xdata = []
ydata = []
for i in yLabels:
	for filename in os.listdir(labelDirectory + str(i)):
	    if filename.endswith(".png"):
	    	print str(i), filename
	    	trainingimage = cv2.imread(labelDirectory + str(i) + "//" + filename)
	    	trainingimage = trainingimage.flatten().astype(np.float32)
	    	trainingimage /= 255
	    	xdata.append(trainingimage)
	    	ydata.append(i)

xdata = np.asarray(xdata)
ydata = np.asarray(ydata)
X_train, X_test, y_train, y_test = train_test_split(xdata, ydata)
print "x train shape", X_train.shape[0]
clf = DBN([X_train.shape[0], 300, 10], learn_rates=0.3, learn_rate_decays=0.9, epochs=10, verbose=1)

clf.fit(X_train, y_train)

from sklearn.metrics import classification_report
from sklearn.metrics import zero_one_score

y_pred = clf.predict(X_test)
print "Accuracy:", zero_one_score(y_test, y_pred)
print "Classification report:"
print classification_report(y_test, y_pred)