tf keras issues again

requirements.txt

@@ -1,4 +1,5 @@
 streamlit
 numpy
 tensorflow
+keras
 Pillow

src/nets/Loss.py

@@ -1,5 +1,5 @@
 import tensorflow as tf
-from tensorflow.keras import backend as K
+from keras import backend as K
 
 def multi_category_focal_loss2(gamma=2., alpha=1):
     """

src/nets/mobilenet.py

@@ -1,8 +1,8 @@
-from tensorflow.keras import backend as K
-from tensorflow.keras.layers import (Activation, BatchNormalization, Conv2D,
+from keras import backend as K
+from keras.layers import (Activation, BatchNormalization, Conv2D,
                           DepthwiseConv2D, Dropout, GlobalAveragePooling2D,
                           Input, Reshape)
-from tensorflow.keras.models import Model
+from keras.models import Model
 
 
 def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):

src/nets/resnet50.py

@@ -1,8 +1,8 @@
-from tensorflow.keras import layers
-from tensorflow.keras.layers import (Activation, AveragePooling2D, BatchNormalization,
+from keras import layers
+from keras.layers import (Activation, AveragePooling2D, BatchNormalization,
                           Conv2D, Dense, Flatten, Input, MaxPooling2D,
                           ZeroPadding2D)
-from tensorflow.keras.models import Model
+from keras.models import Model
 
 
 def identity_block(input_tensor, kernel_size, filters, stage, block):

src/nets/vgg16.py

@@ -1,5 +1,5 @@
-from tensorflow.keras.layers import Conv2D, Dense, Flatten, Input, MaxPooling2D
-from tensorflow.keras.models import Model        #导入包Conv2D是卷积核 Flatten是展开 Input输入  MaxPooling2D最大卷积核
+from keras.layers import Conv2D, Dense, Flatten, Input, MaxPooling2D
+from keras.models import Model        #导入包Conv2D是卷积核 Flatten是展开 Input输入  MaxPooling2D最大卷积核
 
 def VGG16(input_shape=None, classes=1000): #def 就是开始定义VGG16的网络
     img_input = Input(shape=input_shape)  # 224, 224, 3

src/utils/callbacks.py

@@ -5,9 +5,10 @@ matplotlib.use('Agg')
 from matplotlib import pyplot as plt
 import scipy.signal
 import tensorflow as tf
+import keras
 
 
-class LossHistory(tf.keras.callbacks.Callback):
+class LossHistory(keras.callbacks.Callback):
     def __init__(self, log_dir):
         import datetime
         curr_time = datetime.datetime.now()
@@ -59,7 +60,7 @@ class LossHistory(tf.keras.callbacks.Callback):
         plt.cla()
         plt.close("all")
 
-class ExponentDecayScheduler(tf.keras.callbacks.Callback):
+class ExponentDecayScheduler(keras.callbacks.Callback):
     def __init__(self,
                  decay_rate,
                  verbose=0):
@@ -71,13 +72,13 @@ class ExponentDecayScheduler(tf.keras.callbacks.Callback):
     def on_epoch_end(self, batch, logs=None):
         lr = self.model.optimizer.learning_rate
         try:
-            current_lr = tf.keras.backend.get_value(lr)
+            current_lr = keras.backend.get_value(lr)
         except Exception:
             current_lr = lr
             
         new_lr = current_lr * self.decay_rate
         try:
-            tf.keras.backend.set_value(lr, new_lr)
+            keras.backend.set_value(lr, new_lr)
         except Exception:
             print("Warning: Could not set learning rate dynamically.")
         

src/utils/dataloader.py

@@ -4,13 +4,13 @@ from random import shuffle
 import cv2
 import tensorflow as tf
 import numpy as np
-from tensorflow.keras.utils import to_categorical
+from keras.utils import to_categorical
 from PIL import Image
 
 from .utils import cvtColor, preprocess_input
 
 
-class ClsDatasets(tf.keras.utils.Sequence):
+class ClsDatasets(keras.utils.Sequence):
     def __init__(self, annotation_lines, input_shape, batch_size, num_classes, train, **kwargs):
         super().__init__()
         self.annotation_lines   = annotation_lines