add validator: need work

classifier.py

@@ -6,13 +6,13 @@
 
 import itertools
 import os
-
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 import matplotlib.pylab as plt
 import numpy as np
 
 import tensorflow as tf
 import tensorflow_hub as hub
-from tensorflow.keras import datasets, layers, models
+from tensorflow.keras import datasets, layers, models, preprocessing
 
 print("TF version:", tf.__version__)
 print("Hub version:", hub.__version__)
@@ -28,6 +28,7 @@ BATCH_SIZE=32
 datagen_kwargs = dict(rescale=1./255, validation_split=.20)
 dataflow_kwargs = dict(target_size=IMAGE_SIZE, batch_size=BATCH_SIZE,
                    interpolation="bilinear")
+
 train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
     **datagen_kwargs)
 
@@ -38,9 +39,7 @@ print(f"Data shape {train_generator[1][0].shape}")
 
 
 valid_generator = train_datagen.flow_from_directory(
-    "data/seg_test/seg_test", subset="validation", shuffle=False, **dataflow_kwargs)
-
-
+    "data/seg_train/seg_train", subset="validation", shuffle=False, **dataflow_kwargs)
 
 
 model = tf.keras.models.Sequential()
@@ -65,4 +64,14 @@ model.compile(optimizer=optmizer, loss=tf.keras.losses.MeanSquaredError(),
 
 history = model.fit(train_generator, batch_size=32,
                     epochs=30, validation_data=valid_generator)
+                    
+
+img = preprocessing.image.load_img("data/seg_test/buildings/20057.jpg")
+img_arr = preprocessing.image.img_to_array(img)
+img_arr = tf.expand_dims(img_arr, 0)
+true_labels = valid_generator.classeds
+predictions = model.predict(img_arr)
+
 
+y_pred = np.array([np.argmax(x) for x in predictions])
+print(y_pred)