MNIST Basic Example with Cloudmesh

mnist_v1.py

# replace the # with ! to run them in the Python Notebook
# pip install cloudmesh-installer
# pip install cloudmesh-common

import time 

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.utils import to_categorical, plot_model
from keras.datasets import mnist

from cloudmesh.common.StopWatch import StopWatch

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

StopWatch.start("data-load")
(x_train, y_train), (x_test, y_test) = mnist.load_data()
StopWatch.stop("data-load")

x1 = x_train[0]
y1 = y_train[0]
plt.imshow(x1)
y1

num_labels = len(np.unique(y_train))
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
image_size = x_train.shape[1]
input_size = image_size * image_size

x_train = np.reshape(x_train, [-1, input_size])
x_train = x_train.astype('float32') / 255
x_test = np.reshape(x_test, [-1, input_size])
x_test = x_test.astype('float32') / 255

batch_size = 4
hidden_units = 64

model = Sequential()
model.add(Dense(hidden_units, input_dim=input_size))
model.add(Dense(num_labels))
model.add(Activation('softmax'))
model.summary()
plot_model(model, to_file='mnist_v1.png', show_shapes=True)

StopWatch.start("compile")
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
StopWatch.stop("compile")
StopWatch.start("train")
model.fit(x_train, y_train, epochs=1, batch_size=batch_size)
StopWatch.stop("train")

StopWatch.start("test")
loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))
StopWatch.stop("test")

StopWatch.benchmark()