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

# -*- 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)