# linear regression
model = tf.matmul(X,W) + b
cost = tf.reduce_mean(tf.square(model - Y))
train = tf.train.GradientDescentOptimizer(0.01).minimize(cost)# binary classification
model = tf.sigmoid(tf.add(tf.matmul(X,W), b))
cost = tf.reduce_mean((-1)*Y*tf.log(model) + (-1)*(1-Y)*tf.log(1 - model))
train = tf.train.GradientDescentOptimizer(0.01).minimize(cost)
prediction = tf.cast(model > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction, Y), dtype=tf.float32))
# softmax
model_LC=tf.add(tf.matmul(X,W),b)
model = tf.nn.softmax(model_LC)
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model_LC, labels=Y))
cost = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(model), axis=1))
train = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(model, 1), tf.argmax(Y,1)), tf.float32))