Revert "Typo on #153"

DeepPATH_code/00_preprocessing/0d_SortTiles.py

@@ -251,13 +251,13 @@ def sort_subfolders(metadata, load_dic, **kwargs):
     parser.add_argument("--SourceFolder", help="path to tiled images", dest='SourceFolder')
     parser.add_argument("--JsonFile", help="path to metadata json file", dest='JsonFile')
     parser.add_argument("--Magnification", help="magnification to use", type=float, dest='Magnification')
-    parser.add_argument("--MagDiffAllowed", help="difference allwed on Magnification", type=float, dest='MagDiffAllowed')
+    parser.add_argument("--MagDiffAllowed", help="difference allowed on Magnification", type=float, dest='MagDiffAllowed')
     parser.add_argument("--SortingOption", help="see option at the epilog", type=int, dest='SortingOption')
     parser.add_argument("--PercentValid", help="percentage of images for validation (between 0 and 100)", type=float, dest='PercentValid')
     parser.add_argument("--PercentTest", help="percentage of images for testing (between 0 and 100)", type=float, dest='PercentTest')
     parser.add_argument("--PatientID", help="Patient ID is supposed to be the first PatientID characters (integer expected) of the folder in which the pyramidal jpgs are. Slides from same patient will be in same train/test/valid set. This option is ignored if set to 0 or -1 ", type=int, dest='PatientID')
     parser.add_argument("--TMB", help="path to json file with mutational loads; or to BRAF mutations", dest='TMB')
-    parser.add_argument("--nSplit", help="interger n: Split into train/test in n different ways", dest='nSplit')
+    parser.add_argument("--nSplit", help="integer n: Split into train/test in n different ways", dest='nSplit')
     parser.add_argument("--outFilenameStats", help="Check if the tile exists in an out_filename_Stats.txt file and copy it only if it True, or is the expLabel option had the highest probability", dest='outFilenameStats')
     parser.add_argument("--expLabel", help="Index of the expected label within the outFilenameStats file (if only True/False is needed, leave this option empty).", dest='expLabel')
     parser.add_argument("--threshold", help="threshold above which the probability the class should be to be considered as true (if not specified, it would be considered as true if it has the max probability).", dest='threshold')

DeepPATH_code/01_training/2Classes/inception/slim/README.md

@@ -9,8 +9,7 @@ keeping a model's architecture transparent and its hyperparameters explicit.
 ## Teaser
 
 As a demonstration of the simplicity of using TF-Slim, compare the simplicity of
-the code necessary for defining the entire [VGG]
-(http://www.robots.ox.ac.uk/~vgg/research/very_deep/) network using TF-Slim to
+the code necessary for defining the entire [VGG](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) network using TF-Slim to
 the lengthy and verbose nature of defining just the first three layers (out of
 16) using native tensorflow:
 
@@ -244,12 +243,9 @@ number. More concretely, the scopes in the example above would be 'conv3_1',
 
 ### Scopes
 
-In addition to the types of scope mechanisms in TensorFlow ([name_scope]
-(https://www.tensorflow.org/api_docs/python/framework.html#name_scope),
-[variable_scope]
-(https://www.tensorflow.org/api_docs/python/state_ops.html#variable_scope),
-TF-Slim adds a new scoping mechanism called "argument scope" or [arg_scope]
-(scopes.py). This new scope allows a user to specify one or more operations and
+In addition to the types of scope mechanisms in TensorFlow ([name_scope](https://www.tensorflow.org/api_docs/python/framework.html#name_scope),
+[variable_scope](https://www.tensorflow.org/api_docs/python/state_ops.html#variable_scope),
+TF-Slim adds a new scoping mechanism called "argument scope" or [arg_scope](./scopes.py). This new scope allows a user to specify one or more operations and
 a set of arguments which will be passed to each of the operations defined in the
 `arg_scope`. This functionality is best illustrated by example. Consider the
 following code snippet:
@@ -264,7 +260,7 @@ It should be clear that these three Convolution layers share many of the same
 hyperparameters. Two have the same padding, all three have the same weight_decay
 and standard deviation of its weights. Not only do the duplicated values make
 the code more difficult to read, it also adds the addition burder to the writer
-of needing to doublecheck that all of the values are identical in each step. One
+of needing to double-check that all of the values are identical in each step. One
 solution would be to specify default values using variables:
 
 ```python
@@ -362,7 +358,7 @@ classes. For regression problems, this is often the sum-of-squares differences
 between the predicted and true values.
 
 Certain models, such as multi-task learning models, require the use of multiple
-loss functions simultaneously. In other words, the loss function ultimatey being
+loss functions simultaneously. In other words, the loss function ultimately being
 minimized is the sum of various other loss functions. For example, consider a
 model that predicts both the type of scene in an image as well as the depth from
 the camera of each pixel. This model's loss function would be the sum of the
@@ -494,12 +490,9 @@ with tf.Session() as sess:
   ...
 ```
 
-See [Restoring Variables]
-(https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#restoring-variables)
-and [Choosing which Variables to Save and Restore]
-(https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#choosing-which-variables-to-save-and-restore)
-sections of the [Variables]
-(https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html) page for
+See [Restoring Variables](https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#restoring-variables)
+and [Choosing which Variables to Save and Restore](https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#choosing-which-variables-to-save-and-restore)
+sections of the [Variables](https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html) page for
 more details.
 
 ### Using slim.variables to Track which Variables need to be Restored

DeepPATH_code/01_training/3Classes/inception/slim/README.md

@@ -9,8 +9,7 @@ keeping a model's architecture transparent and its hyperparameters explicit.
 ## Teaser
 
 As a demonstration of the simplicity of using TF-Slim, compare the simplicity of
-the code necessary for defining the entire [VGG]
-(http://www.robots.ox.ac.uk/~vgg/research/very_deep/) network using TF-Slim to
+the code necessary for defining the entire [VGG](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) network using TF-Slim to
 the lengthy and verbose nature of defining just the first three layers (out of
 16) using native tensorflow:
 
@@ -244,12 +243,9 @@ number. More concretely, the scopes in the example above would be 'conv3_1',
 
 ### Scopes
 
-In addition to the types of scope mechanisms in TensorFlow ([name_scope]
-(https://www.tensorflow.org/api_docs/python/framework.html#name_scope),
-[variable_scope]
-(https://www.tensorflow.org/api_docs/python/state_ops.html#variable_scope),
-TF-Slim adds a new scoping mechanism called "argument scope" or [arg_scope]
-(scopes.py). This new scope allows a user to specify one or more operations and
+In addition to the types of scope mechanisms in TensorFlow ([name_scope](https://www.tensorflow.org/api_docs/python/framework.html#name_scope),
+[variable_scope](https://www.tensorflow.org/api_docs/python/state_ops.html#variable_scope),
+TF-Slim adds a new scoping mechanism called "argument scope" or [arg_scope](scopes.py). This new scope allows a user to specify one or more operations and
 a set of arguments which will be passed to each of the operations defined in the
 `arg_scope`. This functionality is best illustrated by example. Consider the
 following code snippet:
@@ -264,7 +260,7 @@ It should be clear that these three Convolution layers share many of the same
 hyperparameters. Two have the same padding, all three have the same weight_decay
 and standard deviation of its weights. Not only do the duplicated values make
 the code more difficult to read, it also adds the addition burder to the writer
-of needing to doublecheck that all of the values are identical in each step. One
+of needing to double-check that all of the values are identical in each step. One
 solution would be to specify default values using variables:
 
 ```python
@@ -362,7 +358,7 @@ classes. For regression problems, this is often the sum-of-squares differences
 between the predicted and true values.
 
 Certain models, such as multi-task learning models, require the use of multiple
-loss functions simultaneously. In other words, the loss function ultimatey being
+loss functions simultaneously. In other words, the loss function ultimately being
 minimized is the sum of various other loss functions. For example, consider a
 model that predicts both the type of scene in an image as well as the depth from
 the camera of each pixel. This model's loss function would be the sum of the
@@ -439,8 +435,7 @@ let TF-Slim know about the additional loss and let TF-Slim handle the losses.
 ## Putting the Pieces Together
 
 By combining TF-Slim Variables, Operations and scopes, we can write a normally
-very complex network with very few lines of code. For example, the entire [VGG]
-(https://www.robots.ox.ac.uk/~vgg/research/very_deep/) architecture can be
+very complex network with very few lines of code. For example, the entire [VGG](https://www.robots.ox.ac.uk/~vgg/research/very_deep/) architecture can be
 defined with just the following snippet:
 
 ```python
@@ -494,12 +489,9 @@ with tf.Session() as sess:
   ...
 ```
 
-See [Restoring Variables]
-(https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#restoring-variables)
-and [Choosing which Variables to Save and Restore]
-(https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#choosing-which-variables-to-save-and-restore)
-sections of the [Variables]
-(https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html) page for
+See [Restoring Variables](https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#restoring-variables)
+and [Choosing which Variables to Save and Restore](https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html#choosing-which-variables-to-save-and-restore)
+sections of the [Variables](https://www.tensorflow.org/versions/r0.7/how_tos/variables/index.html) page for
 more details.
 
 ### Using slim.variables to Track which Variables need to be Restored

DeepPATH_code/03_postprocessing/0h_ROC_MultiOutput.py

@@ -388,7 +388,7 @@ def main():
 
 
 	# save data
-	print("******* FP / TP for average probabilitys")
+	print("******* FP / TP for average probabilities")
 	print(fpr)
 	print(tpr)
 	for i in range(n_classes):
@@ -514,7 +514,7 @@ def main():
 
 
 	# save data
-	print("******* FP / TP for average probabilitys")
+	print("******* FP / TP for average probabilities")
 	print(fpr)
 	print(tpr)
 	for i in range(n_classes):