Remove num_channels variable, since it is now reduntant

cifar100_network_trainer.py

@@ -44,7 +44,7 @@ dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
 
 classifier_network = ClassifierNetworkGraph(input_x=data_inputs, target_placeholder=data_targets,
                                             dropout_rate=dropout_rate, batch_size=batch_size,
-                                            num_channels=train_data.inputs.shape[2], n_classes=train_data.num_classes,
+                                            n_classes=train_data.num_classes,
                                             is_training=training_phase, augment_rotate_flag=rotate_data,
                                             strided_dim_reduction=strided_dim_reduction,
                                             use_batch_normalization=batch_norm)  # initialize our computational graph

cifar10_network_trainer.py

@@ -44,8 +44,8 @@ dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
 
 classifier_network = ClassifierNetworkGraph(input_x=data_inputs, target_placeholder=data_targets,
                                             dropout_rate=dropout_rate, batch_size=batch_size,
-                                            num_channels=train_data.inputs.shape[2], n_classes=train_data.num_classes,
-                                            is_training=training_phase, augment_rotate_flag=rotate_data,
+                                            n_classes=train_data.num_classes, is_training=training_phase,
+                                            augment_rotate_flag=rotate_data,
                                             strided_dim_reduction=strided_dim_reduction,
                                             use_batch_normalization=batch_norm)  # initialize our computational graph
 

emnist_network_trainer.py

@@ -44,7 +44,7 @@ dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
 
 classifier_network = ClassifierNetworkGraph(input_x=data_inputs, target_placeholder=data_targets,
                                             dropout_rate=dropout_rate, batch_size=batch_size,
-                                            num_channels=train_data.inputs.shape[2], n_classes=train_data.num_classes,
+                                            n_classes=train_data.num_classes,
                                             is_training=training_phase, augment_rotate_flag=rotate_data,
                                             strided_dim_reduction=strided_dim_reduction,
                                             use_batch_normalization=batch_norm)  # initialize our computational graph

msd10_network_trainer.py

@@ -44,7 +44,7 @@ dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
 classifier_network = ClassifierNetworkGraph(network_name='FCCClassifier',
                                             input_x=data_inputs, target_placeholder=data_targets,
                                             dropout_rate=dropout_rate, batch_size=batch_size,
-                                            num_channels=1, n_classes=train_data.num_classes,
+                                            n_classes=train_data.num_classes,
                                             is_training=training_phase, augment_rotate_flag=rotate_data,
                                             strided_dim_reduction=strided_dim_reduction,
                                             use_batch_normalization=batch_norm)  # initialize our computational graph

msd25_network_trainer.py

@@ -44,7 +44,7 @@ dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
 classifier_network = ClassifierNetworkGraph(network_name='FCCClassifier',
                                             input_x=data_inputs, target_placeholder=data_targets,
                                             dropout_rate=dropout_rate, batch_size=batch_size,
-                                            num_channels=1, n_classes=train_data.num_classes,
+                                            n_classes=train_data.num_classes,
                                             is_training=training_phase, augment_rotate_flag=rotate_data,
                                             strided_dim_reduction=strided_dim_reduction,
                                             use_batch_normalization=batch_norm)  # initialize our computational graph

network_architectures.py

@@ -6,7 +6,7 @@ from utils.network_summary import count_parameters
 
 
 class VGGClassifier:
-    def __init__(self, batch_size, layer_stage_sizes, name, num_classes, num_channels=1, batch_norm_use=False,
+    def __init__(self, batch_size, layer_stage_sizes, name, num_classes, batch_norm_use=False,
                  inner_layer_depth=2, strided_dim_reduction=True):
 
         """
@@ -28,7 +28,6 @@ class VGGClassifier:
         """
         self.reuse = False
         self.batch_size = batch_size
-        self.num_channels = num_channels
         self.layer_stage_sizes = layer_stage_sizes
         self.name = name
         self.num_classes = num_classes
@@ -89,7 +88,7 @@ class VGGClassifier:
 
 
 class FCCLayerClassifier:
-    def __init__(self, batch_size, layer_stage_sizes, name, num_classes, num_channels=1, batch_norm_use=False,
+    def __init__(self, batch_size, layer_stage_sizes, name, num_classes, batch_norm_use=False,
                  inner_layer_depth=2, strided_dim_reduction=True):
 
         """
@@ -111,7 +110,6 @@ class FCCLayerClassifier:
         """
         self.reuse = False
         self.batch_size = batch_size
-        self.num_channels = num_channels
         self.layer_stage_sizes = layer_stage_sizes
         self.name = name
         self.num_classes = num_classes

network_builder.py

@@ -1,11 +1,10 @@
 import tensorflow as tf
-
 from network_architectures import VGGClassifier, FCCLayerClassifier
 
 
 class ClassifierNetworkGraph:
     def __init__(self, input_x, target_placeholder, dropout_rate,
-                 batch_size=100, num_channels=1, n_classes=100, is_training=True, augment_rotate_flag=True,
+                 batch_size=100, n_classes=100, is_training=True, augment_rotate_flag=True,
                  tensorboard_use=False, use_batch_normalization=False, strided_dim_reduction=True,
                  network_name='VGG_classifier'):
 
@@ -30,14 +29,12 @@ class ClassifierNetworkGraph:
         self.batch_size = batch_size
         if network_name == "VGG_classifier":
             self.c = VGGClassifier(self.batch_size, name="classifier_neural_network",
-                                   batch_norm_use=use_batch_normalization, num_channels=num_channels,
-                                   num_classes=n_classes, layer_stage_sizes=[64, 128, 256],
-                                   strided_dim_reduction=strided_dim_reduction)
+                                   batch_norm_use=use_batch_normalization, num_classes=n_classes,
+                                   layer_stage_sizes=[64, 128, 256], strided_dim_reduction=strided_dim_reduction)
         elif network_name == "FCCClassifier":
             self.c = FCCLayerClassifier(self.batch_size, name="classifier_neural_network",
-                                   batch_norm_use=use_batch_normalization, num_channels=num_channels,
-                                   num_classes=n_classes, layer_stage_sizes=[64, 128, 256],
-                                   strided_dim_reduction=strided_dim_reduction)
+                                   batch_norm_use=use_batch_normalization,  num_classes=n_classes,
+                                   layer_stage_sizes=[64, 128, 256], strided_dim_reduction=strided_dim_reduction)
 
         self.input_x = input_x
         self.dropout_rate = dropout_rate