本文整理汇总了Python中tensorflow_hub.get_num_image_channels方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow_hub.get_num_image_channels方法的具体用法？Python tensorflow_hub.get_num_image_channels怎么用？Python tensorflow_hub.get_num_image_channels使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow_hub的用法示例。
在下文中一共展示了tensorflow_hub.get_num_image_channels方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: add_jpeg_decoding
# 需要导入模块: import tensorflow_hub [as 别名]
# 或者: from tensorflow_hub import get_num_image_channels [as 别名]
def add_jpeg_decoding(module_spec):
"""Adds operations that perform JPEG decoding and resizing to the graph..
Args:
module_spec: The hub.ModuleSpec for the image module being used.
Returns:
Tensors for the node to feed JPEG data into, and the output of the
preprocessing steps.
"""
input_height, input_width = hub.get_expected_image_size(module_spec)
input_depth = hub.get_num_image_channels(module_spec)
jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput')
decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
# Convert from full range of uint8 to range [0,1] of float32.
decoded_image_as_float = tf.image.convert_image_dtype(decoded_image,
tf.float32)
decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
resize_shape = tf.stack([input_height, input_width])
resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32)
resized_image = tf.image.resize_bilinear(decoded_image_4d,
resize_shape_as_int)
return jpeg_data, resized_image 示例2: add_jpeg_decoding
# 需要导入模块: import tensorflow_hub [as 别名]
# 或者: from tensorflow_hub import get_num_image_channels [as 别名]
def add_jpeg_decoding(module_spec):
"""Adds operations that perform JPEG decoding and resizing to the graph..
Args:
module_spec: The hub.ModuleSpec for the image module being used.
Returns:
Tensors for the node to feed JPEG data into, and the output of the
preprocessing steps.
"""
input_height, input_width = hub.get_expected_image_size(module_spec)
input_depth = hub.get_num_image_channels(module_spec)
jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput')
decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
# Convert from full range of uint8 to range [0,1] of float32.
decoded_image_as_float = tf.image.convert_image_dtype(decoded_image,
tf.float32)
decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
resize_shape = tf.stack([input_height, input_width])
resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32)
resized_image = tf.image.resize_bilinear(decoded_image_4d,
resize_shape_as_int)
return jpeg_data, resized_image