def run_training_with_my_data(step_num, keep_prob):
# file_dir = 'local_data/'
# image, label = get_files(file_dir)
# image_batches, label_batches = get_batches(image, label,
# resize_w=28, resize_h=28, batch_size=2, capacity=20)
input_new = ild.InputLocalData('local_data/')
image_batches, label_batches = input_new.get_batches(28, 28, 2, 20)
print(label_batches)
logits = cnn_model(image_batches, keep_prob=keep_prob, first_w="w_conv1")
loss = get_loss(logits, label_batches)
train_step = training(loss)
acc = get_accuracy(logits, label_batches)
init = tf.global_variables_initializer()
session.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=session, coord=coord)
try:
for step in np.arange(step_num):
print("run_training: step %d" % step)
if coord.should_stop():
break
_, train_acc, train_loss = session.run([train_step, acc, loss])
print("loss:{} , accuracy:{}".format(train_loss, train_acc))
except tf.errors.OutOfRangeError:
print("Done!!!")
finally:
coord.request_stop()
coord.join(threads)
"""
主函数
"""
import numpy as np
import tensorflow as tf
import input_local_data as ild
import training_graph as tg
import args_manager as am
session = tf.InteractiveSession()
# --------------------------------- build a graph -- start -------------------------------
args = am.ArgumentManager("model_save/cnn.ckpt", session)
input_data = ild.InputLocalData('local_data/')
img_batch, lab_batch = input_data.get_batches(resize_w=28, resize_h=28,
batch_size=5, capacity=20)
graph = tg.TrainingGraph(channels=3, keep_prob=1, classNum=10)
train_step, acc = graph.build_graph_with_batch(img_batch, lab_batch)
# --------------------------------- build a graph -- end -------------------------------
# init all variables
ys = input("attention!!!\n restore the variables? (y/n)\n")
if ys == 'y':
args.restore()
else:
init = tf.global_variables_initializer()
session.run(init)