本文整理匯總了Python中vgsl_input.ImageInput方法的典型用法代碼示例。如果您正苦於以下問題：Python vgsl_input.ImageInput方法的具體用法？Python vgsl_input.ImageInput怎麽用？Python vgsl_input.ImageInput使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類vgsl_input的用法示例。
在下文中一共展示了vgsl_input.ImageInput方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: Build
# 需要導入模塊: import vgsl_input [as 別名]
# 或者: from vgsl_input import ImageInput [as 別名]
def Build(self, input_pattern, input_spec, model_spec, output_spec,
optimizer_type, num_preprocess_threads, reader):
"""Builds the model from the separate input/layers/output spec strings.
Args:
input_pattern: File pattern of the data in tfrecords of TF Example format.
input_spec: Specification of the input layer:
batchsize,height,width,depth (4 comma-separated integers)
Training will run with batches of batchsize images, but runtime can
use any batch size.
height and/or width can be 0 or -1, indicating variable size,
otherwise all images must be the given size.
depth must be 1 or 3 to indicate greyscale or color.
NOTE 1-d image input, treating the y image dimension as depth, can
be achieved using S1(1x0)1,3 as the first op in the model_spec, but
the y-size of the input must then be fixed.
model_spec: Model definition. See vgslspecs.py
output_spec: Output layer definition:
O(2|1|0)(l|s|c)n output layer with n classes.
2 (heatmap) Output is a 2-d vector map of the input (possibly at
different scale).
1 (sequence) Output is a 1-d sequence of vector values.
0 (value) Output is a 0-d single vector value.
l uses a logistic non-linearity on the output, allowing multiple
hot elements in any output vector value.
s uses a softmax non-linearity, with one-hot output in each value.
c uses a softmax with CTC. Can only be used with s (sequence).
NOTE Only O1s and O1c are currently supported.
optimizer_type: One of 'GradientDescent', 'AdaGrad', 'Momentum', 'Adam'.
num_preprocess_threads: Number of threads to use for image processing.
reader: Function that returns an actual reader to read Examples from input
files. If None, uses tf.TFRecordReader().
"""
self.global_step = tf.Variable(0, name='global_step', trainable=False)
shape = _ParseInputSpec(input_spec)
out_dims, out_func, num_classes = _ParseOutputSpec(output_spec)
self.using_ctc = out_func == 'c'
images, heights, widths, labels, sparse, _ = vgsl_input.ImageInput(
input_pattern, num_preprocess_threads, shape, self.using_ctc, reader)
self.labels = labels
self.sparse_labels = sparse
self.layers = vgslspecs.VGSLSpecs(widths, heights, self.mode == 'train')
last_layer = self.layers.Build(images, model_spec)
self._AddOutputs(last_layer, out_dims, out_func, num_classes)
if self.mode == 'train':
self._AddOptimizer(optimizer_type)
# For saving the model across training and evaluation
self.saver = tf.train.Saver()