[SOLVED] CS Keras Trains a simple convnet on the MNIST dataset.

Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.

from __future__ import print_function
import keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == channels_first:
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype(float32)
x_test = x_test.astype(float32)
x_train /= 255
x_test /= 255
print(x_train shape:, x_train.shape)
print(x_train.shape[0], train samples)
print(x_test.shape[0], test samples)

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation=relu, input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation=relu))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation=relu))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation=softmax))

model.compile(loss=categorical_crossentropy,
optimizer=adam,
metrics=[accuracy])

model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split=0.2)

score = model.evaluate(x_test, y_test, verbose=0)
print(Test loss:, score[0])
print(Test accuracy:, score[1])