main.py

import numpy as np
import matplotlib.pyplot as plt

import keras

from keras.datasets import mnist

from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train.shape, y_train.shape, X_test.shape, y_test.shape


def plot_input_img(i):
    plt.imshow(X_train[i], cmap='binary')
    plt.title(y_train[i])
    plt.axis('off')
    plt.show()

# Normalizing the image to [0, 1] range
X_train = X_train.astype(np.float32) / 255
X_test = X_test.astype(np.float32) / 255

# REshape / expand the dimensions of images to (28,28,1)
X_train = np.expand_dims(X_train, -1)
X_test = np.expand_dims(X_test, -1)

# convert classes to one hot vectors
y_train = keras.utils.to_categorical(y_train)

y_test = keras.utils.to_categorical(y_test)

model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(MaxPool2D((2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))

model.add(Flatten())
model.add(Dropout(0.25))

model.add(Dense(10, activation="softmax"))

# Compile the model
model.compile(loss=keras.losses.categorical_crossentropy, optimizer="adam", metrics=["accuracy"])

# Print the model summary
model.summary()

#callbacks
from keras.callbacks import EarlyStopping, ModelCheckpoint

#Early stopping

es = EarlyStopping(monitor='val_accuracy', min_delta=0.01, patience=4, verbose=1)

#Model Check point

mc= ModelCheckpoint("./bestmodel.h5", monitor="val_accuracy", verbose= 1, save_best_only=True)

cb = [es,mc]

# Train the model

his = model.fit(X_train, y_train, epochs=50, validation_split=0.3, callbacks=cb)

model_S = keras.models.load_model("C://Users//Puneet//Desktop//Puneet//Technofest//bestmodel.h5")
score = model_S.evaluate(X_test, y_test)

print(f" the model accuracy is {score[1]}")