import numpy as np
import time
import keras.models as km
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Flatten, Dropout, Activation
from keras.utils import np_utils
import matplotlib.pyplot as plt

predictions = ['T-shirt/top', 'trouser', 'Pullover', 'Dress', 'Coat',
              'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

(X_train, y_train), (X_test, y_test) = mnist.load_data()
num_pixels = X_train.shape[1] * X_train.shape[2]
num_classes = 10

t1 = time.time()
X_train = X_train.reshape(60000, 784) / 255
X_test = X_test.reshape(10000, 784) / 255
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

# let's print the shape before we reshape and normalize
print("X_train shape", X_train.shape)
print("y_train shape", y_train.shape)
print("X_test shape", X_test.shape)
print("y_test shape", y_test.shape)

Y_train = np_utils.to_categorical(y_train, num_classes)
Y_test = np_utils.to_categorical(y_test, num_classes)
t2 = time.time()
print("Preprocessing took %.2f sec." % (t2 - t1))

print('Loading model and weights.')
json_file = open('mnist.json', 'r')
loaded_nnet = json_file.read()
json_file.close()

model = km.model_from_json(loaded_nnet)
model.load_weights('mnist.h5')

print("Training network ...")
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

print("Making predictions...")
t3 = time.time()
for i in range(9):
	img = np.array(X_test[i][np.newaxis,:])
	preds = model.predict_classes(img)
	print("Image[", i, "] - Me thinks me saw a : ", predictions[int(preds[0])] )
t4 = time.time()
print("Predictions took %.2f sec." % (t4 - t3))

print('Total running time %.2f sec' % (t4 - t1))