Created
August 13, 2019 22:28
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TensorFlow fizzbuzz
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import numpy as np | |
import tensorflow as tf | |
def binary_encode(i, num_digits): | |
return np.array([i >> d & 1 for d in range(num_digits)]) | |
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]) | |
NUM_DIGITS = 10 | |
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)]) | |
NUM_HIDDEN = 100 | |
X = tf.placeholder("float", [None, NUM_DIGITS]) | |
Y = tf.placeholder("float", [None, 4]) | |
def init_weights(shape): | |
return tf.Variable(tf.random_normal(shape, stddev=0.01)) | |
w_h = init_weights([NUM_DIGITS, NUM_HIDDEN]) | |
w_o = init_weights([NUM_HIDDEN, 4]) | |
def model(X, w_h, w_o): | |
h = tf.nn.relu(tf.matmul(X, w_h)) | |
return tf.matmul(h, w_o) | |
py_x = model(X, w_h, w_o) | |
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) | |
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) | |
predict_op = tf.argmax(py_x, 1) | |
def fizz_buzz(i, prediction): | |
return [str(i), "fizz", "buzz", "fizzbuzz"][prediction] | |
BATCH_SIZE = 128 | |
with tf.Session() as sess: | |
tf.initialize_all_variables().run() | |
for epoch in range(10000): | |
p = np.random.permutation(range(len(trX))) | |
trX, trY = trX[p], trY[p] | |
for start in range(0, len(trX), BATCH_SIZE): | |
end = start + BATCH_SIZE | |
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]}) | |
print(epoch, np.mean(np.argmax(trY, axis=1) == sess.run(predict_op, feed_dict={X: trX, Y: trY}))) | |
numbers = 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) |
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