model=tf.keras.Sequential([
    tf.keras.Input(shape=(80,80,3)),
    tf.keras.layers.Conv2D(filters=32,kernel_size=(3,3),padding='same',activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2,2),strides=(2,2)),
    tf.keras.layers.Conv2D(filters=64,kernel_size=(3,3),padding='same',activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2,2),strides=(2,2)),
    tf.keras.layers.Conv2D(filters=32,kernel_size=(3,3),padding='same',activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2,2),strides=(2,2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(1000,activation='relu'),
    tf.keras.layers.Dropout(rate=0.5),
    tf.keras.layers.Dense(43,activation='softmax')
])
model.compile(
    loss='categorical_crossentropy',
    optimizer='adam',
    metrics=['accuracy']
)
train_history=model.fit(x_train,y_train_onehot,batch_size=100,epochs=8,validation_split=0.2,verbose=1,
          )