def test():
input_data = tf.placeholder(tf.int32, [batchSize, maxSeqLength])
labels = tf.placeholder(tf.int32, [batchSize])
data = tf.nn.embedding_lookup(wordVectors, input_data)
data = tf.reshape(data, [batchSize, maxSeqLength, numDimensions, 1])
train_logits = CNN_model.inference1(data, batchSize, N_CLASSES)
train_loss = CNN_model.losses(train_logits, labels)
train_acc = CNN_model.evaluation(train_logits, labels)
all_accuracy = 0
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, './models/pretrained_CNN.ckpt-20000')
for i in range(2000):
train_batch, train_label_batch = input_text_data.get_allTest(i)
tra_loss, tra_acc = sess.run([train_loss, train_acc], {
input_data: train_batch,
labels: train_label_batch
})
all_accuracy = all_accuracy + tra_acc
print('All_accuracy:')
print(all_accuracy / 2000)
def training():
input_data = tf.placeholder(tf.int32, [batchSize, maxSeqLength])
labels = tf.placeholder(tf.int32, [batchSize])
data = tf.nn.embedding_lookup(wordVectors, input_data)
data = tf.reshape(data, [batchSize, maxSeqLength, numDimensions, 1])
train_logits = CNN_model.inference1(data, batchSize, N_CLASSES)
train_loss = CNN_model.losses(train_logits, labels)
train_op = CNN_model.trainning(train_loss, learning_rate)
train_acc = CNN_model.evaluation(train_logits, labels)
sess = tf.InteractiveSession()
tf.summary.scalar('Loss', train_loss)
tf.summary.scalar('Accuracy', train_acc)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
with tf.Session(config=config) as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for i in range(MAX_STEP):
train_batch, train_label_batch = input_text_data.getTrainBatch()
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc],
{
input_data: train_batch,
labels: train_label_batch
})
# 汇总到Tensorboard
if i % 1000 == 0:
print("Step %d, train loss = %.2f, train accuracy = %.2f%%" %
(i, tra_loss, tra_acc))
test_batch, test_label_batch = input_text_data.getTestBatch()
summary = sess.run(merged, {
input_data: test_batch,
labels: test_label_batch
})
writer.add_summary(summary, i)
test_loss, test_acc = sess.run([train_loss, train_acc], {
input_data: test_batch,
labels: test_label_batch
})
print(
"*************, test loss = %.2f, test accuracy = %.2f%%" %
(test_loss, test_acc))
if i % 2000 == 0 or (i + 1) == MAX_STEP:
save_path = saver.save(sess,
"./models/pretrained_CNN.ckpt",
global_step=i)
print("saved to %s" % save_path)
writer.close()