This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
  File "c:\Users\user\Documents\AI\ai_test2.py", line 84, in <module>
    predictions = model.predict(x_test[0], batch_size=None, verbose=3, steps=None)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\user~1\AppData\Local\Temp\__autograph_generated_filegijegug6.py", line 15, in tf__predict_function
    retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
    ^^^^^
ValueError: in user code:

    File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 2416, in predict_function  *
        return step_function(self, iterator)
    File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 2401, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 2389, in run_step  **
        outputs = model.predict_step(data)
    File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 2357, in predict_step
        return self(x, training=False)
    File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\input_spec.py", line 298, in assert_input_compatibility
        raise ValueError(

    ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 28, 28, 3), found shape=(None, 28, 3)

import keras
from keras.layers import Conv2D, MaxPooling2D
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Input
from keras.preprocessing.image import array_to_img, img_to_array, load_img
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import glob
import pickle
from PIL import Image
from keras.src.utils import np_utils

rps = ['rock/', 'paper/', 'scissors/']  #0, 1, 2

TRAIN_BASE_DIR = 'resize_data/train/'
TEST_BASE_DIR = 'resize_data/test/'

x_train = []
y_train = []
x_test, y_test = [], []



for idx, name in enumerate(rps):
    path = TRAIN_BASE_DIR + name + '*.jpg'
    files = glob.glob(path)
    for file in files:
        image = load_img(file)
        image = np.asarray(image)/255.0
        x_train.append(image)
        y_train.append(np.asarray(idx))

x_train, y_train = np.array(x_train), np.array(y_train)



for idx, name in enumerate(rps):
    path = TEST_BASE_DIR + name + '*.jpg'
    files = glob.glob(path)
    for file in files:
        image = load_img(file)
        image = np.asarray(image)/255.0
        x_test.append(image)
        y_test.append(np.asarray(idx))

x_test, y_test = np.array(x_test), np.array(y_test)



model = Sequential([
    Flatten(),
    Input(shape=(28, 28, 3)),
    Dropout(0.2),
    Dense(128, activation='relu'),
    Dense(3, activation='softmax')
])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

model.fit(x_train, y_train, epochs=100)
model.save('my_model.h5')

model.summary()

# model = keras.models.load_model('my_model.h5')
model.evaluate(x_test, y_test)

plt.imshow(x_test[0])
plt.show()
predictions = model.predict(x_test[0], batch_size=None, verbose=3, steps=None)
print(predictions)

>>> import glob
>>> import numpy as np
>>> from PIL import Image
>>> img = glob.glob('resize_data/test/*/*.jpg')
>>> img = Image.open(img[0])
>>> img = np.array(img)
>>> print(img.shape)
(28, 28, 3)