from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout, BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from collections import Counter

Generator = ImageDataGenerator(rotation_range = 40, shear_range = 0.15, zoom_range = 0.4)

models = []
eva_list = []
for i in range(5):
    X_train, X_test, Y_train, Y_test = train_test_split(train_image, label_image, test_size = 0.3)

    Train_generator = Generator.flow(X_train, Y_train, batch_size = 128)
    model = Sequential()
    #Conv 2D layer here( not important)

    model.add(Dense(10, activation="softmax"))
    model.compile(optimizer='adam',loss='CategoricalCrossentropy', metrics=['accuracy'])

    model.fit(Train_generator, batch_size= 128, epochs= 50, verbose=2)
    eva_list.append(model.evaluate(X_test, Y_test, verbose=1))

test = test/ 255
test = test.to_numpy().reshape((-1, 28, 28, 1))

result_group = []
for i in range(len(models)):
#loop through the models to make prediction with each model, store the result in result group
    temp_result = models[i].predict(test)
    result_group.append(np.argmax(temp_result, axis = 1))

result = []
#loop through each test index, create a temporary list and find the most chosen number and use that as a final result
for i in range(len(result_group[0])):
    compare = []
    for z in range(len(result_group)):
    common = Counter(compare)

I have spent multiple days on the MNIST dataset. I have trained a rather deep CNN model and get a good 99.1 percent result. However, after reading some discussion posts I figured it may be a good idea to do bagging to increase the accuracy. My approach is basically to create a list to store those newly trained CNN models. And in the end, using a for loop to do prediction on each of the models. However I don't know where I made a mistake, now all my model output predict 1 for all test data. Anyone can tell me what's going on?

  • $\begingroup$ for i in range(len(result_group[0])): this line looks fishy to me can you and print statements and see what the variables are holding at different checkpoints $\endgroup$ – Madhur Yadav Sep 15 '20 at 13:26
  • 1
    $\begingroup$ Oh I should document my code better. That line isn’t the problem. I just loop through each prediction case index and find out the value that most model predict. Append them to the final result list. I try to print out temp_result and it already has the same soft max output for everything $\endgroup$ – Junhan Ouyang Sep 15 '20 at 14:12

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