adjusting to tf2

build_model_load_pretrained_weights_and_save.py

@@ -1,8 +1,8 @@
 import os
 import sys
 import tensorflow as tf
-import keras , warnings
+import warnings
-from keras.optimizers import *
+from tensorflow.keras.optimizers import *
 from sacred import Experiment
 from models import *
 from utils import *

metrics.py

@@ -1,4 +1,4 @@
-from keras import backend as K
+from tensorflow.keras import backend as K
 import tensorflow as tf
 import numpy as np
 

models.py

@@ -1,7 +1,7 @@
-from keras.models import *
+from tensorflow.keras.models import *
-from keras.layers import *
+from tensorflow.keras.layers import *
-from keras import layers
+from tensorflow.keras import layers
-from keras.regularizers import l2
+from tensorflow.keras.regularizers import l2
 
 resnet50_Weights_path='./pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
 IMAGE_ORDERING ='channels_last'

train.py

@@ -1,29 +1,21 @@
 import os
 import sys
 import tensorflow as tf
-from keras.backend.tensorflow_backend import set_session
+from tensorflow.compat.v1.keras.backend import set_session
-import keras , warnings
+import warnings
-from keras.optimizers import *
+from tensorflow.keras.optimizers import *
 from sacred import Experiment
 from models import *
 from utils import *
 from metrics import *
-from keras.models import load_model
+from tensorflow.keras.models import load_model
 from tqdm import tqdm
 
 def configuration():
-    keras.backend.clear_session()
+    config = tf.compat.v1.ConfigProto()
-    tf.reset_default_graph()
-    warnings.filterwarnings('ignore')
-    
-    os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
-    config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
-    
-    
     config.gpu_options.allow_growth = True
-    config.gpu_options.per_process_gpu_memory_fraction=0.95#0.95
+    session = tf.compat.v1.Session(config=config)
-    config.gpu_options.visible_device_list="0"
+    set_session(session)
-    set_session(tf.Session(config=config))
 
 def get_dirs_or_files(input_data):
     if os.path.isdir(input_data):
@@ -219,7 +211,7 @@ def run(n_classes,n_epochs,input_height,
             validation_data=val_gen,
             validation_steps=1,
             epochs=1)
-        model.save(dir_output+'/'+'model_'+str(i)+'.h5')
+        model.save(dir_output+'/'+'model_'+str(i))
     
 
     #os.system('rm -rf '+dir_train_flowing)