how to resolve following error with CNN python Code?

Question

Image data description: 2D binary images with 200x200 size 123 labels (classes) are present, each class (label) contains 10 image frames, where the first 4 images I considered as test case remaining will be training dataset.

As per my knowledge, I change the CNN Code to classify the image data, but I am getting the following error:

WARNING:tensorflow:From C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.

Instructions for updating:

Colocations handled automatically by placer.

WARNING:tensorflow:From C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site-packages\keras\backend\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.

Instructions for updating:

Please use rate instead of keep_prob. Rate should be set to rate = 1 - keep_prob.

Traceback (most recent call last):

File "C:/Users/hp/PycharmProjects/FirstProject3/test.py", line 79, in model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

File "C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site-packages\keras\engine\training.py", line 952, in fit batch_size=batch_size)

File "C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site-packages\keras\engine\training.py", line 789, in _standardize_user_data exception_prefix='target')

File "C:\Users\hp\PycharmProjects\FirstProject3\venv\lib\site-packages\keras\engine\training_utils.py", line 138, in standardize_input_data str(data_shape))

ValueError: Error when checking target: expected dense_2 to have shape (123,) but got array with shape (124,)

How to resolve the error?

My Code:

    import keras
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Flatten
    from keras.layers import Conv2D, MaxPooling2D
    import numpy as np
    import cv2
    import os

    path1='C:\\Data\\For new Paper3\Old\\GaitDatasetB-silh_PerfectlyAlingedImages_EnergyImage\\';
    all_images = []
    all_labels = []
    subjects = os.listdir(path1)
    numberOfSubject = len(subjects)
    print('Number of Subjects: ', numberOfSubject)
    for number1 in range(0, numberOfSubject):  # numberOfSubject
        path2 = (path1 + subjects[number1] + '/')
        sequences = os.listdir(path2);
        numberOfsequences = len(sequences)
        for number2 in range(4, numberOfsequences):
            path3 = path2 + sequences[number2]
            img = cv2.imread(path3 , 0)
            img = img.reshape(200, 200, 1)
            all_images.append(img)
            all_labels.append(number1+1)
    x_train = np.array(all_images)
    y_train = np.array(all_labels)
    y_train = keras.utils.to_categorical(y_train)
    print(y_train)

    print(x_train)


    all_images = []
    all_labels = []

    for number1 in range(0, numberOfSubject):  # numberOfSubject
        path2 = (path1 + subjects[number1] + '/')
        sequences = os.listdir(path2);
        numberOfsequences = len(sequences)
        for number2 in range(0, 4):
            path3 = path2 + sequences[number2]
            img = cv2.imread(path3 , 0)
            img = img.reshape(200, 200, 1)
            all_images.append(img)
            all_labels.append(number1+1)
    x_test = np.array(all_images)
    y_test = np.array(all_labels)
    y_test = keras.utils.to_categorical(y_test)
    print(y_test)

    print(x_test)

    batch_size = 738
    num_classes = 123
    epochs = 12

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(200,200,1)))
    model.add(Conv2D(64, (5, 5), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(738, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])

    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

reference for Code: https://towardsdatascience.com/build-your-own-convolution-neural-network-in-5-mins-4217c2cf964f

Answer 1

Your data has 124 classes while you're assigning num_classes=123.

The warnings are due to you have the latest tensorflow version and keras hasn't been updated yet to fully support it.