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我想製作tensorflow的初始版本v3，爲圖像分配標記。我的目標是將JPEG圖像轉換爲初始神經網絡接受的輸入。我不知道如何首先處理這些圖像，以便它可以使用Google Inception的v3模型運行。原來tensorflow項目是在這裏： https://github.com/tensorflow/models/tree/master/inception圖像處理：如何處理圖像以用於初始化
原來，所有的圖片都在數據集和整個數據集首先在ImageProcessing.py傳遞給輸入（）或distorted_inputs（）。數據集中的圖像被處理並傳遞給train（）或eval（）方法（這兩種方法都有效）。問題是我想要一個函數來打印出一個特定圖像（不是數據集）的標籤。
以下是推理函數的代碼，用於在谷歌開始時生成標籤。 inceptionv4函數是一個在tensorflow中實現的卷積神經網絡。
def inference(images, num_classes, for_training=False, restore_logits=True,
scope=None):
"""Build Inception v3 model architecture.
See here for reference: http://arxiv.org/abs/1512.00567
Args:
images: Images returned from inputs() or distorted_inputs().
num_classes: number of classes
for_training: If set to `True`, build the inference model for training.
Kernels that operate differently for inference during training
e.g. dropout, are appropriately configured.
restore_logits: whether or not the logits layers should be restored.
Useful for fine-tuning a model with different num_classes.
scope: optional prefix string identifying the ImageNet tower.
Returns:
Logits. 2-D float Tensor.
Auxiliary Logits. 2-D float Tensor of side-head. Used for training only.
"""
# Parameters for BatchNorm.
batch_norm_params = {
# Decay for the moving averages.
'decay': BATCHNORM_MOVING_AVERAGE_DECAY,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
}
# Set weight_decay for weights in Conv and FC layers.
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
with slim.arg_scope([slim.ops.conv2d],
stddev=0.1,
activation=tf.nn.relu,
batch_norm_params=batch_norm_params):
logits, endpoints = inception_v4(
images,
dropout_keep_prob=0.8,
num_classes=num_classes,
is_training=for_training,
scope=scope)
# Add summaries for viewing model statistics on TensorBoard.
_activation_summaries(endpoints)
# Grab the logits associated with the side head. Employed during training.
auxiliary_logits = endpoints['AuxLogits']
return logits, auxiliary_logits
這是我嘗試在傳遞給推理函數之前處理圖像。
def process_image(self, image_path):
filename_queue = tf.train.string_input_producer(image_path)
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
img = tf.image.decode_jpeg(value)
height = self.image_size
width = self.image_size
image_data = tf.cast(img, tf.float32)
image_data = tf.reshape(image_data, shape=[1, height, width, 3])
return image_data
我想簡單地處理一個圖像文件，以便我可以將它傳遞給推理函數。這個推斷會打印出標籤。上面的代碼沒有工作，印刷錯誤：
ValueError: Shape() must have rank at least 1
我很感激，如果任何人都可以提供任何洞察到這一問題。