How to create this custom ANN using tensorflow? - tensorflow

Question

I am trying to create this custom ANN using tensorflow. Here is image of the toy network and code.
import tensorflow as tf
import numpy as np
in = np.array([1, 2, 3, 4], , dtype="float32")
y_true = np.array([10, 11], , dtype="float32")
# w is vector of weights
# y_pred = np.array([in[0]*w[0]+in[1]*w[0]], [in[2]*w[1]+in[3]*w[1]] )
# y_pred1 = 1 / (1 + tf.math.exp(-y_pred)) # sigmoid activation function
def loss_fun(y_true, y_pred1):
loss1 = tf.reduce_sum(tf.pow(y_pred1 - y_true, 2))
# model.compile(loss=loss_fun, optimizer='adam', metrics=['accuracy'])
The output of this network goes to another ANN to the right and I know that stuff, but don't know how can I create the connections, update the w, y_pred, and compile the model. Any help?

Answer 1

Something like this ought to work
import tensorflow as tf
import numpy as np
def y_pred(x, w):
return [x[0]*w[0]+x[1]*w[0], x[2]*w[1]+x[3]*w[1]]
def loss_fun(y_true, y_pred):
return tf.reduce_sum(tf.pow(y_pred - y_true, 2))
x = np.array([1, 2, 3, 4], dtype="float32")
y_true = np.array([10, 11], dtype="float32")
w = tf.Variable(initial_value=np.random.normal(size=(2)), name='weights', dtype=tf.float32)
xt = tf.convert_to_tensor(x)
yt = tf.convert_to_tensor(y_true)
sgd_opt = tf.optimizers.SGD()
training_steps = 100
display_steps = 10
for step in range(training_steps):
with tf.GradientTape() as tape:
tape.watch(w)
yp = y_pred(xt, w)
loss = loss_fun(yt, yp)
dl_dw = tape.gradient(loss, w)
sgd_opt.apply_gradients(zip([dl_dw], [w]))
if step % display_steps == 0:
print(loss, w)