Build a neural network with known weights and bias
By : UchihaWind
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further R do provide startweights argument to initialize custom weights, see StackOverflow thread. I also won't see citations for changing bias and transfer function. Either use MATLAB (which is not a good idea for a R expert) or better design custom network based on following fact:

Neural network bias training
By : sunil
Date : March 29 2020, 07:55 AM
help you fix your problem Moved answer from OP's question Turns out I never dealt with my training data properly. The input vector: code :
[[0.0], [1.0], [2.0], [3.0]]
[[0.0], [0.3333], [0.6666], [1.0]]
[[0.0], [2.0], [4.0], [6.0]]
[[0.0], [0.333], [0.666], [1.0]]
[[2.0], [3.0], [4.0], [5.0]]
X:
[[0.]
[1.]
[2.]
[3.]]
shape: (4, 1)
[[0.30926124]
[2.1030826 ]
[3.89690395]
[5.6907253 ]]
shape: (4, 1)
[[2.]
[3.]
[4.]
[5.]]
shape: (4, 1)
[[3.89690395]
[5.6907253 ]
[7.48454666]
[9.27836801]]

L1regularization to all weights (not weights and bias) in neural network in tensorflow
By : Lukrecija Tarvydaite
Date : March 29 2020, 07:55 AM
it should still fix some issue Since you're using Keras layers, the weights usually will have 'kernel' in their name. Use this to subset out the weights from all the trainables. code :
weights = [x for x in model.trainable_weights if 'kernel' in x.name]
tf.contrib.layers.apply_regularization(l1_regularizer, weights)
weights = [x for x in model.trainable_weights if 'bias' not in x.name]
tf.contrib.layers.apply_regularization(l1_regularizer, weights)
from tf.keras import regularizers
....
....
model = tf.keras.Sequential([
tf.keras.layers.Dense(2, activation=tf.sigmoid, input_shape=(2,), kernel_regularizer=regularizers.l1()), # input shape required
tf.keras.layers.Dense(2, activation=tf.sigmoid, kernel_regularizer=regularizers.l1())
])

Convolutional Neural Network : Weights and Bias initialization
By : Nnamdi Osisiogu
Date : March 29 2020, 07:55 AM
help you fix your problem The number of input channels for the weights wc2, wc3, and wc4 need to be the same as the number of output channels for the previous layers. Keeping the number of output channels the way you have it, they would be changed to: code :
'wc1': tf.get_variable('W0', shape=(3,3,1,8), initializer=tf.contrib.layers.xavier_initializer()),
'wc2': tf.get_variable('W1', shape=(3,3,8,12), initializer=tf.contrib.layers.xavier_initializer()),
'wc3': tf.get_variable('W2', shape=(3,3,12,16), initializer=tf.contrib.layers.xavier_initializer()),
'wc4': tf.get_variable('W3', shape=(3,3,16,20), initializer=tf.contrib.layers.xavier_initializer()),

How to print weights and bias during training after each epoch
By : Jack Harper
Date : March 29 2020, 07:55 AM
I hope this helps you . model.compile does not take the callbacks, that is given to model.fit.

