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import tensorflow as tf
from tensorflow.keras import layers, models
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
import time
import random

# load data
train_data = pd.read_csv("/kaggle/input/digit-recognizer/train.csv")

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import tensorflow as tf
from tensorflow.keras import layers, models
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split

# Install Gpyopt
!pip install GPyOpt
import GPy, GPyOpt

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from keras.preprocessing.image import ImageDataGenerator

def image_data_generator():
    return ImageDataGenerator(rotation_range=30,
                              width_shift_range=0.20,
                              height_shift_range=0.20,
                              shear_range=0.2,
                              zoom_range=0.2,
                              fill_mode='nearest')

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from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau

model_checkpoint_callback = ModelCheckpoint(filepath=os.path.join("./weights/", "model-{epoch:02d}-{val_loss:.4f}.hdf5"),
                                            monitor='val_loss',
                                            mode='min',
                                            save_best_only=True)

reduce_lr_callback = ReduceLROnPlateau(monitor='val_loss',
                                       factor=0.47,
                                       patience=5,

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import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential()
model.add(layers.Conv2D(128, (7, 7), activation='relu', padding='same', input_shape=(28, 28, 1)))
model.add(layers.Dropout(0.4))

model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(256, (3, 3), activation='relu'))  
model.add(layers.Dropout(0.4))

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print("Hello, World!")