import tensorflow as tf
from mnist import input_data
from mnist import module
import os
data = input_data.read_data_sets('MNIST_data', one_hot=True)
# create model
with tf.variable_scope("regression"):
x = tf.placeholder(tf.float32, [None, 784])
y, variables = module.regression(x)
# train
y_ = tf.placeholder("float", [None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
learning_rate = 0.001
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(
cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# 断点续训
ckpt = tf.train.get_checkpoint_state(
os.path.join(os.path.dirname(__file__), 'data', 'regression.ckpt'))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
# # 0.9表示可以使用GPU 90%的资源进行训练，可以任意修改
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
config = tf.ConfigProto(allow_soft_placement=True)
# 最多占gpu资源的70%
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
# 开始不会给tensorflow全部gpu资源 而是按需增加
config.gpu_options.allow_growth = True
x = tf.placeholder("float", [None, 784])
sess = tf.Session(config=config)
with tf.variable_scope("regression"):
print(model.regression(x))
y1, variables = model.regression(x)
saver = tf.train.Saver(variables)
regression_file = tf.train.latest_checkpoint("mnist/data/regreesion.ckpt")
if regression_file is not None:
saver.restore(sess, regression_file)
with tf.variable_scope("convolutional"):
keep_prob = tf.placeholder("float")
y2, variables = model.convolutional(x, keep_prob)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(variables)
convolutional_file = tf.train.latest_checkpoint(
"mnist/data/convolutional.ckpt")
if convolutional_file is not None:
saver.restore(sess, convolutional_file)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
# # 0.9表示可以使用GPU 90%的资源进行训练，可以任意修改
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
config = tf.ConfigProto(allow_soft_placement=True)
# 最多占gpu资源的70%
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
# 开始不会给tensorflow全部gpu资源 而是按需增加
config.gpu_options.allow_growth = True
x = tf.placeholder("float", [None, 784])
sess = tf.Session(config=config)
with tf.variable_scope("regression"):
print(module.regression(x))
y1, variables = module.regression(x)
saver = tf.train.Saver(variables)
regression_file = tf.train.latest_checkpoint("mnist/data/regreesion.ckpt")
if regression_file is not None:
saver.restore(sess, regression_file)
with tf.variable_scope("convolutional"):
keep_prob = tf.placeholder("float")
y2, variables = module.convolutional(x, keep_prob)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(variables)
convolutional_file = tf.train.latest_checkpoint(
"mnist/data/convolutional.ckpt")
if convolutional_file is not None:
saver.restore(sess, convolutional_file)