train_now.py

#train.py for CNN sequential model
from tensorflow.keras import layers
from tensorflow.keras import models
from keras.datasets import mnist
from keras.utils import to_categorical

##(X_train,Y_train),(X_test,Y_test)
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
##X_train = train_images
##X_test = test_images

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))

model.add(layers.Dense(10, activation='softmax'))
#[0-9] scaling to probabilities for prediction
#result to be interpreted as prob dist
model.summary()

# X_train.shape --> (60000,28,28)
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
#normalize values from 0 to 1
#CNN(Batch_Size,Height,Width,ColourScale) --> 1 for black and white 

test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
#truth labes not one hot encoded
# model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

test_loss, test_acc = model.evaluate(test_images, test_labels)

print(test_acc)

model.save('mnist.h5')