### 1.Creat data ###
#number 1 to 10 data
train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\dataset5\\IR_train7\\'
validate_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\dataset5\\IR_validate7\\'
test_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\dataset5\\IR_validate8\\'
logs_train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_logs\\IR_TrainLogs\\dataset5_DianziZangwu\\'
model_train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_Models\\dataset5_DianziZangwu\\'
if DELETE == True:
shutil.rmtree(logs_train_dir, ignore_errors=False, onerror=None)
shutil.rmtree(model_train_dir, ignore_errors=False, onerror=None)
else:
pass
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.get_batch(train,
train_label,
IMAGE_WIDTH,
IMAGE_HEIGHT,
IMAGE_CHANNEL,
BATCH_SIZE,
shuffle=True,
number_thread=1000,
capacity=CAPACITY)
validate, validate_label = input_data.get_files(validate_dir)
VALIDATE_BATCH_SIZE = len(validate)
print(VALIDATE_BATCH_SIZE)
CONV3_KENEL_NUM = 70
CONV3_KENEL_SIZE = 5
CONV4_KENEL_NUM = 2
CONV4_KENEL_SIZE = 3
### 1.Creat data ###
#number 1 to 10 data
train_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_database\\Test\\IR_test7\\'
logs_validation_dir = 'D:\\pythonworkspace\\TensorflowTraining\\exercises\\Shen\\Practice\\ACFdata\\IR_logs\\IR_TestLogs\\IR_TAN_4CONV_GAP_2BN_dropoff_3class\\'
if DELETE == True:
shutil.rmtree(logs_validation_dir, ignore_errors=False, onerror=None)
else:
pass
test, test_label = input_data.get_files(train_dir)
test_batch, test_label_batch = input_data.get_batch(test, test_label,
IMAGE_WIDTH, IMAGE_HEIGHT,
IMAGE_CHANNEL, BATCH_SIZE,
CAPACITY)
trainphase = tf.placeholder(tf.bool, name='trainphase')
### 2.Define placeholder for inputs to network ###
### 3. Setup Network ###
# conv1 layer ##
BN0 = layer.batch_norm_layer(test_batch, trainphase, 'BN0')