60個字符解決fizzbuzz問題：

for x in range(101):print"fizz"[x%3*4::]+"buzz"[x%5*4::]or x

下面是用tensorflow解決，跟上面的比起來非常復雜，但很有意思，而且適合學習tensorflow，發散一下思維，拓展tensorflow的應用范圍。

tensorflow 應用fizzbuzz

轉載請注明地址：http://www.cnblogs.com/SSSR/p/5630497.html

直接上代碼如下：

具體案例解釋請參考：http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/

# -*- coding: utf-8 -*- """ Created on Wed Jun 29 10:57:41 2016@author: ubuntu """# Fizz Buzz in Tensorflow! # see http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/import numpy as np import tensorflow as tfNUM_DIGITS = 10# Represent each input by an array of its binary digits. def binary_encode(i, num_digits):return np.array([i >> d & 1 for d in range(num_digits)])# One-hot encode the desired outputs: [number, "fizz", "buzz", "fizzbuzz"] def fizz_buzz_encode(i):if i % 15 == 0: return np.array([0, 0, 0, 1])elif i % 5 == 0: return np.array([0, 0, 1, 0])elif i % 3 == 0: return np.array([0, 1, 0, 0])else: return np.array([1, 0, 0, 0])# Our goal is to produce fizzbuzz for the numbers 1 to 100. So it would be # unfair to include these in our training data. Accordingly, the training data # corresponds to the numbers 101 to (2 ** NUM_DIGITS - 1). trX = np.array([binary_encode(i, NUM_DIGITS) for i in range(101, 2 ** NUM_DIGITS)]) trY = np.array([fizz_buzz_encode(i) for i in range(101, 2 ** NUM_DIGITS)])# We'll want to randomly initialize weights. def init_weights(shape):return tf.Variable(tf.random_normal(shape, stddev=0.01))# Our model is a standard 1-hidden-layer multi-layer-perceptron with ReLU # activation. The softmax (which turns arbitrary real-valued outputs into # probabilities) gets applied in the cost function. def model(X, w_h, w_o):h = tf.nn.relu(tf.matmul(X, w_h))return tf.matmul(h, w_o)# Our variables. The input has width NUM_DIGITS, and the output has width 4. X = tf.placeholder("float", [None, NUM_DIGITS]) Y = tf.placeholder("float", [None, 4])# How many units in the hidden layer. NUM_HIDDEN = 100# Initialize the weights. w_h = init_weights([NUM_DIGITS, NUM_HIDDEN]) w_o = init_weights([NUM_HIDDEN, 4])# Predict y given x using the model. py_x = model(X, w_h, w_o)# We'll train our model by minimizing a cost function. cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)# And we'll make predictions by choosing the largest output. predict_op = tf.argmax(py_x, 1)# Finally, we need a way to turn a prediction (and an original number) # into a fizz buzz output def fizz_buzz(i, prediction):return [str(i), "fizz", "buzz", "fizzbuzz"][prediction]BATCH_SIZE = 128# Launch the graph in a session with tf.Session() as sess:tf.initialize_all_variables().run()for epoch in range(10000):# Shuffle the data before each training iteration.p = np.random.permutation(range(len(trX)))trX, trY = trX[p], trY[p]# Train in batches of 128 inputs.for start in range(0, len(trX), BATCH_SIZE):end = start + BATCH_SIZEsess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})# And print the current accuracy on the training data.print(epoch, np.mean(np.argmax(trY, axis=1) ==sess.run(predict_op, feed_dict={X: trX, Y: trY})))# And now for some fizz buzznumbers = np.arange(1, 101)teX = np.transpose(binary_encode(numbers, NUM_DIGITS))teY = sess.run(predict_op, feed_dict={X: teX})output = np.vectorize(fizz_buzz)(numbers, teY)print(output)

　　

轉載于:https://www.cnblogs.com/SSSR/p/5630497.html

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