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September 7, 2017 09:04
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Try out saving and restoring Tensorflow variables defined using tf.get_variable
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''' | |
Created on Aug 18, 2017 | |
@author: kashefy | |
''' | |
import os | |
import shutil | |
import tensorflow as tf | |
from tensorflow.examples.tutorials.mnist import input_data | |
import numpy as np | |
if __name__ == '__main__': | |
validate_only = True # Switch to True after first trainign run, write down the final values of the weights for comparison | |
if not validate_only: | |
if os.path.isdir('./a'): | |
shutil.rmtree('a') | |
os.makedirs('./a') | |
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) | |
name_w = 'W' | |
x = tf.placeholder(tf.float32, [None, 784]) | |
with tf.variable_scope("var_scope", reuse=None): | |
W = tf.get_variable(name_w, shape=[784, 10], | |
initializer=tf.random_normal_initializer(stddev=0.1)) | |
b = tf.get_variable('b', shape=[10], | |
initializer=tf.constant_initializer(0.1)) | |
logits = tf.matmul(x, W) + b | |
y = tf.nn.softmax(logits) | |
y_ = tf.placeholder(tf.float32, [None, 10]) | |
cross_entropy = tf.reduce_mean( | |
tf.nn.softmax_cross_entropy_with_logits(\ | |
labels=y_, logits=logits)) | |
train_step = tf.train.GradientDescentOptimizer(0.9).minimize(cross_entropy) | |
print [op.name for op in tf.get_default_graph().get_operations() if op.op_def and 'Variable' in op.op_def.name] | |
init_op = tf.global_variables_initializer() | |
with tf.Session() as sess: | |
sess.run(init_op) | |
if not validate_only: | |
saver = tf.train.Saver(max_to_keep=5) | |
else: | |
saver = tf.train.import_meta_graph('./a/x-999.meta') | |
saver.restore(sess, tf.train.latest_checkpoint('./a/')) | |
print [op.name for op in tf.get_default_graph().get_operations() if op.op_def and 'Variable' in op.op_def.name] | |
w0 = np.copy(sess.run(W)) | |
print(sess.run(W).flatten()[406:412]) | |
for itr in range(1000): | |
batch_xs, batch_ys = mnist.train.next_batch(128) | |
_, c = sess.run([train_step, cross_entropy], | |
feed_dict={x: batch_xs, y_: batch_ys}) | |
#print(itr, c) | |
print(sess.run(W).flatten()[406:412]) | |
print np.array_equal(w0, sess.run(W)) | |
if not validate_only: | |
saver.save(sess, './a/x', global_step=itr) | |
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
print(sess.run(accuracy, | |
feed_dict={x: mnist.test.images, y_: mnist.test.labels})) | |
print(sess.run(accuracy, | |
feed_dict={x: mnist.validation.images, y_: mnist.validation.labels})) | |
pass |
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