Properly implemented resnet50 and resnet101

The resnet50 and 101 models available on TF keras has bias terms after every convolutional layers. It shouldn't

https://github.com/tensorflow/tensorflow/issues/37365

This MR implement both properly

bob/learn/tensorflow/models/resnet50_modified.py

@@ -10,10 +10,9 @@ This resnet 50 implementation provides a cleaner version
 
 import tensorflow as tf
 
-from tensorflow.keras import layers
 from tensorflow.keras.regularizers import l2
-from tensorflow.keras.layers import Input, Conv2D, Activation, BatchNormalization
-from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D, Flatten, Dense
+from tensorflow.keras.layers import Conv2D, Activation, BatchNormalization
+from tensorflow.keras.layers import MaxPooling2D
 
 global weight_decay
 weight_decay = 1e-4
@@ -226,7 +225,7 @@ def resnet50_modified(input_tensor=None, input_shape=None, **kwargs):
     if input_tensor is None:
         input_tensor = tf.keras.Input(shape=input_shape)
     else:
-        if not K.is_keras_tensor(input_tensor):
+        if not tf.keras.backend.is_keras_tensor(input_tensor):
             input_tensor = tf.keras.Input(tensor=input_tensor, shape=input_shape)
 
     bn_axis = 3
@@ -345,7 +344,7 @@ def resnet101_modified(input_tensor=None, input_shape=None, **kwargs):
 
 if __name__ == "__main__":
     input_tensor = tf.keras.layers.InputLayer([112, 112, 3])
-    model = resnet_50(input_tensor)
+    model = resnet50_modified(input_tensor)
 
     print(len(model.variables))
     print(model.summary())