first IA code | Python Fiddle

import tensorflow as tf

# Model parameters
W = tf.Variable([.3], dtype=tf.float32) #variables assignades que poden ser + o - correctes
b = tf.Variable([-.3], dtype=tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b # model que utilitzem
y = tf.placeholder(tf.float32)

# loss calcula el marge de error amb el nostre model 
loss = tf.reduce_sum(tf.square(linear_model - y))  # sum of the squares
# optimizer optimtza les variables w i b per fer el codi mes "inteligent" va ajustanse a ritme de 0.01
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)

# training data 
x_train = [1, 2, 3, 4] 
y_train = [0, -1, -2, -3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)  # reset values to wrong
for i in range(1000):
  sess.run(train, {x: x_train, y: y_train})

# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train}) #guardem a les variables curr_w,curr_b, curr_loss, les variables que "hem tobat durant la execuccio"
print("W: %s b: %s loss: %s" % (curr_W, curr_b, curr_loss))

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