32), array([0.9945322], dtype = float32)]
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75%

First, instantiate a base model with pre-trained weights,,Here's what the first workflow looks like in Keras:,Instantiate a base model and load pre-trained weights into it

Example_snippet/controller/utility/_initial.js/ import numpy as np import tens. . .
import numpy as np
import tensorflow as tf
from tensorflow
import keras
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