# coding:utf-8

import datetime
import numpy as np

np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import RMSprop

start = datetime.datetime.now()

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
X_train = X_train.reshape((X_train.shape[0], -1)) / 255.  # normalize
X_test = X_test.reshape((X_test.shape[0], -1)) / 255.  # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# Another way to build your neural net
# model = Sequential([
#     Dense(500, input_dim=784),
#     Activation('relu'),
#     Dense(10),
#     Activation('softmax'),
# ])

model = Sequential([
    Dense(512, input_dim=784),
    Activation('relu'),
    Dense(512),
    Activation('relu'),
    Dense(10),
    Activation('softmax'),
])

# Another way to define your optimizer
# rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
rmsprop = RMSprop(lr=0.001)
model.summary()
# We add metrics to get more results you want to see
model.compile(optimizer=rmsprop,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=2, batch_size=32)

print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)

print('test loss: ', loss)
print('test accuracy: ', accuracy)

print("use time:", datetime.datetime.now() - start)

# coding:utf-8

import datetime
import numpy as np

np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import RMSprop

start = datetime.datetime.now()

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
X_train = X_train.reshape((X_train.shape[0], -1)) / 255.  # normalize
X_test = X_test.reshape((X_test.shape[0], -1)) / 255.  # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# Another way to build your neural net
# model = Sequential([
#     Dense(500, input_dim=784),
#     Activation('relu'),
#     Dense(10),
#     Activation('softmax'),
# ])

model = Sequential([
    Dense(512, input_dim=784),
    Activation('relu'),
    Dense(512),
    Activation('relu'),
    Dense(10),
    Activation('softmax'),
])

# Another way to define your optimizer
# rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
rmsprop = RMSprop(lr=0.001)
model.summary()
# We add metrics to get more results you want to see
model.compile(optimizer=rmsprop,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=2, batch_size=32)

print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)

print('test loss: ', loss)
print('test accuracy: ', accuracy)

print("use time:", datetime.datetime.now() - start)

# coding:utf-8 # for reproducibility # download the mnist to the path '~/.keras/datasets/' if it is the first time to be called # X shape (60,000 28x28), y shape (10,000, ) # data pre-processing # normalize # normalize # Another way to build your neural net # model = Sequential([ #     Dense(500, input_dim=784), #     Activation('relu'), #     Dense(10), #     Activation('softmax'), # ]) # Another way to define your optimizer # rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) # We add metrics to get more results you want to see # Another way to train the model # Evaluate the model with the metrics we defined earlier