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Example#1.py

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+import tensorflow as tf
+
+# X and Y data
+x_train = [1,2,3]
+y_train = [1,2,3]
+
+W = tf.Variable(tf.random_normal([1]), name='weight')
+b = tf.Variable(tf.random_normal([1]), name='bias')
+
+# Our hypothesis XW+b
+hypothesis = x_train * W + b
+
+# cost/loss function
+cost = tf.reduce_mean(tf.square(hypothesis-y_train))
+
+
+# minimize
+optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
+train = optimizer.minimize(cost)
+
+# Launch the graph in a session
+sess = tf.Session()
+
+# Initializes global variables in the graph.
+sess.run(tf.global_variables_initializer())
+
+#Fit the line
+for step in range(2001):
+    sess.run(train)
+    if step % 20 == 0:
+        print(step, sess.run(cost), sess.run(W), sess.run(b))