How can i use 38 classes instead of 1000 in model.predict decode predictions - tensorflow

Question

i am founding an error in plant disease detection using resnet50 deep learning model every time it raises an error message in decode_predictions
error
expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 38)"
enter code here
model = ResNet50(weights='imagenet',include_top=False,classes=38)
try:
model = load_model('/content/drive/My
Drive/color/checkpoints/ResNet50_model_weights.h5')
print("model loaded")
except:
print("model not loaded")
img_path = '/content/drive/My Drive/color/test/0/appleblackrot188.jpg'
img = image.load_img(img_path, target_size=(300, 300))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds,top=3)[0])

Answer 1

You can try using the preprocesing function:
import tensorflow as tf
# Using the keras wrapper on tensorflow (it must be the same using just keras).
IMAGE = [] # From image source, i did it from the camera.
toPred = tf.keras.applications.resnet50.preprocess_input(np.expand_dims(tf.keras.preprocessing.image.img_to_array(IMAGE), axis=0))
Maybe that can help :)

Answer 2

decode_predictions works only for ImageNet (no. of classes = 1000). For these 38 classes of plants, you have to write your own decode predictions based on the ground truth label you've assigned for each plant.

Answer 3

First, you need an index JSON file and create a new decode_predictions function.
For example
This HAM10000 that has 7 classes and you need to split to each folder like this
then make an index JSON file like this
{
"0" : [
"AKIEC",
"akiec"
],
"1" : [
"BCC",
"bcc"
],
"2" : [
"BKL",
"bkl"
],
"3" : [
"DF",
"df"
],
"4" : [
"MEL",
"mel"
],
"5" : [
"NV",
"nv"
],
"6" : [
"VASC",
"vasc"
]
}
Then try this code
def decode_predictions(preds, top=4, class_list_path='/content/SKIN_DATA/HAM10000_index.json'):
if len(preds.shape) != 2 or preds.shape[1] != 7: # your classes number
raise ValueError('`decode_predictions` expects '
'a batch of predictions '
'(i.e. a 2D array of shape (samples, 1000)). '
'Found array with shape: ' + str(preds.shape))
index_list = json.load(open(class_list_path))
results = []
for pred in preds:
top_indices = pred.argsort()[-top:][::-1]
result = [tuple(index_list[str(i)]) + (pred[i],) for i in top_indices]
result.sort(key=lambda x: x[2], reverse=True)
results.append(result)
return results