Keras

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (7,7) # Make the figures a bit bigger


from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.utils import np_utils

# ## Load Training Data
nb_classes = 10 # number of outputs = number of digits

# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("X_train original shape", X_train.shape)
print("y_train original shape", y_train.shape)


for i in range(9):
plt.subplot(3,3,i+1)
plt.imshow(X_train[i], cmap='gray', interpolation='none')
plt.title("Class {}".format(y_train[i]))

# ## Format the data for training

## reshape 28*28 = 784
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')


## normalize
X_train /= 255
X_test /= 255
print("Training matrix shape", X_train.shape)
print("Testing matrix shape", X_test.shape)


# In[6]:
## one-hot format - convert class vectors to binary class matrix
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

# ## Build the NN
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout(0.2))

model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))

model.add(Dense(10))
model.add(Activation('softmax'))
model.summary()

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

# ## Train the model
history = model.fit(X_train, Y_train,
batch_size=128,
epochs=4,
verbose=1,
validation_data=(X_test, Y_test))

# ## Evaluate the performance
score = model.evaluate(X_test, Y_test, verbose=1)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])

# ## Inspecting the output
predicted_classes = model.predict_classes(X_test)

# Check which items we got right / wrong
correct_indices = np.nonzero(predicted_classes == y_test)[0]
incorrect_indices = np.nonzero(predicted_classes != y_test)[0]

plt.figure()
for i, correct in enumerate(correct_indices[:9]):
plt.subplot(3,3,i+1)
plt.imshow(X_test[correct].reshape(28,28), cmap='gray', interpolation='none')
plt.title("Predicted {}, Class {}".format(predicted_classes[correct], y_test[correct]))
plt.show()

plt.figure()
for i, incorrect in enumerate(incorrect_indices[:9]):
plt.subplot(3,3,i+1)
plt.imshow(X_test[incorrect].reshape(28,28), cmap='gray', interpolation='none')
plt.title("Predicted {}, Class {}".format(predicted_classes[incorrect], y_test[incorrect]))

plt.show()

# ## List all data in history

print(history.history.keys())

# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()


https://github.com/wxs/keras-mnist-tutorial/blob/master/MNIST%20in%20Keras.ipynb