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github.com/alielabridi/Image-recognition-reCaptcha-TensorFlow
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Functions
20 in github.com/alielabridi/Image-recognition-reCaptcha-TensorFlow
⨍
Functions
20
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Types & classes
0
↓ 3 callers
Function
get_image_path
Returns a path to an image for a label at the given index. Args: image_lists: Dictionary of training images for each label. label_name: Lab
retrain.py:253
↓ 3 callers
Function
get_random_cached_bottlenecks
Retrieves bottleneck values for cached images. If no distortions are being applied, this function can retrieve the cached bottleneck values direc
retrain.py:504
↓ 2 callers
Function
ensure_dir_exists
Makes sure the folder exists on disk. Args: dir_name: Path string to the folder we want to create.
retrain.py:374
↓ 2 callers
Function
get_or_create_bottleneck
Retrieves or calculates bottleneck values for an image. If a cached version of the bottleneck data exists on-disk, return that, otherwise calcula
retrain.py:416
↓ 2 callers
Function
run_bottleneck_on_image
Runs inference on an image to extract the 'bottleneck' summary layer. Args: sess: Current active TensorFlow Session. image_data: String of
retrain.py:326
↓ 2 callers
Function
variable_summaries
Attach a lot of summaries to a Tensor (for TensorBoard visualization).
retrain.py:707
↓ 1 callers
Function
add_evaluation_step
Inserts the operations we need to evaluate the accuracy of our results. Args: result_tensor: The new final node that produces results. grou
retrain.py:781
↓ 1 callers
Function
add_final_training_ops
Adds a new softmax and fully-connected layer for training. We need to retrain the top layer to identify our new classes, so this function adds th
retrain.py:720
↓ 1 callers
Function
add_input_distortions
Creates the operations to apply the specified distortions. During training it can help to improve the results if we run the images through simple
retrain.py:618
↓ 1 callers
Function
cache_bottlenecks
Ensures all the training, testing, and validation bottlenecks are cached. Because we're likely to read the same image multiple times (if there are
retrain.py:467
↓ 1 callers
Function
create_image_lists
Builds a list of training images from the file system. Analyzes the sub folders in the image directory, splits them into stable training, testing
retrain.py:171
↓ 1 callers
Function
create_inception_graph
Creates a graph from saved GraphDef file and returns a Graph object. Returns: Graph holding the trained Inception network, and various tensors
retrain.py:306
↓ 1 callers
Function
get_bottleneck_path
Returns a path to a bottleneck file for a label at the given index. Args: image_lists: Dictionary of training images for each label. label_
retrain.py:286
↓ 1 callers
Function
get_random_distorted_bottlenecks
Retrieves bottleneck values for training images, after distortions. If we're training with distortions like crops, scales, or flips, we have to r
retrain.py:546
↓ 1 callers
Function
maybe_download_and_extract
Download and extract model tar file. If the pretrained model we're using doesn't already exist, this function downloads it from the TensorFlow.or
retrain.py:346
↓ 1 callers
Function
should_distort_images
Whether any distortions are enabled, from the input flags. Args: flip_left_right: Boolean whether to randomly mirror images horizontally. r
retrain.py:600
Function
_progress
(count, block_size, total_size)
retrain.py:359
Function
main
(_)
retrain.py:802
Function
read_list_of_floats_from_file
Reads list of floats from a given file. Args: file_path: Path to a file where list of floats was stored. Returns: Array of bottleneck val
retrain.py:398
Function
write_list_of_floats_to_file
Writes a given list of floats to a binary file. Args: list_of_floats: List of floats we want to write to a file. file_path: Path to a file
retrain.py:384