ML Lab 09

x_data = np.array([[0,0],[0,1],[1,0],[1,1]], dtype = np.float32)
y_data = np.array([[0],[1],[1],[0]], dtype = np.float32)

X= tf.placeholder(tf.float32)
Y= tf.placeholder(tf.float32)
W1= tf.Variable(tf.random_normal([2,2]),name = 'weight')
b1= tf.Variable(tf.random_normal([2]),name = 'bias')
layer1= tf.sigmoid (tf.matmul(X,W1)+b1)

W2= tf.Variable(tf.random_normal([2,1]), name = 'weight2')
b2= tf.Variable(tf.random_normal([1]), name = 'bias2')

hypothesis = tf.sigmoid(tf.matmul(layer1,W2)+b2)

cost = -tf.reduce_mean(Y* tf.log(hypothesis) + (1-Y)*tf.log(1-hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate =0.1).minimize(cost)

predicted = tf.cast(hypothesis>0.5, dtype = tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted,Y), dtype=tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for step in range(10001):
        sess.run(train, feed_dict = {X:  x_data, Y: y_data})
        if step % 100 == 0 :
            print(step, sess.run(cost,feed_dict={X:x_data,Y:y_data}), sess.run([W1,W2]))

    h,c,a = sess.run([hypothesis, predicted, accuracy], feed_dict = {X: x_data, Y: y_data})
    print("\nhypothesis: ",h, "\nPredict: ",c, "\nAccuracy: ", a)