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# Method 1
hypothesis = tf.matmul(X,W) + b
cost = tf.nn.sigmoid_cross_entropy_with_logits(hypothesis, Y)
optimizer = tf.train.GradientDescentOptimizer(0.001).minimize(cost)
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

# Method 2
hypothesis2 = tf.sigmoid(tf.matmul(X,W) + b)
cost2 = -tf.reduce_mean(Y * tf.log(hypothesis2) + (1-Y)*tf.log(1-hypothesis2))
optimizer2 = tf.train.GradientDescentOptimizer(0.001).minimize(cost2)
predicted2 = tf.cast(hypothesis2 > 0.5, dtype=tf.float32)
accuracy2 = tf.reduce_mean(tf.cast(tf.equal(predicted2, Y), dtype=tf.float32))

x_train = [[1.,2.],[3.,4.]]
y_train = [4.,8.]

# Build a graph
X = tf.placeholder(tf.float32, shape=[None,2])
Y = tf.placeholder(tf.float32, shape=[None,1])

W = tf.Variable(tf.random_normal([2,1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

hypothesis = tf.matmul(X,W) + b
cost = tf.square(hypothesis - Y)

y_train = [[4.],[8.]]