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我試圖用tensorflow實施RBM RBM實施，這裏是代碼：與tensorflow
rbm.py
""" An rbm implementation for TensorFlow, based closely on the one in Theano """
import tensorflow as tf
import math
def sample_prob(probs):
return tf.nn.relu(
tf.sign(
probs - tf.random_uniform(probs.get_shape())))
class RBM(object):
def __init__(self, name, input_size, output_size):
with tf.name_scope("rbm_" + name):
self.weights = tf.Variable(
tf.truncated_normal([input_size, output_size],
stddev=1.0/math.sqrt(float(input_size))), name="weights")
self.v_bias = tf.Variable(tf.zeros([input_size]), name="v_bias")
self.h_bias = tf.Variable(tf.zeros([output_size]), name="h_bias")
def propup(self, visible):
return tf.nn.sigmoid(tf.matmul(visible, self.weights) + self.h_bias)
def propdown(self, hidden):
return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(self.weights)) + self.v_bias)
def sample_h_given_v(self, v_sample):
return sample_prob(self.propup(v_sample))
def sample_v_given_h(self, h_sample):
return sample_prob(self.propdown(h_sample))
def gibbs_hvh(self, h0_sample):
v_sample = self.sample_v_given_h(h0_sample)
h_sample = self.sample_h_given_v(v_sample)
return [v_sample, h_sample]
def gibbs_vhv(self, v0_sample):
h_sample = self.sample_h_given_v(v0_sample)
v_sample = self.sample_v_given_h(h_sample)
return [h_sample, v_sample]
def cd1(self, visibles, learning_rate=0.1):
h_start = self.propup(visibles)
v_end = self.propdown(h_start)
h_end = self.propup(v_end)
w_positive_grad = tf.matmul(tf.transpose(visibles), h_start)
w_negative_grad = tf.matmul(tf.transpose(v_end), h_end)
update_w = self.weights.assign_add(learning_rate * (w_positive_grad - w_negative_grad))
update_vb = self.v_bias.assign_add(learning_rate * tf.reduce_mean(visibles - v_end, 0))
update_hb = self.h_bias.assign_add(learning_rate * tf.reduce_mean(h_start - h_end, 0))
return [update_w, update_vb, update_hb]
def reconstruction_error(self, dataset):
err = tf.stop_gradient(dataset - self.gibbs_vhv(dataset)[1])
return tf.reduce_sum(err * err)
rbm_MNIST_test.py
import tensorflow as tf
import numpy as np
import rbm
import input_data
def build_model(X, w1, b1, wo, bo):
h1 = tf.nn.sigmoid(tf.matmul(X, w1)+b1)
model = tf.nn.sigmoid(tf.matmul(h1, wo)+bo)
return model
def init_weight(shape):
return tf.Variable(tf.random_normal(shape, mean=0.0, stddev=0.01))
def init_bias(dim):
return tf.Variable(tf.zeros([dim]))
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])
rbm_layer = rbm.RBM("mnist", 784, 500)
for i in range(10):
print "RBM CD: ", i
rbm_layer.cd1(trX)
rbm_w, rbm_vb, rbm_hb = rbm_layer.cd1(trX)
wo = init_weight([500,10])
bo = init_bias(10)
py_x = build_model(X, rbm_w, rbm_hb, wo, bo)
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)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
for i in range(10):
for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
print i, np.mean(np.argmax(teY, axis=1) ==
sess.run(predict_op, feed_dict={X: teX, Y: teY}))
，但來這裏的錯誤：
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1626, in as_graph_def raise ValueError("GraphDef cannot be larger than 2GB.") ValueError: GraphDef cannot be larger than 2GB.
有人能幫我解決這個問題？