CNN CODE WITH STEP

#1. IMPORT FILE
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, Dense, Dropout, MaxPooling2D, Flatten
from tensorflow.keras.preprocessing import image
import numpy as np

#2. IMPORT IMAGE DATASET
train_ds, val_ds = keras.utils.image_dataset_from_directory(
    "E:/Maroon/College/S9/DL/Fonts",
    color_mode="grayscale",
    image_size=(28, 28),
    validation_split=0.2,
    subset="both",
    seed=1337
)

#3. VARIABLE DECLARATION
def process(image,label):
    image=tf.cast(image/255,tf.float32)
    return image,label;

train_ds=train_ds.map(process)
val_ds=val_ds.map(process)

#OR WITHOUT FUNCTION (KARVU HOY TO KARJO)
#train_ds = train_ds.map(lambda x, y: (tf.cast(x, tf.float32) / 255.0, y))
#val_ds   = val_ds.map(lambda x, y: (tf.cast(x, tf.float32) / 255.0, y))

#4.MODEL CREATION (SUGGESTION AVSE)
model = Sequential([
    Conv2D(32, (5, 5), activation="relu", padding="same", input_shape=(28, 28, 1)), #input_shape nu suggestion ni avse
    MaxPooling2D(padding="same"),
    Conv2D(64, (5, 5), activation="relu", padding="same"),
    MaxPooling2D(padding="same"),
    Flatten(),
    Dense(1024, activation="relu"),
    Dropout(0.2),
    Dense(26, activation="sigmoid")  #CHANGE DENSE 26 ACCORDING TO THE NEED LIKE 10 FOR 0-9 NUMBERS
])

#OPTIONAL
#model.summary()

#5. MODEL COMPILE AND TRAINING (SUGGESTION NI AVSE)
model.compile(optimizer="adam",
              loss="sparse_categorical_crossentropy",
              metrics=["accuracy"])
model.fit(train_ds, epochs=20, validation_data=val_ds)

#6. IMAGE ARRAY
img = image.load_img("E:/Maroon/College/S9/DL/Fonts/test1.png",
                     target_size=(28, 28), color_mode="grayscale")
img_array = np.expand_dims(image.img_to_array(img) / 255.0, axis=0)

#6. MODEL PREDICTION
pred = model.predict(img_array)
pred_class = np.argmax(pred, axis=1)[0]

#7.VALUE GENERATION
class_labels = [chr(i) for i in range(65, 91)]  #Auto-generate A–Z
#class_labels=["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"]  Predefined data
#class_labels = [chr(i) for i in range(65, 91)]  Auto-generate 0-9
#class_labels = [str(i) for i in range(0, 101)]  Auto-generate 0 to 100

#8 PRINT
print("Predicted label:", class_labels[pred_class])