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def Fonction(x_train = [1,2,3,4],y_train = [0,-1,-2,-3]):
# Model parameters
s = tf.Variable([.3], tf.float32)
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = s * x*x + W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# 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_s, curr_W, curr_b, curr_loss = sess.run([s, W, b, loss], {x:x_train, y:y_train})
print("s: %s W: %s b: %s loss: %s"%(curr_s, curr_W, curr_b, curr_loss))
result = lambda x : round((curr_s*x*x+curr_W*x+curr_b).mean(),1)
return(result) |
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