# ------------------------------------------------------------------------------------------
# ------------------------------ train model ------------------------------
mydata_train = DataSet(data_train, label_train)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
accuracys=[]
begin_time = datetime.now()
for i in range(train_iter):
# print('iter:', i)
batch = mydata_train.next_batch(128) # 此处的batch是由[128,1600]和[128,]组成的tuple，batch[0]就是tuple
labels = labels_transform(batch[1], classes_num)
if (i + 1) % 20 == 0:
train_accuracy = sess.run(accuracy, feed_dict={_X: batch[0], y: labels,
keep_prob: 1.0, batch_size: _batch_size})
accuracys.append(train_accuracy)
print("\nthe %dth loop,training accuracy:%f" % (i + 1, train_accuracy))
sess.run(train_op, feed_dict={_X: batch[0], y: labels, keep_prob: 0.5, batch_size: _batch_size})
# gc.collect()
end_time = datetime.now()
duarion = (end_time-begin_time).seconds
print('train time:',duarion)
file=open('accuracy-1DCNN12-1.txt','w')
string = str(accuracys)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
_batch_size = 128
mydata_train = DataSet(data_train, label_train)
statr = time.time()
accuracys = []
begin_time = datetime.now()
for i in range(train_iter):
batch = mydata_train.next_batch(_batch_size)
labels = labels_transform(batch[1], class_num)
if (i + 1) % 20 == 0:
train_accuracy = sess.run(accuracy,
feed_dict={
_X: batch[0],
y: labels,
keep_prob: 1.0,
batch_size: _batch_size
})
print("\nthe %dth loop,training accuracy:%f" % (i + 1, train_accuracy))
accuracys.append(train_accuracy)
sess.run(train_op,
feed_dict={
_X: batch[0],
##### errors_impl.InternalError: Failed to create session. 看看这里为什么报错
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
_batch_size = 128
mydata_train = DataSet(data_train, label_train)
accuracys = []
begin_time = datetime.now()
for i in range(train_iter):
batch = mydata_train.next_batch(_batch_size)
labels = labels_transform(batch[1], class_num)
if (i + 1) % 100 == 0:
train_accuracy = sess.run(accuracy,
feed_dict={
_X: batch[0],
y: labels,
keep_prob: 1.0,
batch_size: _batch_size
})
print("\nthe %dth loop,training accuracy:%f" % (i + 1, train_accuracy))
accuracys.append(train_accuracy)
sess.run(train_op,
feed_dict={
_X: batch[0],
y: labels,