import tensorflow as tf import numpy as np tf.set_random_seed(777) # for reproducibility # Predicting animal type based on various features xy = np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.float32) x_data = xy[:, 0:-1] y_data = xy[:, [-1]] print(x_data.shape, y_data.shape) nb_classes = 7 # 0 ~ 6 X = tf.placeholder(tf.float32, [None, 16]) Y = tf.placeholder(tf.int32, [None, 1]) # 0 ~ 6 Y_one_hot = tf.one_hot(Y, nb_classes) # one hot print("one_hot", Y_one_hot) Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes]) print("reshape", Y_one_hot) W = tf.Variable(tf.random_normal([16, 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) logits = tf.matmul(X, W) + b hypothesis = tf.nn.softmax(logits) # Cross entropy cost/loss cost_i = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y_one_hot) cost = tf.reduce_mean(cost_i) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost) prediction = tf.argmax(hypothesis, 1) correct_prediction = tf.equal(prediction, tf.argmax(Y_one_hot, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # Launch graph with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for step in range(2000): sess.run(optimizer, feed_dict={X: x_data, Y: y_data}) if step % 100 == 0: loss, acc = sess.run([cost, accuracy], feed_dict={ X: x_data, Y: y_data}) print("Step: {:5}\tLoss: {:.3f}\tAcc: {:.2%}".format( step, loss, acc)) # Let's see if we can predict pred = sess.run(prediction, feed_dict={X: x_data}) # y_data: (N,1) = flatten => (N, ) matches pred.shape for p, y in zip(pred, y_data.flatten()): print("[{}] Prediction: {} True Y: {}".format(p == int(y), p, int(y))) ''' Step: 0 Loss: 5.106 Acc: 37.62% Step: 100 Loss: 0.800 Acc: 79.21% Step: 200 Loss: 0.486 Acc: 88.12% Step: 300 Loss: 0.349 Acc: 90.10% Step: 400 Loss: 0.272 Acc: 94.06% Step: 500 Loss: 0.222 Acc: 95.05% Step: 600 Loss: 0.187 Acc: 97.03% Step: 700 Loss: 0.161 Acc: 97.03% Step: 800 Loss: 0.140 Acc: 97.03% Step: 900 Loss: 0.124 Acc: 97.03% Step: 1000 Loss: 0.111 Acc: 97.03% Step: 1100 Loss: 0.101 Acc: 99.01% Step: 1200 Loss: 0.092 Acc: 100.00% Step: 1300 Loss: 0.084 Acc: 100.00% ... [True] Prediction: 0 True Y: 0 [True] Prediction: 0 True Y: 0 [True] Prediction: 3 True Y: 3 [True] Prediction: 0 True Y: 0 [True] Prediction: 0 True Y: 0 [True] Prediction: 0 True Y: 0 [True] Prediction: 0 True Y: 0 [True] Prediction: 3 True Y: 3 [True] Prediction: 3 True Y: 3 [True] Prediction: 0 True Y: 0 '''
lab-06-2-softmax_zoo_classifier
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转载自blog.csdn.net/qq_30868235/article/details/80903882
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