# hunkim/DeepLearningZeroToAll

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9f8fb94 Oct 6, 2017
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 # Lab 6 Softmax Classifier import tensorflow as tf tf.set_random_seed(777) # for reproducibility x_data = [[1, 2, 1, 1], [2, 1, 3, 2], [3, 1, 3, 4], [4, 1, 5, 5], [1, 7, 5, 5], [1, 2, 5, 6], [1, 6, 6, 6], [1, 7, 7, 7]] y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]] X = tf.placeholder("float", [None, 4]) Y = tf.placeholder("float", [None, 3]) nb_classes = 3 W = tf.Variable(tf.random_normal([4, nb_classes]), name='weight') b = tf.Variable(tf.random_normal([nb_classes]), name='bias') # tf.nn.softmax computes softmax activations # softmax = exp(logits) / reduce_sum(exp(logits), dim) hypothesis = tf.nn.softmax(tf.matmul(X, W) + b) # Cross entropy cost/loss cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost) # Launch graph with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for step in range(2001): sess.run(optimizer, feed_dict={X: x_data, Y: y_data}) if step % 200 == 0: print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data})) print('--------------') # Testing & One-hot encoding a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9]]}) print(a, sess.run(tf.argmax(a, 1))) print('--------------') b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4, 3]]}) print(b, sess.run(tf.argmax(b, 1))) print('--------------') c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0, 1]]}) print(c, sess.run(tf.argmax(c, 1))) print('--------------') all = sess.run(hypothesis, feed_dict={ X: [[1, 11, 7, 9], [1, 3, 4, 3], [1, 1, 0, 1]]}) print(all, sess.run(tf.argmax(all, 1))) ''' -------------- [[ 1.38904958e-03 9.98601854e-01 9.06129117e-06]] [1] -------------- [[ 0.93119204 0.06290206 0.0059059 ]] [0] -------------- [[ 1.27327668e-08 3.34112905e-04 9.99665856e-01]] [2] -------------- [[ 1.38904958e-03 9.98601854e-01 9.06129117e-06] [ 9.31192040e-01 6.29020557e-02 5.90589503e-03] [ 1.27327668e-08 3.34112905e-04 9.99665856e-01]] [1 0 2] '''