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PCA in TensorFlow
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import numpy as np | |
import tensorflow as tf | |
# N, size of matrix. R, rank of data | |
N = 100 | |
R = 5 | |
# generate data | |
W_true = np.random.randn(N,R) | |
C_true = np.random.randn(R,N) | |
Y_true = np.dot(W_true, C_true) | |
Y_tf = tf.constant(Y_true.astype(np.float32)) | |
W = tf.Variable(np.random.randn(N,R).astype(np.float32)) | |
C = tf.Variable(np.random.randn(R,N).astype(np.float32)) | |
Y_est = tf.matmul(W,C) | |
loss = tf.reduce_sum((Y_tf-Y_est)**2) | |
# regularization | |
alpha = tf.constant(1e-4) | |
regW = alpha*tf.reduce_sum(W**2) | |
regC = alpha*tf.reduce_sum(C**2) | |
# full objective | |
objective = loss + regW + regC | |
# optimization setup | |
train_step = tf.train.AdamOptimizer(0.001).minimize(objective) | |
# fit the model | |
init_op = tf.initialize_all_variables() | |
with tf.Session() as sess: | |
sess.run(init_op) | |
for n in range(10000): | |
sess.run(train_step) | |
if (n+1) % 1000 == 0: | |
print('iter %i, %f' % (n+1, sess.run(objective))) |
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