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#!/usr/bin/env python
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import os
# This shows how to save/restore your model (trained variables).
# To see how it works, please stop this program during training and resart.
# This network is the same as
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden): # this network is the same as the previous one except with an extra hidden layer + dropout
X = tf.nn.dropout(X, p_keep_input)
h = tf.nn.relu(tf.matmul(X, w_h))
h = tf.nn.dropout(h, p_keep_hidden)
h2 = tf.nn.relu(tf.matmul(h, w_h2))
h2 = tf.nn.dropout(h2, p_keep_hidden)
return tf.matmul(h2, w_o)
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])
w_h = init_weights([784, 625])
w_h2 = init_weights([625, 625])
w_o = init_weights([625, 10])
p_keep_input = tf.placeholder("float")
p_keep_hidden = tf.placeholder("float")
py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(py_x, 1)
ckpt_dir = "./ckpt_dir"
if not os.path.exists(ckpt_dir):
global_step = tf.Variable(0, name='global_step', trainable=False)
# Call this after declaring all tf.Variables.
saver = tf.train.Saver()
# This variable won't be stored, since it is declared after tf.train.Saver()
non_storable_variable = tf.Variable(777)
# Launch the graph in a session
with tf.Session() as sess:
# you need to initialize all variables
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path) # restore all variables
start = global_step.eval() # get last global_step
print("Start from:", start)
for i in range(start, 100):
for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):, feed_dict={X: trX[start:end], Y: trY[start:end],
p_keep_input: 0.8, p_keep_hidden: 0.5})
global_step.assign(i).eval() # set and update(eval) global_step with index, i, ckpt_dir + "/model.ckpt", global_step=global_step)
print(i, np.mean(np.argmax(teY, axis=1) ==, feed_dict={X: teX,
p_keep_input: 1.0,
p_keep_hidden: 1.0})))
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