報錯
WARNING:tensorflow:From?D:/Python/workspace/Pybasis/Handwritten_digit_main.py:6:?The?name?tf.placeholder?is?deprecated.?Please?use?tf.compat.v1.placeholder?instead.WARNING:tensorflow:From?D:/Python/workspace/Pybasis/Handwritten_digit_main.py:7:?The?name?tf.Session?is?deprecated.?Please?use?tf.compat.v1.Session?instead.2019-10-11?13:26:14.887014:?I?tensorflow/core/platform/cpu_feature_guard.cc:145]?This?TensorFlow?binary?is?optimized?with?Intel(R)?MKL-DNN?to?use?the?following?CPU?instructions?in?performance?critical?operations:??AVX?AVX2To?enable?them?in?non-MKL-DNN?operations,?rebuild?TensorFlow?with?the?appropriate?compiler?flags.2019-10-11?13:26:14.889065:?I?tensorflow/core/common_runtime/process_util.cc:115]?Creating?new?thread?pool?with?default?inter?op?setting:?4.?Tune?using?inter_op_parallelism_threads?for?best?performance.WARNING:tensorflow:From?D:/Python/workspace/Pybasis/Handwritten_digit_main.py:9:?The?name?tf.variable_scope?is?deprecated.?Please?use?tf.compat.v1.variable_scope?instead.WARNING:tensorflow:From?D:/Python/workspace/Pybasis/Handwritten_digit_main.py:11:?The?name?tf.train.Saver?is?deprecated.?Please?use?tf.compat.v1.train.Saver?instead.WARNING:tensorflow:From?D:\Python\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py:1276:?checkpoint_exists?(from?tensorflow.python.training.checkpoint_management)?is?deprecated?and?will?be?removed?in?a?future?version.Instructions?for?updating:Use?standard?file?APIs?to?check?for?files?with?this?prefix.Process?finished?with?exit?code?-1073741819?(0xC0000005)

mian:


import?os
import?model
import?tensorflow?as?tf
import?input_data

data=input_data.read_data_sets('MINST_data',one_hot=True)

with?tf.variable_scope("convolutional"):
????x=tf.placeholder(tf.float32,[None,784],name="x")
????keep_prob=tf.placeholder(tf.float32)
????y,variables=model.convolutional(x,keep_prob)

y_=tf.placeholder(tf.float32,[None,10],name='y')
cross_entropy=-tf.reduce_sum(y_*tf.log(y))
train_step=tf.train.AdamOptimizer(1e-4).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(variables)

with?tf.Session()?as?sess:
????merged_summary_op=tf.summary.merge_all()
????summay_writer=tf.summary.FileWriter('/tmp/mnist_log/1',sess.graph)
????summay_writer.add_graph(sess.graph)
????sess.run(tf.global_variables_initializer())

????for?i?in?range(20000):
????????batch=data.train.next_batch(50)
????????if?i%100==0:
????????????train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
????????????print("step?%d,?training?accuracy?%g"%(i,train_accuracy))
????????sess.run(train_step,feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})

????print(sess.run(accuracy,feed_dict={x:data.test.images,y_:data.test.labels,keep_prob:1.0}))

????path=saver.save(
????????sess,os.path.join(os.path.dirname(__file__),'data','convolutional.ckpt'),
????????write_meta_graph=False,write_state=False)

????print("Saved:",path)