def test_mnist_28by28(self):
import time
import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_digits
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import LabelBinarizer
from ann import ANN
# load lecun mnist dataset
X = []
y = []
with open('data/mnist_test_data.txt', 'r') as fd, open('data/mnist_test_label.txt', 'r') as fl:
for line in fd:
img = line.split()
pixels = [int(pixel) for pixel in img]
X.append(pixels)
for line in fl:
pixel = int(line)
y.append(pixel)
X = np.array(X, np.float)
y = np.array(y, np.float)
# normalize input into [0, 1]
X -= X.min()
X /= X.max()
# quick test
#X = X[:1000]
#y = y[:1000]
# for my network
X_test = X
y_test = y #LabelBinarizer().fit_transform(y)
nn = ANN([1,1])
nn = nn.deserialize('28_200000.pickle') # '28_100000.pickle'
predictions = []
for i in range(X_test.shape[0]):
o = nn.predict(X_test[i])
predictions.append(np.argmax(o))
# compute a confusion matrix
print("confusion matrix")
print(confusion_matrix(y_test, predictions))
# show a classification report
print("classification report")
print(classification_report(y_test, predictions))
# free memory
X = None
y = None
def step_cb(nn, step):
print("ping")
nn.serialize(nn, str(step) + ".pickle")
# load or create an ANN
nn = ANN([1,1])
serialized_name = '28_1000000.pickle'
if os.path.exists(serialized_name):
# load a saved ANN
nn = nn.deserialize(serialized_name)
else:
# create the ANN with:
# 1 input layer of size 64 (the images are 8x8 gray pixels)
# 1 hidden layer of size 100
# 1 output layer of size 10 (the labels of digits are 0 to 9)
nn = ANN([784, 300, 10])
# see how long training takes
startTime = time.time()
# train it
nn.train2(30, X_train_l, labels_train_l, 100000, step_cb)
elapsedTime = time.time() - startTime
print("Training took {0} seconds".format(elapsedTime))
